Executive information systemFrom Wikipedia, the free encyclopedia

An executive information system (EIS) is a type of management information system that facilitates and supports senior executive information and decision-making needs. It provides easy access to internal and external information relevant to organizational goals. It is commonly considered a specialized form of decision support system (DSS).[1]

EIS emphasizes graphical displays and easy-to-use user interfaces. They offer strong reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems. EIS and data warehousing technologies are converging in the marketplace.

In recent years, the term EIS has lost popularity in favor of business intelligence (with the sub areas of reporting, analytics, and digital dashboards).

Contents  [hide] 

1 History

2 Components

o 2.1 Hardware

o 2.2 Software

o 2.3 User interface

o 2.4 Telecommunication

3 Applications

o 3.1 Manufacturing

o 3.2 Marketing

o 3.3 Financial

4 Advantages and disadvantages

o 4.1 Advantages of EIS

o 4.2 Disadvantages of EIS

5 Future trends

6 See also

7 References

8 External links

History[edit]

Traditionally, executive information systems were mainframe computer-based programs. The purpose was to package a company’s data and to provide sales performance or market research statistics for decision makers, such as financial officers, marketing directors, and chief executive

officers, who were not necessarily well acquainted with computers. The objective was to develop computer applications that highlighted information to satisfy senior executives’ needs. Typically, an EIS provides only data that supported executive level decisions, not all company data.

Today, the application of EIS is not only in typical corporate hierarchies, but also at personal computers on a local area network. EIS now cross computer hardware platforms and integrate information stored on mainframes, personal computer systems, and minicomputers. As some client service companies adopt the latest enterprise information systems, employees can use their personal computers to get access to the company’s data and identify information relevant to their decision making. This arrangement lets all users customize their access to company data, and provides relevant information to upper and lower corporate levels.

Components[edit]

EIS components can typically be classified as:

Hardware

Software

User interface

Telecommunications

Hardware[edit]When talking about computer hardware for an EIS environment, we should focus on the hardware that meet the executive’s needs. The executive must be put first and the executive’s needs must be defined before the hardware can be selected. The basic hardware needed for a typical EIS includes four components:

1. Input data-entry devices. These devices allow the executive to enter, verify, and update data

immediately

2. The central processing unit (CPU), which is the kernel because it controls the other

computer system components

3. Data storage files. The executive can use this part to save useful business information, and

this part also help the executive to search historical business information easily

4. Output devices, which provide a visual or permanent record for the executive to save or

read. This device refers to the visual output device such as monitor or printer

In addition, with the advent of local area networks (LAN), several EIS products for networked workstations became available. These systems require less support and less expensive computer hardware. They also increase EIS information access to more company users.

Software[edit]Choosing the appropriate software is vital to an effective EIS.[citation needed] Therefore, the software components and how they integrate the data into one system are important. A typical EIS includes four software components:

1. Text-handling software—documents are typically text-based

2. Database—heterogeneous databases on a range of vendor-specific and open computer

platforms help executives access both internal and external data

3. Graphic base—graphics can turn volumes of text and statistics into visual information for

executives. Typical graphic types are: time series charts, scatter diagrams, maps, motion

graphics, sequence charts, and comparison-oriented graphs (i.e., bar charts)

4. Model base—EIS models contain routine and special statistical, financial, and other

quantitative analysis

User interface[edit]An EIS must be efficient to retrieve relevant data for decision makers, so the user interface is very important. Several types of interfaces can be available to the EIS structure, such as scheduled reports, questions/answers, menu driven, command language, natural language, and input/output.

Telecommunication[edit]As decentralizing is becoming the current trend in companies, telecommunications will play a pivotal role in networked information systems. Transmitting data from one place to another has become crucial for establishing a reliable network. In addition, telecommunications within an EIS can accelerate the need for access to distributed data.

Applications[edit]

EIS helps executives find data according to user-defined criteria and promote information-based insight and understanding. Unlike a traditional management information systempresentation, EIS can distinguish between vital and seldom-used data, and track different key critical activities for executives, both which are helpful in evaluating if the company is meeting its corporate objectives. After realizing its advantages, people have applied EIS in many areas, especially, in manufacturing, marketing, and finance areas.

Manufacturing[edit]Manufacturing is the transformation of raw materials into finished goods for sale, or intermediate processes involving the production or finishing of semi-manufactures. It is a large branch of industry and of secondary production. Manufacturing operational control focuses on day-to-day operations, and the central idea of this process is effectiveness and efficiency.

Marketing[edit]In an organization, marketing executives’ duty is managing available marketing resources to create a more effective future. For this, they need make judgments about risk and uncertainty of a project and its impact on the company in short term and long term. To assist marketing executives in making effective marketing decisions, an EIS can be applied. EIS provides sales forecasting, which can allow the market executive to compare sales forecast with past sales. EIS also offers an approach to product price, which is found in venture analysis. The market executive can evaluate pricing as related to competition along with the relationship of product quality with price charged. In summary, EIS software package enables marketing executives to manipulate the data by looking for trends, performing audits of the sales data, and calculating totals, averages, changes, variances, or ratios.

Financial[edit]Financial analysis is one of the most important steps to companies today. Executives needs to use financial ratios and cash flow analysis to estimate the trends and make capital investment decisions. An EIS integrates planning or budgeting with control of performance reporting, and it can be

extremely helpful to finance executives. EIS focuses on financial performance accountability, and recognizes the importance of cost standards and flexible budgeting in developing the quality of information provided for all executive levels.

Advantages and disadvantages[edit]

Advantages of EIS[edit]

Easy for upper-level executives to use, extensive computer experience is not required in

operations

Provides timely delivery of company summary information

Information that is provided is better understood

EIS provides timely delivery of information. Management can make decisions promptly.

Improves tracking information

Offers efficiency to decision makers

Disadvantages of EIS[edit]

System dependent

Limited functionality, by design

Information overload for some managers

Benefits hard to quantify

High implementation costs

System may become slow, large, and hard to manage

Need good internal processes for data management

May lead to less reliable and less secure data

Future trends[edit]

The future of executive info systems is not bound by mainframe computer systems. This trend free executives from learning different computer operating systems, and substantially decreases implementation costs. Because this trend includes using existing software applications, executives don't need to learn a new or special language for the EIS package.

Dss

Benefits[edit]

1. Improves personal efficiency

2. Speed up the process of decision making

3. Increases organizational control

4. Encourages exploration and discovery on the part of the decision maker

5. Speeds up problem solving in an organization

6. Facilitates interpersonal communication

7. Promotes learning or training

8. Generates new evidence in support of a decision

9. Creates a competitive advantage over competition

10.Reveals new approaches to thinking about the problem space

11.Helps automate managerial processes

12.Create Innovative ideas to speed up the performance

DSS characteristics and capabilities[edit]

1. Solve semi-structured and unstructured problems

2. Support managers at all levels

3. Support individuals and groups

4. Interdependence and sequence of decisions

5. Support Intelligence, Design, Choice

6. Adaptable and flexible

7. Interactive and ease of use

8. Interactive and efficiency

9. Human control of the process

10.Ease of development by end user

11.Modeling and analysis

12.Data access

13.Standalone and web-based integration

14.Support varieties of decision processes

15.Support varieties of decision trees

16.Quick response

q. siri

SiriFrom Wikipedia, the free encyclopedia

This article is about the intelligent personal assistant developed by Apple Inc. For other uses, see Siri (disambiguation).

Siri

Siri on the iPhone 5S, running iOS 7.

Original author(s) SRI International

Developer(s) Apple Inc.

Initial release October 4, 2011; 2 years ago

Operating system iOS 5  and later (iPhone)

iOS 6  and later (iPad andiPod touch)

Platform iPhone (4S and later)

iPad (third generation and later)

iPad Mini  (all generations)

iPod Touch (fifth generationand later)

Available in English

French

German

Japanese

Chinese (Cantonese, Mandarin)

Korean

Italian

Spanish [1]

Type intelligent software assistant

License Proprietary

Website www.apple.com/ios/siri/

Siri / ̍ s ɪr i /  is an intelligent personal assistant and knowledge navigator which works as an application for Apple Inc.'s iOS. The application uses a natural language user interface to answer questions, make recommendations, and perform actions by delegating requests to a set of Web services. Apple claims that the software adapts to the user's individual preferences over time and personalizes results.[2] The name Siri is Norwegian, meaning "beautiful woman who leads you to victory", and comes from the intended name for the original developer's first child.[3]

Siri was originally introduced as an iOS application available in the App Store by Siri, Inc., which was acquired by Apple on April 28, 2010.[4] Siri, Inc. had announced that their software would be available for BlackBerry and for phones running Android, but all development efforts for non-Apple platforms were cancelled after the acquisition by Apple.[5]

Siri has been an integral part of iOS since iOS 5 [6]  and was introduced as a feature of the iPhone 4S in October 14, 2011.[7] Siri was added to the third generation iPad with the release of iOS 6 in September 2012, and has been included on all iOS devices released during or after October 2012. [8]

[9]

Contents

  [hide] 

1 iPhone application

2 iOS integration

3 Research and development

o 3.1 Voice actors

4 Vehicle integration

5 Reception

6 Supported languages

7 International versions

8 Geographic limitations

9 See also

10 References

11 External links

iPhone application[edit]

Siri was launched first as an application available on Apple's App Store in the United States. It integrated with services such asOpenTable,[10] Google Maps,[11] MovieTickets and TaxiMagic.[12] Using voice recognition technology from Nuance and their service partners, users could make reservations at specific restaurants, buy movie tickets or get a cab by dictating instructions in natural language to Siri.[13] Siri was acquired by Apple on April 28, 2010, and the original application ceased to function on October 14, 2011.[4][14]

iOS integration[edit]

The Siri feature shown on a white iPhone 4S.

On October 4, 2011 (Siri's birthday),[15] Apple introduced the iPhone 4S with their implementation of a beta version of Siri.[16] The new version of Siri is integrated into iOS, and offers conversational interaction with many applications, including reminders, weather, stocks, messaging, email,

calendar, contacts, notes, music, clocks, web browser, Wolfram Alpha, and maps.[2] Currently, Siri supports English (American, Canadian, Australian, British), French, German, Japanese, Italian (Italy, Switzerland), Spanish (Mexico, Spain), Mandarin (China, Taiwan), Korean, and Cantonese. On launch, Siri had limited functionality outside the United States and Canada. However, Apple, with the release of iOS 6, added the missing functionality to other countries.[17][18]

After announcing that Siri is included with the iPhone 4S, Apple removed the existing Siri app (which ran on all iPhone models) from the App Store.[19]

In October 2011, independent developers stated that they had ported Siri into the other iOS devices.[20][21] However, some news sites suggest that the videos posted by the developers as "proof" only show the user interface of the Siri software, and not the voice commands, implying that developers have not been able to port the application with full functionality.[22] However, new reports from January 2012 suggest that independent developers have succeeded in porting Siri to earlier iPhone models, the iPod Touch, and iPad. i4Siri.com, a United States based team, have demonstrated Siri working as intended on the iPhone 4, iPod Touch, and iPad, communicating without the Apple servers.[23]

In later January 2012, independent developers successfully created and distributed a legal port of Siri to older devices via Cydia.[24] The port, however, requires authorization keys from another iPhone 4S, which can be exploited in the form of a proxy server, or by transferring the Siri authorization file from an iPhone 4S.[25] Due to this requirement, developers have bypassed Apple's Siri server completely by creating their own backend using APIs from services such as Google and Wolfram Alpha.[26]

On June 11, 2012, at Apple's WWDC conference, Apple announced that Siri will be available on the iPad (third generation) beginning in late 2012 with the release of iOS 6. Also on June 11, 2012, at Apple's WWDC conference, Apple announced updates for Siri coming in iOS 6 (which launched in fall 2012.) These new features include: opening apps, telling sports scores and other sports related information, checking movie times, finding restaurants and also ordering reservations. Siri can also tell the height of sports players in iOS 6. It also brought some previously US only features, such as Google Maps and Yelp integration, international.

On September 12, 2012, Apple announced that Siri will also be on the iPhone 5 [27]  and the iPod Touch (fifth generation).[28]

Siri is also included on the iPad (fourth generation) [29]  and iPad Mini.[30]

On June 13, 2013, Apple revealed that Siri will have a gender option, meaning that you can choose if Siri will sound like a boy or a girl, with the release of iOS 7.[31]

Currently, Siri is included on the iPhone 4S, iPhone 5, iPhone 5C, iPhone 5S, 5th generation iPod Touch, 3rd generation iPad, 4th generation iPad, iPad Air, and all iPad Minis.

Research and development[edit]

Siri is a spin-out from the SRI International Artificial Intelligence Center, and is an offshoot of the DARPA-funded CALO project.[32][33] Siri was co-founded by SRI's Dag Kittlaus (CEO) and Adam Cheyer (VP Engineering) and by Tom Gruber (CTO).[34]

Siri's primary technical areas focus on a Conversational Interface, Personal Context Awareness, and Service Delegation.[35]

Siri's speech recognition engine is provided by Nuance Communications, a speech technology company, although this was not officially acknowledged by either Apple or Nuance until AllThingsD Conference (2013).[36]

The original Siri application relied upon a number of partners, including:

OpenTable , Gayot, CitySearch, BooRah, Yelp, Yahoo

Local, Yandex, ReserveTravel, Localeze for restaurant and business questions and actions;

Eventful , StubHub, and LiveKick for events and concert information;

MovieTickets , Rotten Tomatoes, and the New York Times for movie information and reviews;

Bing Answers , Wolfram Alpha and Evi for factual question answering;[37]

Bing , Yahoo, and Google for web search (Bing as default since iOS 7[38]).

The sources in Apple's implementation of Siri differ from the original iPhone application. It integrates with default iOS functionality, such as contacts, calendars and text messages. It also supports search from Google, Bing, Yahoo, Wolfram Alpha, Google Maps, Yelp! and Wikipedia.

Siri also contains numerous pre-programmed responses to conversational and amusing questions. These are designed to provide both an entertainment factor and give Siri human-like qualities. [39]

Voice actors[edit]

The original American voice was provided by Susan Bennett in July 2005.[40] Reports that the voice was provided by Alison Dufty were incorrect.[41]

The British male voice is called "Daniel" and is voiced by Jon Briggs, a former technology journalist. The voice was recorded for Scansoft, which had merged with Nuance Communications in October 2005, although Apple has never confirmed any involvement of Nuance with Siri.[42]

The Australian female voice is called "Karen" and is voiced by Karen Jacobsen, an Australian-born and New York-based entertainer, singer, voiceover artist, and songwriter.[43]Jacobsen is also the Australian voice in GPS navigation devices for Garmin, Mio, Navman, and TomTom.[44]

Vehicle integration[edit]

Main article: CarPlay

In June 2012, former Apple SVP Scott Forstall announced that Apple had been in discussions with automobile manufacturers and companies to get Siri integration as part of a scheme called "Siri Eyes Free" mode to provided eyes and hands-free operation, stating that Siri could be in vehicles in as soon as 11 months.

The day following the announcement of this unprecedented collaboration between Apple and automobile manufacturers, Harman International Industries's stock immediately fell by 15%, given Harman's substantial revenue sources from providing GPS, Navigation, and Telematics systems for vehicles, many in particular manufactured by companies partnering with Apple.

At WWDC 2013, Apple's Eddy Cue announced a new system called "iOS in the Car" aimed at integrating Siri and other iOS functions more fully into native in-car systems, likesatellite navigation (Satnav) and music playback, which was later renamed CarPlay by Apple on March 3, 2014.

Reception[edit]

Siri was met with critical acclaim for its ease of use and practicality, as well as its apparent "personality". However, issues did arise when Siri was used by consumers from areas with distinct accents. Google's executive chairman and former chief, Eric Schmidt, has conceded that Siri could pose a "competitive threat" to the company's core search business.[45]

Writing in The Guardian, journalist Charlie Brooker considered Siri's personality to be unpleasantly servile, but found that the software worked "annoyingly well".[46] Siri was criticized by organizations such as the American Civil Liberties Union (ACLU) and NARAL Pro-Choice America after users found that it would not provide information about the location of birth control or abortion providers, sometimes directing users to pro-life crisis pregnancy centers instead. Apple responded that this was a glitch which would be fixed in the final version.[47]

Siri has not been well received by some English speakers with distinctive accents, including Scottish [48]  and Americans from Boston or the South.[49][50] Apple's Siri FAQ states that, "as more people use Siri and it's exposed to more variations of a language, its overall recognition of dialects and accents will continue to improve, and Siri will work even better."[1]

Despite many functions still requiring the use of the touchscreen, the National Federation of the Blind describes the iPhone as "the only fully accessible handset that a blind person can buy".[51]

In March 2012, Frank M. Fazio filed a class action lawsuit against Apple on behalf of the people who felt misled about the capabilities of Siri and failing to function as depicted in Apple's Siri commercials. Fazio filed the lawsuit in California and claimed that the iPhone 4S is merely a "more expensive iPhone" if Siri fails to function as advertised.[52][53] On July 22, 2013 U.S. District Judge Claudia Wilken in San Francisco dismissed the suit but said the plaintiffs could amend at a later

time. The reason given for dismissal was that plaintiffs did not sufficiently document enough misrepresentations by Apple for the trial to proceed.[54]

In March 2012, the American Civil Liberties Union expressed concern that Siri was sending a large amount of personal voice and user information to Apple, including the first name and nickname of the phone owner and his or her contacts, the owner's relationship with those contacts, personal labels assigned to email accounts, and the names of songs and playlists stored on the phone. [55]

Siri and Apple maps together were viewed as "flops" by the media in April 2012.[56][57][58][59] In October 2012, Scott Forstall, Apple's head of mobile software, and the leader held responsible for Siri and the poorly received Apple Maps, was let go from Apple.[57][58][60]

On October 30, 2012, Google released a new Google Search app for iOS, which featured an enhanced Google Voice Search function and aimed to compete with Siri.[61] Google's Voice Search was compared favorably to Siri, with some reviewers preferring it.[62] The Unofficial Apple Weblog's side-by-side comparison said that Google's Voice Search on iOS is "amazingly quick and relevant, and has more depth [than Siri]".[63]

Supported languages[edit]

Language

Region iOS version[64]

English

 United States 5.0 onwards

 United Kingdom 5.0 onwards

 Australia 5.0 onwards

 Canada 6.0 onwards

French  France 5.0 onwards

 Canada 6.0 onwards

Language

Region iOS version[64]

 Switzerland 6.0 onwards

German

 Germany 5.0 onwards

 Switzerland 6.0 onwards

Japanese  Japan 5.1 onwards

Spanish

 Spain 6.0 onwards

 Mexico 6.0 onwards

 United States 6.0 onwards

Italian

 Italy 6.0 onwards

 Switzerland 6.0 onwards

Korean  South Korea 6.0 onwards

Mandarin

 China 6.0 onwards

 Taiwan 6.0 onwards

Language

Region iOS version[64]

Cantonese  Hong Kong 6.0 onwards

International versions[edit]

According to sources from Brazilian site Techguru, Nuance Communications has delivered the final version in Portuguese to Apple. It also announced that the company would be making a deal with the bank Bradesco to provide an application similar to Siri for voice support.[65]

Geographic limitations[edit]

In Siri's original release its functionality was limited in most countries, with maps and local search with help only being available within the United States. For example, asking Siri in the United Kingdom to list local businesses, to navigate somewhere, or to give traffic information, elicits the reply "I can only look for businesses, maps and traffic in the United States, and when you're using U.S. English. Sorry about that." Using Siri within the United States with the British English voice (Daniel) elicits a similar response — despite the user's geographic location.[66] However, as of iOS 6, Siri now has functionality to find local businesses and other location services outside of the United States.

See also[edit]

Q, virtual office

Virtual officeFrom Wikipedia, the free encyclopedia

This article has been nominated to be checked for its neutrality. Discussion of this nomination can be found on the talk page. (July 2013)

A virtual office provides communication and address services that allow users to reduce traditional office costs while maintaining business professionalism.[1] Frequently the term is confused with "office business centers" or "executive suites" which demand a conventional lease whereas a true virtual office does not require that expense.[2]

Contents  [hide] 

1 History

2 Services

3 Users

4 Economy

5 References

History[edit]

The virtual office idea came[3] from a combination of technological innovation and the Information Age. The concept has roots in the Industrial Revolution, where parallels to current work styles, specifically working from home, have been drawn.[4] The virtual office concept is an evolution of the executive suite industry. However, the inflexibility of an executive suite lease doesn't work for many business models and helped spur the virtual office concept.[5] The first commercial application of a virtual office occurred in 1994, when Ralph Gregory founded "The Virtual Office, Inc",[6] in Boulder, Colorado.

Virtual Office Setup

If your work involves traveling to client locations or other places away from your home base, you should probably consider buying a laptop computer rather than a desktop system. With a laptop you will always have your files with you and won't have any of those embarrassing moments where you left an important document at your office, because...well, your office is with you. While a laptop may seem a bit cumbersome to always travel with, there are many lightweight models out there that are very powerful. Just make sure you get a good carrying case that has a shoulder strap and room for your hard copy documents.

If always working from the keyboard and small screen of a laptop doesn't appeal to you, there are other solutions. Yes, they've thought of everything! To make using your laptop more efficient in your home office, a docking station can be set up that you can simply plug your laptop into. Docking stations make it

easy to have a standard monitor, keyboard and mouse, printer, fax machine, scanner, and other peripherals always hooked and ready to use. By plugging your laptop into the docking station, you are able to use it just like a standard desktop system, and you won't have to worry about transferring or synching files to another computer.

If you do not travel, or if you do not need access to all of your files while you travel, you can just get the traditional desktop computer system. Make sure you have plenty of hard drive space, memory for running several programs at once, and a moderately fast processor. If you're doing graphics work (anything involving photo images, illustrations or animations) you'll need a much faster processor and as much hard drive space and RAM as you can afford.

Other equipment and hardware options you might consider include:

A black and white 600-1200 dpi laser printer if your final documents require crisp, high quality black and white output. Laser printers also provide the fastest output, so if you know your volume will be high you should also consider a laser printer.

A color laser printer if your documents need high quality color illustrations, photos, or charts. These are quite expensive so make sure you compare the print quality with a less expensive ink jet printer.

An inkjet printer if you need good quality text, color charts and graphs, or photos. With ink jet printers, the paper that is used often makes the biggest difference in the print quality. Get paper that is best suited for the job you are doing. Also, try to get a test print from different models to compare quality before you buy. Ink jets can provide very good quality but are not as fast printing as laser printers.

A fax machine if you will need to fax paper documents often. There is also the option of online faxing services such as E-Fax.

A scanner if you will need to scan documents or photos. You can also use a scanner along with e-mail or fax software in place of a regular fax machine.

A CD burner (CD-RW) if you need to provide clients with large files electronically, or if you want to back up your files on CD. There are many business uses for a CD writer, not to mention the ability to make your own music CDs.

A DVD writer (DVD-RAM) if you need to provide clients with extremely large files, such as video, electronically.

A removable media storage device. Iomega™ offers the most common drive of this type, called the Zip™ drive, but there are many others like it. Data is written to the disk just like it would be to a floppy diskette. The difference is the amount of data that can be written. Currently, there are 100 Mb and 250 Mb disks available for the ZIP drive. Iomega also manufactures Jaz™ drives that use disks that can hold up to 2 Gb of data.

A modem for accessing the Internet, faxing electronically, and e-mail. This can be either a standardmodem that you use with your existing phone lines for dial up access, a DSL modem that also uses your phone line but does not tie up your line, or a cable modem that uses the same cable your cable television is hooked up to. DSL and cable modems are for broadband Internet access and require special connections.

A digital camera if your work requires photos for presentations, reports, a web site, or other documents. While you can also use a regular camera and scanner to get digital photos for documents, you may find the immediate access you get with a digital camera more efficient than waiting for film to be processed and printed. The quality of the digital image is still somewhat better with actually photos that are scanned, but for most business applications digital cameras produce sharp enough images. Images for use in marketing materials may need to be of higher quality.

A multi-purpose scanner, fax machine, copier, printer if your space is limited and quality not as critical. Keep in mind with this type of equipment, however, if one part of it stops working you'll be without the other functions until it can be repaired!

For obvious reasons, mainly because equipment in the technology world changes more often than some people change underwear, we'll not go into the technical specifications for the computer equipment you'll need in your office.Virtual Office Software

On the software side of things, there are several options for you to consider. If you work independently and do not have others that you need to coordinate with then fulfilling your software requirements is not so difficult. Here are some categories of software you may need along with links to some of the most popular packages:

an e-mail program -- You can use the e-mail program that your ISP provides, but programs such asMicrosoft Outlook Express or Eudora will give you good e-mail functions and you won't have to change programs if you change ISPs.

a word processor -- Microsoft Word, WordPerfect, and Lotus Word Pro

spreadsheet and database programs -- Microsoft Excel, Lotus 1-2-3, Intuit QuickBase

presentation software -- Microsoft PowerPoint, or Astound.

virus protection software -- McAfee, or Norton AntiVirus

a utility program for computer maintenance -- Symantec Norton Utilities, McAfee, or TechTool Pro

Portable Document Format (PDF) reader software - Adobe Acrobat Reader -- This software is very helpful for reviewing formatted documents such as brochure layouts from outside designers or co-workers.

graphics and/or image editing tool -- CorelDraw, Adobe Illustrator, Adobe Photoshop, Macromedia Freehand, Deneba Canvas (Image editing software may also accompany your scanner.)

Internet browsers -- Netscape Navigator or Microsoft Internet Explorer

Many business applications come packaged in "suites" that provide all of the above product categories and then some. Some of the more popular packages include:

Microsoft Office , as well as the less expensive Microsoft Works

Corel

Lotus

AppleWorks

Many programs also have less expensive "light" or "limited" versions that may work for smaller businesses. They are also usually available for both Windows and Macintosh computer platforms.

Group software needsIf you are working with others, and have the need to coordinate scheduling, access central files, maintain a contact manager, meet in chat rooms, etc. then you have more of a challenge. There are programs available, such as Lotus Notes  or Novell Groupwise, that provide these types of features as a software solution. These solutions may require quite a good bit of computer knowledge and an IT person to manage the system.

As an alternative, there are also online management services that provide these types of services on the Internet for access with your browser. These are fairly simple to use. They offer many features to promote coordination of information between members of a team, client interactions, or simply communication and file-sharing with co-workers. They typically charge a small monthly fee per user, or a larger flat rate for unlimited users. Some include free limited versions, however. Below are some of these services available on the net:

Virtual Office: Afteroffice

Visto: Web-based scheduling, file storage, and management tools

OfficeClip: Clip your team together

PlanetIntra: Web-based management and communication tools

Punch Networks: The Internet file management platform

Your software needs will vary greatly depending on the type of work you are doing. Check with similar businesses or your industry association to find out what programs are preferred by your peers.

Don't forget about shareware too. There are a lot of great programs that may perform all of the tasks you need without the high price tag. Check out Tucows or ZDNet for shareware and freeware reviews and downloads.

PrintCitation & Date

Feedback

Other Equipment to Consider

In addition to computer equipment and software, you'll also need a good telephone. Caller ID helps by allowing you to screen out telemarketers or other calls you can't take at the moment.

A surge protector is necessary not just to give you additional outlets for your computer and its peripherals, but to protect your equipment.

You'll need a desk with plenty of workspace. It should have space for a computer, as well as room to spread out paperwork if necessary. A corner "L" shaped desk works well for this. Make sure the desk has

a large keyboard tray that can accommodate your mouse pad and mouse, as well. Many computer desks don't have large enough trays. Don't forget about space for your printer, scanner, fax and other equipment.

Shelves, cabinets, and file cabinets are also necessities that help tremendously by utilizing vertical space and keeping things organized. You may also be able to use the tops of these shelves and file cabinets for your printer, scanner, fax machine, etc.

Also, don't forget to invest in a comfortable chair that offers good back support. It should have as many adjustable parts as possible to help it fit your body. Arms on the chair will also make it more comfortable, particularly if you will be doing some work other than that on a computer. For more information on setting up your home office visit About.com's Home Office Furniture page.Virtual Business Communication Tools

Communications today are drastically different than they were even 15 years ago. E-mail has become a way of life and the only communication method you may have with some people. If you think about how you communicated in business in 1985 as opposed to how you communicate in business now, there's no comparison. You probably used your office phone, and... well, there wasn't a heck of a lot more back then... maybe a telex machine. Shortly after that, however, fax machines began to enter the market, then car phones and e-mail hit the scene. Things changed very quickly after that. As technology advanced, the expectations of the amount of work produced also advanced. Now, we produce a lot more work a lot faster and expectations of higher productivity continue to climb because technology is enabling us to do it faster.

With technology advancing so rapidly and workloads increasing along with it, the desire to work from home and alleviate some of the stress that comes along with commuting, juggling family life, etc. has also become very strong. In that respect, the same technology that took away our freedom is also allowing us more freedom than we've ever experienced ... well, except for back before technology forced us to work so hard!

So, what does that have to do with communications and how we can communicate in a virtual business environment? A large part of work in any business is tied into communications of one type or another. If you can communicate effectively you can work more effectively. Take advantage of the technology available for communications and use your new found freedom to take back some of your life. Here's how...

With a simple cell phone you can go to your child's softball game without fear of missing an important call.

By using wireless web technology via cell phone or a Personal Digital Assistant, you can go the grocery store while you're waiting on that e-mailed file that needs your approval before it can be submitted.

With a virtual assistant or readily available office services, you can work from your basement but have a professional address, and a receptionist answering your calls.

With video conferencing you can communicate face-to-face with clients or co-workers across the country without ever leaving your city.

With teleconferencing combined with Internet presentation software, you can communicate with several people in real time while you're all viewing the same presentation from locations around the world. Sonexisoffers these tools.

Using web hosted office tools you can perform scheduling, send files, communicate via chat rooms or instant messaging with co-workers, or clients.

In custom chat rooms or with instant messaging, you can have a discussion with several people from different locations and in situations where you can't necessarily talk.

Via web conferencing you can hold live interactive seminars, meetings, or other get togethers.

So, as you can see, communications in any office environment, whether virtual or not, are now quite simple and possible from almost anywhere. Don't forget, you also have the old standard, wired, corded, telephone you can use.

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Setting up an Internet Connection

All of this hardware and software won't do you any good if you don't have a connection to the Internet. Your best bet is a broadband connection if you can get access. The term broadband just means a high bandwidth technology like DSL, or cable that allows you to send and receive files, sound, and video over a single connection.

If you can get cable in your home then most likely you can also get a cable modem and Internet access. DSL uses your standard telephone line, but requires that you be located relatively close to the provider's central office (in some cases 3-4 miles). Check with local providers to see if DSL is available in your area. Many providers offer online tools that simply require you to enter your phone number to determine if service is available at your home.

If you live in an area without cable or DSL access, you still have the option of Internet access via satellite. These systems offer fast connections, but require satellite dishes and receivers as well as special modems. Click here for more information about satellite Internet access.

If you do get an "always on" broadband connection then you also need to put in a firewall. Read our article about How Firewalls Work to get the skinny on protecting your files from hackers.

Regardless of the type of connection you get to the Internet, you will need an ISP (Internet Service Provider). In addition to access to the Internet, your ISP will give you an e-mail address, and possibly 5-10 Mb of free space for a website. You can also get additional e-mail addresses from sites like HotMail orYahoo or Excite. These are free and the advantage of having one is that it doesn't have to change if you change your ISP. You can keep the same e-mail address and have the mail from that address forwarded to any other e-mail account you wish. It simply eliminates the process of sending out notices to all of your contacts that your e-mail address has changed. If you have an e-mail address from

your company that you use for business, it is often a good idea to get a separate e-mail address for your personal e-mail.

If you need to connect multiple computers in your home, read our article about How to Network Your Home.

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What about client meetings?

Meetings with clients can't always take place in cyberspace, or at the client's location. When you are faced with this situation, rather than having the client come to your home (assuming they are in the same city), look into executive suites or hotels that offer space that can be leased for short periods of time. If you own your own business and this is a common occurrence, you may want to consider leasing an Executive Suite that provides you with a receptionist, voice mail, e-mail, and other services, along with time-limited access to private offices, a reception area, and a meeting room. If you don't need this type of arrangement on a regular basis, you can also rent spaces on an hourly basis at a fairly reasonable rate.

For example, using Offices2Share.com, a meeting room at The Blake Building in Washington, DC with a seating capacity of five, reserved for three hours would be $75. A room for 15 for the same amount of time would be $120. These types of services can often be reserved online and maps, written directions, contact information, photos and information about additional room needs is also provided.

Of course, there are also always the other standard meeting place options that include hotel lobbies, restaurants, golf courses, etc.The Cyber-assistant

Back in the good old days of corner offices with big windows and a secretary outside your door, you knew exactly how to get the administrative part of your work done... your secretary handled it. But what do you do when you don't have that office or that secretary outside the door? The answer is simple, in a virtual office, you get a virtual assistant (VA).

Just go to your favorite web browser and type in "virtual assistant." You'll find many links to sites that are built, managed and maintained by those people who used to sit outside your door and help you manage business.

These assistants can do just about everything their predecessors did. (You will have to get your own coffee!) They can provide services on an as-needed basis. Or, if you prefer, you can contract a specific amount of time each day, week or month. By using e-mail, fax, and other electronic technologies, you can get work in and out just as quickly as before. You pay only for the time you use rather than paying someone to sit idly at a desk during slow periods. Many of these VA's also offer after-hours services. There is even a Virtual Assistant University (AssistU) that offers 20-week courses for Virtual Assistants

and awards degrees and certifications at both basic and masters levels. The program has received very good reviews.

What should you look for?Aside from the flexible work hours and the reduced cost involved, the benefits of using VAs also include the wide range of skills you can find. Although the AssistU defines a Virtual Assistant as a person that provides long-term administrative support for clients, you'll see many people referring to themselves as VAs and offering skills ranging from basic data entry to web design to accounting. Make sure you see samples of their work and speak with references. When you speak with their references, ask not only about the quality of the work, but the also about the speed and accuracy.

With these VAs being entrepreneurs themselves, you'll also find that they may be pretty savvy about new ways of getting things done more efficiently in a cyber-environment. Look for innovative ideas on their web sites, and effective layout and formatting of the sample documents they provide you. Also ask the references you speak with if the VA has offered any good ideas for improving the work process.Setting virtual office policies

If your business has employees all working from different locations telecommuting to your "virtual office," you should consider writing some home office guidelines. This will help standardize the capabilities of each worker, ensure compatible workflow throughout your system, and help you manage your employees in cyberspace.

Consider these issues in your guidelines:

Home office location -- Urge workers to set up their home offices in a separate room that will allow for uninterrupted work.

Home office equipment -- Provide or specify (depending on the situation) the minimum computer systems necessary for your workers.

Computer software -- Standardize the software programs used so files will be compatible and collaboration on documents will be possible.

Procedures for system logins -- Keeping track of who is accessing central databases and when may help you manage security, as well as your staff.

Procedures for submitting time-sheets

Accessibility requirements during regular business hours -- With the flexibility of working from home offices, you and your employees will find yourselves often working odd hours. Don't let this freedom prevent you from be accessible to other workers or your clients.

Procedures for forwarding calls or e-mails when workers must leave the office during business hours

Protocols for client interactions -- This should include e-mail protocols (since we tend to use less formal language in e-mail), traditional correspondence protocols, meeting place protocols, and any others that might come into play in your business.

These guidelines may not need to be formally printed and distributed, but there should be at least some thought put into the standards you want your employees to be aware of and follow. With regular

communication and open invitations to employees for suggestions that can bring about improvements your business can not only succeed, it can thrive.

q. data collaboration

What is Data Collaboration and why is it important?

Industries and institutions need a constant means of communication for the success of their establishment. Documents often need to be collaborated to allow the staff to exchange relevant information about their written works with the use of different documents in their system. The proper relaying of information plays a vital role in the effectiveness of making resolutions among the people who are directly involved in decision making for the establishment. And what better way to communicate one’s work with other people in the company is through data collaboration.

Data collaboration is a means of sharing files with others which allows them to add, modify, or remove text in the document. It incorporates file exchanges through email or by removable media such as thumb drives and the likes, local file servers, SharePoint, information portals, or Wikis. When engaging in this, an executive editor or another team generally oversees this documents which are usually a collection of written works done by various people. It is important to take note that the editors, writers, and chief staff should come into terms with the exact goals they want in their collaboration. So that when their goals have changed the people involved will know that the writing process has changed and they will be able to adjust.

The advantage of this method is that it allows more people to become involved thus resulting in a richer and intricate system. Plus, data collaboration allows more users to access the documents through the internet, making it easier and quicker for them to respond. This can be done with the help of data collaboration soft wares. When people are linked in to a particular data collaboration tool it will be easier for them to help address the issues needed to be solved and the administrators will be notified that a particular staff has done his part of the job. By allowing people to pitch in their ideas in the collaboration of data, organizations will produce lots of valuable insights, solutions, and suggestions that may help in acquiring better end results for the development of the establishment.

One great downfall of data collaboration is that almost all existing documents have diverse file formats, making this a challenge in opening the file one needs to access. It is important for institutions and the people involved in data collaboration to agree on what format will be used and to immediately troubleshoot compatibility issues so that this process will become a success.

q. crm and erp

CRM and ERP: What’s The Difference?Posted by CRM Switch Staff on August 8, 2013

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Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) are two sides of the same profitability coin. ERP and CRM are similar in many ways, as they are both used to increase the overall profitability of a business.These systems overlap in some areas, and can be completely integrated in others. However, as their core functionalities are completely different, it’s best for a business to first look at them as separate, stand-alone systems. When viewed separately, it’s easier to see how ERP and CRM each play a role in improving efficiency and increasing sales.What is CRM?Simply put, CRM is a system for recording and storing all information related to customer interactions.Simply put, CRM is a system for recording and storing all information related to customer interactions. CRM systems like Salesforce and Microsoft Dynamics CRMprovide a standardized method for collecting and sharing customer data and cataloging customer interactions. Since all of the data is standardized, it’s easily shared throughout the business. CRM can be used by executives to create sales projections, by sales reps to maintain contact with clients, by shipping clerks to verify addresses, and by the billing department to create invoices. The goal of CRM is to provide a comprehensive store of customer data that can be used to increase sales, improve customer retention, and make customer relations more efficient.

What is ERP?

Where CRM is focused on the customer, ERP focuses on the business. ERP is a system for improving the efficiency of business processes. Like CRM, ERP allows for the rapid sharing of standardized information throughout all departments. Executives, managers, and employees all enter information into the ERP system, creating a real-time, enterprise-wide snapshot. Problems in any area will automatically create alerts in other affected areas. This allows departments to begin planning for issues before they become a problem in that department. In short, by allowing the business to focus on the data, instead of the operations, ERP provides a method for streamlining business processes across the board. Popular ERP vendors like Epicor, SAP, andMicrosoft either also make CRM software, or their ERP solutions directly integrate with CRM from other vendors.A Distinction with a Difference

Though similar in effect, ERP and CRM systems use different approaches to increase profits. ERP focuses on reducing overhead and cutting costs. By making business processes more efficient, ERP reduces the amount of capital spent on those processes. CRM works to increase profits by producing greater sales volume. With a standardized repository of customer data, it’s easier for everyone, from executives to sales reps, to improve customer relations. In turn, those improved relations translate into increased brand loyalty and profits.

CRM? ERP? Both?ERP focuses on reducing overhead and cutting costs. By making business processes more efficient, ERP reduces the amount of capital spent on those processes.Whether a business needs both systems largely depends on the size and complexity of the business. Even for a small business, a CRM system is better than a haphazard collection of customer data stored on hand-written notes, in numerous emails, or, worse yet, contained solely in the head of a sales rep. Customer relations are the lifeblood of any business—CRM exists to keep that blood pumping freely.

ERP is an invaluable tool for streamlining complex business processes. Many small businesses start in a single room or small office. All of the “departments” may be within earshot of each other. At that point, software that can provide a real-time snapshot of every department may be overkill. As the business grows, the need for, and benefits of, ERP

become clearer. If, at any time, a manager or executive doesn’t know what’s going on in the departments they are responsible for, the time for ERP has long since arrived.

Assigning Importance

Deciding which system is more important is like deciding between having an engine or having a steering wheel in a car. CRM is the engine that drives a business. It improves sales and increases profits. ERP is the steering wheel—it allows a business to be guided with precision, and to steer around obstacles well in advance. ERP and CRM working together make it much easier for a business to increase profits while reducing costs.

Which Comes First?

A business has to have processes before it needs to worry about streamlining them. And it needs to have profits before worrying about cutting costs. The most streamlined, efficient business in the world is still bankrupt without sales. That’s why CRM is often the best bet for a business’s first investment. Generating and maintaining sales is usually what makes everything else possible. By helping to maximize sales figures, CRM can enable a business grow to the point that ERP becomes a necessity.

Maximizing Growth

Increased capital comes about in two ways: more sales or fewer expenses. Using ERP and CRM systems allows a business to pursue both of these avenues. The CRM system brings in more revenue through better sales figures, while the ERP system reduces overall operating expenses. Together, these systems can help a business pursue growth through efficiency and expansion simultaneously. Used separately, ERP and CRM can still be very helpful, but could potentially limit the business to a narrower avenue of growth.

 

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q. business intelligence

Business intelligenceFrom Wikipedia, the free encyclopedia

Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new opportunities. BI, in simple words, makes interpreting voluminous data friendly. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.[1]

Generally, Business Intelligence is made up of an increasing number of components, these are:

Multidimensional aggregation and allocation

Denormalization , tagging and standardization

Realtime reporting with analytical alert

Interface with unstructured data source

Group consolidation, budgeting and rolling forecast

Statistical inference  and probabilistic simulation

Key performance indicators optimization

Version control and process management

Open item management

BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics andprescriptive analytics.

Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence.[2]

Contents  [hide] 

1 History

2 Business intelligence and data warehousing

3 Business intelligence and business analytics

4 Applications in an enterprise

5 Prioritization of business intelligence projects

6 Success factors of implementation

o 6.1 Business sponsorship

o 6.2 Business needs

o 6.3 Amount and quality of available data

7 User aspect

8 BI Portals

9 Marketplace

o 9.1 Industry-specific

10 Semi-structured or unstructured data

o 10.1 Unstructured data vs. semi-structured data

o 10.2 Problems with semi-structured or unstructured data

o 10.3 The use of metadata

11 Future

12 See also

13 References

14 Bibliography

15 External links

History[edit]

The term Business Intelligence was originally first phrased by Richard Millar Devens’ in the ‘Cyclopædia of Commercial and Business Anecdotes’ from 1865. Devens used the term to describe how the banker Sir Henry Furnese, gained profit by receiving and acting upon information about his environment, prior to his competitors. “Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news.” (Devens, (1865), p. 210). The ability to collect and react accordingly based on the information retrieved, an ability that Furnese excelled in, is today still at the very heart of BI.[3]

In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He employed the Webster's dictionary definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."[4]

Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.

In 1988, an Italian-Dutch-French-English consortium organized an international meeting on the Multiway Data Analysis in Rome.[5] The ultimate goal is to reduce the multiple dimensions down to one or two (by detecting the patterns within the data) that can then be presented to human decision-makers.

In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."[6] It was not until the late 1990s that this usage was widespread.[7]

Business intelligence and data warehousing[edit]

Often BI applications use data gathered from a data warehouse or a data mart. A data warehouse is a copy of analytical data that facilitates decision support. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.

To distinguish between the concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways:

Using a broad definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making."[8] When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack.

Forrester defines the latter, narrower business intelligence market as, "...referring to just the top layers of the BI architectural stack such as reporting, analytics anddashboards."[9]

Business intelligence and business analytics[edit]

Thomas Davenport argues that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.[10]

Applications in an enterprise[edit]

Business intelligence can be applied to the following business purposes, in order to drive business value.[citation needed]

1. Measurement  – program that creates a hierarchy of performance metrics (see also Metrics

Reference Model) and benchmarking that informs business leaders about progress towards

business goals (business process management).

2. Analytics  – program that builds quantitative processes for a business to arrive at optimal

decisions and to perform business knowledge discovery. Frequently involves: data

mining, process mining, statistical analysis, predictive analytics, predictive

modeling, business process modeling, complex event processing and prescriptive analytics.

3. Reporting /enterprise reporting – program that builds infrastructure for strategic reporting to

serve the strategic management of a business, not operational reporting. Frequently

involves data visualization, executive information system and OLAP.

4. Collaboration /collaboration platform – program that gets different areas (both inside and

outside the business) to work together through data sharing and electronic data interchange.

5. Knowledge management  – program to make the company data driven through strategies

and practices to identify, create, represent, distribute, and enable adoption of insights and

experiences that are true business knowledge. Knowledge management leads to learning

management and regulatory compliance.

In addition to the above, business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met. For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via email or another monitoring service. This end-to-end process requires data governance, which should be handled by the expert.[citation needed]

Prioritization of business intelligence projects[edit]

It is often difficult to provide a positive business case for business intelligence initiatives and often the projects must be prioritized through strategic initiatives. Here are some hints and advantages to increase the benefits for a BI project.

As described by Kimball[11] you must determine the tangible benefits such as eliminated cost of

producing legacy reports.

Enforce access to data for the entire organization.[12] In this way even a small benefit, such as a

few minutes saved, makes a difference when multiplied by the number of employees in the

entire organization.

As described by Ross, Weil & Roberson for Enterprise Architecture,[13] consider letting the BI

project be driven by other business initiatives with excellent business cases. To support this

approach, the organization must have enterprise architects who can identify suitable business

projects.

Use a structured and quantitative methodology to create defensible prioritization in line with the

actual needs of the organization, such as a weighted decision matrix.[14]

Success factors of implementation[edit]

Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:[15]

1. The level of commitment and sponsorship of the project from senior management

2. The level of business need for creating a BI implementation

3. The amount and quality of business data available.

Business sponsorship[edit]The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment.[16] This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.[17]

It is important that personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group. However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage. This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground.

Another management problem that should be encountered before start of implementation is if the business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data may add many months to the original plan, it's wise to make sure the person from management is aware of his actions.

Business needs[edit]Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation.[18] The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.

Companies that implement BI are often large, multinational organizations with diverse subsidiaries.[19] A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist.

Amount and quality of available data[edit]Without good data, it does not matter how good the management sponsorship or business-driven motivation is. Without proper data, or with too little quality data, any BI implementation fails. Before implementation it is a good idea to do data profiling. This analysis identifies the “content, consistency and structure [..]”[18] of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on the shelf temporarily while the IT department figures out how to properly collect data.

When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.

Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence include but are not limited to:

1. Go through business data sources in order to collect needed data

2. Convert business data to information and present appropriately

3. Query and analyze data

4. Act on those data collected

The quality aspect in business intelligence should cover all the process from the source data to the final reporting. At each step, the quality gates are different:

1. Source Data:

Data Standardization: make data comparable (same unit, same pattern..)

Master Data Management:  unique referential

2. Operational Data Store (ODS) :

Data Cleansing:  detect & correct inaccurate data

Data Profiling: check inappropriate value, null/empty

3. Datawarehouse :

Completeness: check that all expected data are loaded

Referential integrity:  unique and existing referential over all sources

Consistency between sources: check consolidated data vs sources

4. Reporting:

Uniqueness of indicators: only one share dictionary of indicators

Formula accuracy: local reporting formula should be avoided or checked

User aspect[edit]

Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization.[20][21] If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system. If the system does not add value to the users´ mission, they simply don't use it.[21]

To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase.[20] This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions.

When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.[20]

Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.[21]

Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball[20] suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found. By doing so, managers can see how well their departments are doing and compare

themselves to others and this may spur them to encourage their staff to utilize the BI system even more.

In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive.[22] Watson describes how a large call centre implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics. Also, agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance.

BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support.[22] Training encourages more people to use the BI application.[20]

Providing user support is necessary to maintain the BI system and resolve user problems. [21] User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team.[20]

BI Portals[edit]

A Business Intelligence portal (BI portal) is the primary access interface for Data Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the users first impression of the DW/BI system. It is typically a browser application, from which the user has access to all the individual services of the DW/BI system, reports and other analytical functionality. The BI portal must be implemented in such a way that it is easy for the users of the DW/BI application to call on the functionality of the application.[23]

The BI portal's main functionality is to provide a navigation system of the DW/BI application. This means that the portal has to be implemented in a way that the user has access to all the functions of the DW/BI application.

The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users.[24]

The BI portal needs to be easy to use and understand, and if possible have a look and feel similar to other applications or web content of the organization the DW/BI application is designed for (consistency).

The following is a list of desirable features for web portals in general and BI portals in particular:

Usable

User should easily find what they need in the BI tool.Content Rich

The portal is not just a report printing tool, it should contain more functionality such as

advice, help, support information and documentation.Clean

The portal should be designed so it is easily understandable and not over complex as to

confuse the usersCurrent

The portal should be updated regularly.Interactive

The portal should be implemented in a way that makes it easy for the user to use its

functionality and encourage them to use the portal. Scalability and customization give the

user the means to fit the portal to each user.Value Oriented

It is important that the user has the feeling that the DW/BI application is a valuable resource

that is worth working on.

Marketplace[edit]

There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and consolidated "megavendors" that have entered the market through a recent trend[25] of acquisitions in the BI industry.[26]

Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service).[27]

Industry-specific[edit]Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations. The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing law.

Semi-structured or unstructured data[edit]

Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news. According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once.[28]

The management of semi-structured data is recognized as a major unsolved problem in the information technology industry.[29] According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.[29][30] Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly informed decision making.[28]

Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.[30]

Unstructured data vs. semi-structured data[edit]Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a BLOB (binary large object), a catch-all data type available in most relational databasemanagement systems. Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document.

Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore it may be more accurate to talk about this as semi-structured documents or data,[29] but no specific consensus seems to have been reached.

Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms. Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution.

Problems with semi-structured or unstructured data[edit]There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,[31] some of those are:

1. Physically accessing unstructured textual data – unstructured data

is stored in a huge variety of formats.

2. Terminology  – Among researchers and analysts, there is a need to

develop a standardized terminology.

3. Volume of data – As stated earlier, up to 85% of all data exists as

semi-structured data. Couple that with the need for word-to-word

and semantic analysis.

4. Searchability of unstructured textual data – A simple search on

some data, e.g. apple, results in links where there is a reference to

that precise search term. (Inmon & Nesavich, 2008)[31] gives an

example: “a search is made on the term felony. In a simple search,

the term felony is used, and everywhere there is a reference to

felony, a hit to an unstructured document is made. But a simple

search is crude. It does not find references to crime, arson, murder,

embezzlement, vehicular homicide, and such, even though these

crimes are types of felonies.”

The use of metadata[edit]To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use ofmetadata.[28] Many systems already capture some metadata (e.g. filename, author, size, etc.), but more useful would be metadata about the actual content – e.g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction.

Future[edit]

A 2009 Gartner paper predicted[32] these developments in the business intelligence market:

Because of lack of information, processes, and tools, through 2012,

more than 35 percent of the top 5,000 global companies regularly fail to

make insightful decisions about significant changes in their business

and markets.

By 2012, business units will control at least 40 percent of the total

budget for business intelligence.

By 2012, one-third of analytic applications applied to business

processes will be delivered through coarse-

grained application mashups.

A 2009 Information Management special report predicted the top BI trends: "green computing, social networkingservices, data visualization, mobile BI, predictive analytics,composite applications, cloud computing and multitouch."[33]

Other business intelligence trends include the following:

Third party SOA-BI products increasingly address ETL issues of

volume and throughput.

Companies embrace in-memory processing, 64-bit processing, and

pre-packaged analytic BI applications.

Operational applications have callable BI components, with

improvements in response time, scaling, and concurrency.

Near or real time BI analytics is a baseline expectation.

Open source BI software replaces vendor offerings.

Other lines of research include the combined study of business intelligence and uncertain data.[34][35] In this context, the data used is not assumed to be precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI.

According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008.[citation needed]

An article by InfoWorld’s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.[36]

q. data warehousing

Data warehouseFrom Wikipedia, the free encyclopedia

[hide]This article has multiple issues. Please help improve it or discuss these issues on theThis article needs additional citations for verification. (February 2008)

This article has an unclear citation style. (September 2013)

Data Warehouse Overview

In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting.

The typical extract-transform-load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[1]

A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. The integrated data source systems may be considered to be a part of a distributed operational data store layer. Data federation methods or data virtualization methods may be used to access the distributed integrated source data systems to consolidate and aggregate data directly into the data warehouse database tables. Unlike the ETL-based data warehouse, the integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems.

A data mart is a small data warehouse focused on a specific area of interest. Data warehouses can be subdivided into data marts for improved performance and ease of use within that area.

Alternatively, an organization can create one or more data marts as first steps towards a larger and more complex enterprise data warehouse.

This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.

Contents  [hide] 

1 Benefits of a data warehouse

2 Generic data warehouse environment

3 History

4 Information storage

o 4.1 Facts

o 4.2 Dimensional vs. normalized approach for storage of data

5 Top-down versus bottom-up design methodologies

o 5.1 Bottom-up design

o 5.2 Top-down design

o 5.3 Hybrid design

6 Data warehouses versus operational systems

7 Evolution in organization use

8 See also

9 References

10 Further reading

11 External links

Benefits of a data warehouse[edit]

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to :

Congregate data from multiple sources into a single database so a single query engine can be

used to present data.

Mitigate the problem of database isolation level lock contention in transaction processing

systems caused by attempts to run large, long running, analysis queries in transaction

processing databases.

Maintain data history, even if the source transaction systems do not.

Integrate data from multiple source systems, enabling a central view across the enterprise. This

benefit is always valuable, but particularly so when the organization has grown by merger.

Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad

data.

Present the organization's information consistently.

Provide a single common data model for all data of interest regardless of the data's source.

Restructure the data so that it makes sense to the business users.

Restructure the data so that it delivers excellent query performance, even for complex analytic

queries, without impacting the operational systems.

Add value to operational business applications, notably customer relationship

management (CRM) systems.

Making decision–support queries easier to write.

Generic data warehouse environment[edit]

The environment for data warehouses and marts includes the following:

Source systems that provide data to the warehouse or mart;

Data integration technology and processes that are needed to prepare the data for use;

Different architectures for storing data in an organization's data warehouse or data marts;

Different tools and applications for the variety of users;

Metadata, data quality, and governance processes must be in place to ensure that the

warehouse or mart meets its purposes.

In regards to source systems listed above, Rainer states, “A common source for the data in data warehouses is the company’s operational databases, which can be relational databases”.[2]

Regarding data integration, Rainer states, “It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse”.[2]

Rainer discusses storing data in an organization’s data warehouse or data marts.”.[2]

Metadata are data about data. “IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures“.[2]

Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers.[2] A “data warehouse” is a repository of historical data that are

organized by subject to support decision makers in the organization.[2] Once data are stored in a data mart or warehouse, they can be accessed.

History[edit]

The concept of data warehousing dates back to the late 1980s[3] when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "data marts" that were tailored for ready access by users.

Key developments in early years of data warehousing were:

1960s — General Mills and Dartmouth College, in a joint research project, develop the

terms dimensions and facts.[4]

1970s — ACNielsen and IRI provide dimensional data marts for retail sales.[4]

1970s — Bill Inmon begins to define and discuss the term: Data Warehouse

1975 — Sperry Univac introduces MAPPER (MAintain, Prepare, and Produce Executive

Reports) is a database management and reporting system that includes the world's first4GL. It

was the first platform designed for building Information Centers (a forerunner of contemporary

Enterprise Data Warehousing platforms)

1983 — Teradata introduces a database management system specifically designed for decision

support.

1983 — Sperry Corporation Martyn Richard Jones defines the Sperry Information Center

approach, which while not being a true DW in the Inmon sense, did contain many of the

characteristics of DW structures and process as defined previously by Inmon, and later by

Devlin. First used at the TSB England & Wales

1984 — Metaphor Computer Systems, founded by David Liddle and Don Massaro, releases

Data Interpretation System (DIS). DIS was a hardware/software package and GUI for business

users to create a database management and analytic system.

1988 — Barry Devlin and Paul Murphy publish the article An architecture for a business and

information system in IBM Systems Journal where they introduce the term "business data

warehouse".

1990 — Red Brick Systems, founded by Ralph Kimball, introduces Red Brick Warehouse, a

database management system specifically for data warehousing.

1991 — Prism Solutions, founded by Bill Inmon, introduces Prism Warehouse Manager,

software for developing a data warehouse.

1992 — Bill Inmon publishes the book Building the Data Warehouse.[5]

1995 — The Data Warehousing Institute, a for-profit organization that promotes data

warehousing, is founded.

1996 — Ralph Kimball publishes the book The Data Warehouse Toolkit.[6]

2000 — Daniel Linstedt releases the Data Vault, enabling real time auditable Data Warehouses

warehouse.

Information storage[edit]

Facts[edit]A fact is a value or measurement, which represents a fact about the managed entity or system.

Facts as reported by the reporting entity are said to be at raw level.

E.g. if a BTS received 1,000 requests for traffic channel allocation, it allocates for 820 and rejects the remaining then it would report 3 facts or measurements to a management system:

tch_req_total = 1000

tch_req_success = 820

tch_req_fail = 180

Facts at raw level are further aggregated to higher levels in various dimensions to extract more service or business-relevant information out of it. These are called aggregates or summaries or aggregated facts.

E.g. if there are 3 BTSs in a city, then facts above can be aggregated from BTS to city level in network dimension. E.g.

Dimensional vs. normalized approach for storage of data[edit]There are three or more leading approaches to storing data in a data warehouse — the most important approaches are the dimensional approach and the normalized approach.

The dimensional approach, whose supporters are referred to as “Kimballites”, believe in Ralph Kimball’s approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/star schema. The normalized approach, also called the 3NF model (Third Normal Form), whose supporters are referred to as “Inmonites”, believe in Bill Inmon's approach in

which it is stated that the data warehouse should be modeled using an E-R model/normalized model.

In a dimensional approach, transaction data are partitioned into "facts", which are generally numeric transaction data, and "dimensions", which are the reference information that gives context to the facts. For example, a sales transaction can be broken up into facts such as the number of products ordered and the price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.

A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly.[citation needed] Dimensional structures are easy to understand for business users, because the structure is divided into measurements/facts and context/dimensions. Facts are related to the organization’s business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008).

The main disadvantages of the dimensional approach are the following:

1. In order to maintain the integrity of facts and dimensions, loading the data warehouse with

data from different operational systems is complicated.

2. It is difficult to modify the data warehouse structure if the organization adopting the

dimensional approach changes the way in which it does business.

In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008)[citation needed]. The main advantage of this approach is that it is straightforward to add information into the database. Some disadvantages of this approach are that, because of the number of tables involved, it can be difficult for users to join data from different sources into meaningful information and to access the information without a precise understanding of the sources of data and of the data structure of the data warehouse.

It should be noted that both normalized and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the two models is the degree of normalization (also known as Normal Forms).

These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008).

In Information-Driven Business,[7] Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents (even when the same fields are used in both models) but this extra information comes at the cost of usability. The technique measures information quantity in terms of Information Entropy and usability in terms of the Small Worlds data transformation measure.[8]

Top-down versus bottom-up design methodologies[edit]

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inappropriate external links. (November 2012)

Bottom-up design[edit]Ralph Kimball,[9] designed an approach to data warehouse design known as bottom-up.[10]

In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes.

Data marts contain, primarily, dimensions and facts. Facts can contain either atomic data and, if necessary, summarized data. The single data mart often models a specific business area such as "Sales" or "Production." These data marts can eventually be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.

The integration of the data marts in the data warehouse is centered on the conformed dimensions (residing in "the bus") that define the possible integration "points" between data marts. The actual integration of two or more data marts is then done by a process known as "Drill across". A drill-across works by grouping (summarizing) the data along the keys of the (shared) conformed dimensions of each fact participating in the "drill across" followed by a join on the keys of these grouped (summarized) facts.

Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent.

Business value can be returned as quickly as the first data marts can be created, and the method lends itself well to an exploratory and iterative approach to building data warehouses. For example, the data warehousing effort might start in the "Sales" department, by building a Sales-data mart. Upon completion of the Sales-data mart, the business might then decide to expand the warehousing activities into the, say, "Production department" resulting in a Production data mart. The requirement for the Sales data mart and the Production data mart to be integrable, is that they share the same "Bus", that will be, that the data warehousing team has made the effort to identify and implement the conformed dimensions in the bus, and that the individual data marts links that information from the bus. The Sales-data mart is good as it is (assuming that the bus is complete) and the Production-data mart can be constructed virtually independent of the Sales-data mart (but not independent of the Bus).

If integration via the bus is achieved, the data warehouse, through its two data marts, will not only be able to deliver the specific information that the individual data marts are designed to do, in this example either "Sales" or "Production" information, but can deliver integrated Sales-Production information, which, often, is of critical business value.

Top-down design[edit]Bill Inmon, has defined a data warehouse as a centralized repository for the entire enterprise.[11] The top-down approach is designed using a normalized enterprise data model."Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision, the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities. Gartner released a research note confirming Inmon's definition in 2005[12] with additional clarity plus they added one additional attribute.

The data warehouse is:

Subject-oriented

The data in the data warehouse is organized so that all the data elements relating to the

same real-world event or object are linked together.Non-volatile

Data in the data warehouse are never over-written or deleted — once committed, the data

are static, read-only, and retained for future reporting.Integrated

The data warehouse contains data from most or all of an organization's operational systems

and these data are made consistent.Time-variant

For an operational system, the stored data contains the current value. The data

warehouse, however, contains the history of data values.No virtualization

A data warehouse is a physical repository.[12]

The top-down design methodology generates highly consistent dimensional views of data across data marts since all data marts are loaded from the centralized repository. Top-down design has also proven to be robust against business changes. Generating new dimensional data marts against the data stored in the data warehouse is a relatively simple task. The main disadvantage to the top-down methodology is that it represents a very large project with a very broad scope. The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial. In addition, the top-down methodology can be inflexible and unresponsive to changing departmental needs during the implementation phases.[11]

Hybrid design[edit]Data warehouse (DW) solutions often resemble the hub and spokes architecture. Legacy systems feeding the DW/BI solution often include customer relationship management(CRM) and enterprise resource planning solutions (ERP), generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load(ETL) process, DW solutions often make use of an operational data store (ODS). The information from the ODS is then parsed into the actual DW. To reduce data redundancy, larger systems will often store the data in a normalized way. Data marts for specific reports can then be built on top of the DW solution.

It is important to note that the DW database in a hybrid solution is kept on third normal form to eliminate data redundancy. A normal relational database however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The DW effectively provides a single source of information from which the data marts can read, creating a highly flexible solution from a BI point of view. The hybrid architecture allows a DW to be replaced with a master data management solution where operational, not static information could reside.

The Data Vault Modeling components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both 3rd normal form andstar schema. The Data Vault model is not a true 3rd normal form, and breaks some of the rules that 3NF dictates be followed. It is however, a top-down architecture with a bottom up design. The Data Vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which when built, still requires the use of a data mart or star schema based release area for business purposes.

Data warehouses versus operational systems[edit]

Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model. Operational system designers generally follow the Codd rules of database normalization in order to ensure data integrity. Codd defined five increasingly stringent rules of normalization. Fully normalized database designs (that is, those satisfying all five Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables. Relational databases are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. Finally, in order to improve performance, older data are usually periodically purged from operational systems.

Evolution in organization use[edit]

These terms refer to the level of sophistication of a data warehouse:

Offline operational data warehouse

Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily,

weekly or monthly) from the operational systems and the data is stored in an integrated

reporting-oriented dataOffline data warehouse

Data warehouses at this stage are updated from data in the operational systems on a regular

basis and the data warehouse data are stored in a data structure designed to facilitate

reporting.On time data warehouse

Online Integrated Data Warehousing represent the real time Data warehouses stage data in

the warehouse is updated for every transaction performed on the source dataIntegrated data warehouse

These data warehouses assemble data from different areas of business, so users can look

up the information they need across other systems.[13]

q. knowledge mgmt.

Knowledge managementFrom Wikipedia, the free encyclopediaNot to be confused with Information management.

Knowledge management (KM) is the process of capturing, developing, sharing, and effectively using organisational knowledge.[1] It refers to a multi-disciplined approach to achieving organisational objectives by making the best use of knowledge.[2]

An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library andinformation sciences (Alavi & Leidner 1999).[3][4] More recently, other fields have started contributing to KM research; these include information and media, computer science,public health, and public policy.[5] Columbia University and Kent State University offer dedicated Master of Science degrees in Knowledge Management.[6][7]

Many large companies, public institutions and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments.[8] Several consulting companies provide strategy and advice regarding KM to these organisations.[8]

Knowledge management efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organisation.[9] KM efforts overlap with organisational learning and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge.[2][10] It is seen as an enabler of organisational learning[11] and a more concrete mechanism than the previous abstract research.[12]

[13]

Contents  [hide] 

1 History

2 Research

o 2.1 Dimensions

o 2.2 Strategies

o 2.3 Motivations

o 2.4 Technologies

3 See also

4 References

5 External links

History[edit]

Knowledge management efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs.[2][12] With increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems,knowledge repositories, group decision support systems, intranets, and computer-supported cooperative work have been introduced to further enhance such efforts.[2]

In 1999, the term personal knowledge management was introduced; it refers to the management of knowledge at the individual level.[14]

In the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement.[15][16] Key lessons learned include people and the cultural norms which influence their behaviors are the most critical resources for successful knowledge creation, dissemination, and application; cognitive, social, and organizational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking, and incentives are essential to accelerate the learning process and to drive cultural change.[16] In short, knowledge management programs can yield impressive benefits to individuals and organizations if they are purposeful, concrete, and action-oriented.

Research[edit]

KM emerged as a scientific discipline in the earlier 1990s.[17] It was initially supported solely by practitioners, when Skandia hired Leif Edvinsson of Sweden as the world's firstChief Knowledge Officer (CKO).[18] Hubert Saint-Onge (formerly of CIBC, Canada), started investigating KM long before that.[2] The objective of CKOs is to manage and maximize the intangible assets of their organisations.[2] Gradually, CKOs became interested in practical and theoretical aspects of KM, and the new research field was formed.[19]Discussion of the KM idea has been taken up by academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport(Babson College) and Baruch Lev (New York University).[3][20] In 2001, Thomas A. Stewart, former editor at Fortune magazine and subsequently the editor of Harvard Business Review, published a cover story highlighting the importance of intellectual capital in organisations.[21] Since its establishment, the KM discipline has been gradually moving towards academic maturity.[2] First, there is a trend toward higher cooperation among academics; particularly, there has been a drop in single-authored publications. Second, the role of practitioners has changed.[19] Their contribution to academic research has been dramatically declining from 30% of overall contributions up to 2002, to only 10% by 2009 (Serenko et al. 2010).[22]

A broad range of thoughts on the KM discipline exist; approaches vary by author and school. [19][23] As the discipline matures, academic debates have increased regarding both the theory and practice of KM, to include the following perspectives:

Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and

creation.[24][25]

Organisational with a focus on how an organisation can be designed to facilitate knowledge

processes best.[8]

Ecological  with a focus on the interaction of people, identity, knowledge, and environmental

factors as a complex adaptive system akin to a natural ecosystem.[26][27]

Regardless of the school of thought, core components of KM include people, processes, technology (or) culture, structure, technology, depending on the specific perspective(Spender & Scherer 2007).

Different KM schools of thought include lenses through which KM can be viewed and explained, to include:

community of practice  [28]

social network analysis [29]

intellectual capital (Bontis & Choo 2002)

information theory  (McInerney 2002)[18]

complexity science [30]

constructivism(Nanjappa & Grant 2003)[31]

The practical relevance of academic research in KM has been questioned (Ferguson 2005) with action research suggested as having more relevance (Andriessen 2004) and the need to translate the findings presented in academic journals to a practice (Booker, Bontis & Serenko 2008).[15][15][32][33]

Dimensions[edit]Different frameworks for distinguishing between different 'types of' knowledge exist.[12] One proposed framework for categorizing the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge.[30] Tacit knowledge represents internalized knowledge that an individual may not be consciously aware of, such as how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others. (Alavi & Leidner 2001).[19] Similarly, Hayes and Walsham (2003) describe content and relational perspectives of knowledge and knowledge management as two fundamentally different epistemological perspectives.[34] The content perspective suggest that knowledge is easily stored because it may be codified, while the relational perspective recognizes the contextual and relational aspects of knowledge which can make knowledge difficult to share outside of the specific location where the knowledge is developed.[34]

The Knowledge Spiral as described by Nonaka & Takeuchi.

Early research suggested that a successful KM effort needs to convert internalized tacit knowledge into explicit knowledge to share it, and the same effort must permit individuals to internalize and make personally meaningful any codified knowledge retrieved from the KM effort.[8][35] Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory.[14] Specifically, for knowledge to be made explicit, it must be translated into information

(i.e., symbols outside of our heads) (Serenko & Bontis 2004).[14] Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995).[36] In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 're-internalized' into implicit knowledge.[36] More recently, together with Georg von Krogh and Sven Voelpel, Nonaka returned to his earlier work in an attempt to move the debate about knowledge conversion forwards (Nonaka, von Krogh & Voelpel 2006);[37] (Nonaka, von Krogh & 2009).[4]

A second proposed framework for categorizing the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems(Sensky 2002).[38]

A third proposed framework for categorizing the dimensions of knowledge distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organisation, or community.[34][39] Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer.[39]

Strategies[edit]Knowledge may be accessed at three stages: before, during, or after KM-related activities.[40] Organisations have tried knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans.[41] Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged.[9]

One strategy to KM involves actively managing knowledge (push strategy).[9][42] In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository.[42] This is commonly known as the Codification approach to KM.[42]

Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy).[9][42] In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002).[30] This is commonly known as the Personalisation approach to KM.

Hansen et al. propose a simple framework, distinguishing two opposing KM strategies: codification and personalization.[43] Codification focuses on collecting and storing codified knowledge in previously designed electronic databases to make it accessible to the organisation.[44] Codification can therefore refer to both tacit and explicit knowledge.[45] In contrast, the personalization strategy aims at encouraging individuals to share their knowledge directly.[44] Information technology plays a less important role, as it is only supposed to facilitate communication and knowledge sharing among members of an organisation.

Other knowledge management strategies and instruments for companies include:[9][26][30]

Rewards (as a means of motivating for knowledge sharing)

Storytelling  (as a means of transferring tacit knowledge)

Cross-project learning

After action reviews

Knowledge mapping (a map of knowledge repositories within a company accessible by all)

Communities of practice

Expert directories (to enable knowledge seeker to reach to the experts)

Best practice transfer

Knowledge fairs

Competence management (systematic evaluation and planning of competences of individual

organisation members)

Proximity & architecture (the physical situation of employees can be either conducive or

obstructive to knowledge sharing)

Master-apprentice relationship

Collaborative technologies (groupware, etc.)

Knowledge repositories (databases, bookmarking engines, etc.)

Measuring and reporting intellectual capital (a way of making explicit knowledge for companies)

Knowledge brokers  (some organisational members take on responsibility for a specific "field"

and act as first reference on whom to talk about a specific subject)

Social software  (wikis, social bookmarking, blogs, etc.)

Inter-project knowledge transfer

Motivations[edit]There are a number of claims as to the motivations leading organisations to undertake a KM effort.[46] Typical considerations driving a KM effort include:[30]

Making available increased knowledge content in the development and provision

of products and services

Achieving shorter new product development cycles

Facilitating and managing innovation and organisational learning

Leveraging the expertise of people across the organisation

Increasing network connectivity between internal and external individuals

Managing business environments and allowing employees to obtain relevant insights

and ideas appropriate to their work

Solving intractable or wicked problems

Managing intellectual capital and intellectual assets in the workforce (such as the expertise

and know-how possessed by key individuals)

Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002).[47] Knowledge sharing remains a challenging issue for knowledge management, while there is no clear agreement barriers may include time issues for knowledge works, the level of trust, lack of effective support technologies and culture (Jennex 2008).[48]

Technologies[edit]Early KM technologies included online corporate yellow pages as expertise locators and document management systems.[24] Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s.[24] Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice (Capozzi 2007).[49] Knowledge management systems can thus be categorized as falling into one or more of the following groups: Groupware, document management systems, expert systems, semantic networks, relational and object oriented databases, simulation tools, and artificial intelligence [9]

More recently, development of social computing tools (such as bookmarks, blogs, and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organisations.[33][50] However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer.[51] These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels(Andrus 2005).[5]

q. data mining

Data Miningby Doug Alexander

dea@tracor.com

 

Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers. It discovers information within the data that queries and reports can't effectively reveal. This paper explores many aspects of data mining in the following areas:

Data Rich, Information Poor Data Warehouses What is Data Mining? What Can Data Mining Do? The Evolution of Data Mining How Data Mining Works Data Mining Technologies Real-World Examples The Future of Data Mining Privacy Concerns Explore Further on the Internet

Data Rich, Information Poor

The amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database. Raw data by itself, however, does not provide much information. In today's fiercely competitive business

environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.

Data Warehouses

The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers and potential customers stored in "Data Warehouses." Data warehouses are becoming part of the technology. Data warehouses are used to consolidate data located in disparate databases. A data warehouse stores large quantities of data by specific categories so it can be more easily retrieved, interpreted, and sorted by users. Warehouses enable executives and managers to work with vast stores of transactional or other data to respond faster to markets and make more informed business decisions. It has been predicted that every business will have a data warehouse within ten years. But merely storing data in a data warehouse does a company little good. Companies will want to learn more about that data to improve knowledge of customers and markets. The company benefits when meaningful trends and patterns are extracted from the data.

What is Data Mining?

Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.

Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.

What Can Data Mining Do?

Although data mining is still in its infancy, companies in a wide range of industries - including retail, finance, heath care, manufacturing transportation, and aerospace - are already using data mining tools and techniques to take advantage of historical data. By using pattern recognition technologies and statistical and mathematical techniques to sift through warehoused information, data mining helps analysts recognize significant

facts, relationships, trends, patterns, exceptions and anomalies that might otherwise go unnoticed.

For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Specific uses of data mining include:

Market segmentation - Identify the common characteristics of customers who buy the same products from your company.

Customer churn - Predict which customers are likely to leave your company and go to a competitor.

Fraud detection - Identify which transactions are most likely to be fraudulent. Direct marketing - Identify which prospects should be included in a mailing list

to obtain the highest response rate. Interactive marketing - Predict what each individual accessing a Web site is

most likely interested in seeing. Market basket analysis - Understand what products or services are commonly

purchased together; e.g., beer and diapers. Trend analysis - Reveal the difference between a typical customer this month

and last.

Data mining technology can generate new business opportunities by:

Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in a large database. Questions that traditionally required extensive hands-on analysis can now be directly answered from the data. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.

Automated discovery of previously unknown patterns: Data mining tools sweep through databases and identify previously hidden patterns. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Using massively parallel computers, companies dig through volumes of data to discover patterns about their customers and products. For example, grocery chains

have found that when men go to a supermarket to buy diapers, they sometimes walk out with a six-pack of beer as well. Using that information, it's possible to lay out a store so that these items are closer.

AT&T, A.C. Nielson, and American Express are among the growing ranks of companies implementing data mining techniques for sales and marketing. These systems are crunching through terabytes of point-of-sale data to aid analysts in understanding consumer behavior and promotional strategies. Why? To gain a competitive advantage and increase profitability!

Similarly, financial analysts are plowing through vast sets of financial records, data feeds, and other information sources in order to make investment decisions. Health-care organizations are examining medical records to understand trends of the past so they can reduce costs in the future.

The Evolution of Data Mining

Data mining is a natural development of the increased use of computerized databases to store data and provide answers to business analysts.

Evolutionary Step Business Question Enabling Technology

Data Collection (1960s)"What was my total revenue in the last five years?"

computers, tapes, disks

Data Access (1980s)"What were unit sales in New England last March?"

faster and cheaper computers with more storage, relational databases

Data Warehousing and Decision Support

"What were unit sales in New England last March? Drill down to Boston."

faster and cheaper computers with more storage, On-line analytical processing (OLAP), multidimensional databases, data warehouses

Data Mining

"What's likely to happen to Boston unit sales next month? Why?"

faster and cheaper computers with more storage, advanced computer algorithms

Traditional query and report tools have been used to describe and extract what is in a database. The user forms a hypothesis about a relationship and verifies it or discounts it with a series of queries against the data. For example, an analyst might hypothesize that people with low income and high debt are bad credit risks and query the database to verify or disprove this assumption. Data mining can be used to generate an hypothesis. For example, an analyst might use a neural net to discover a pattern that

analysts did not think to try - for example, that people over 30 years old with low incomes and high debt but who own their own homes and have children are good credit risks.

How Data Mining Works

How is data mining able to tell you important things that you didn't know or what is going to happen next? That technique that is used to perform these feats is called modeling. Modeling is simply the act of building a model (a set of examples or a mathematical relationship) based on data from situations where the answer is known and then applying the model to other situations where the answers aren't known. Modeling techniques have been around for centuries, of course, but it is only recently that data storage and communication capabilities required to collect and store huge amounts of data, and the computational power to automate modeling techniques to work directly on the data, have been available.

As a simple example of building a model, consider the director of marketing for a telecommunications company. He would like to focus his marketing and sales efforts on segments of the population most likely to become big users of long distance services. He knows a lot about his customers, but it is impossible to discern the common characteristics of his best customers because there are so many variables. From his existing database of customers, which contains information such as age, sex, credit history, income, zip code, occupation, etc., he can use data mining tools, such as neural networks, to identify the characteristics of those customers who make lots of long distance calls. For instance, he might learn that his best customers are unmarried females between the age of 34 and 42 who make in excess of $60,000 per year. This, then, is his model for high value customers, and he would budget his marketing efforts to accordingly.

Data Mining Technologies

The analytical techniques used in data mining are often well-known mathematical algorithms and techniques. What is new is the application of those techniques to general business problems made possible by the increased availability of data and inexpensive storage and processing power. Also, the use of graphical interfaces has led to tools becoming available that business experts can easily use.

Some of the tools used for data mining are:

Artificial neural networks - Non-linear predictive models that learn through training and resemble biological neural networks in structure.

Decision trees - Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.

Rule induction - The extraction of useful if-then rules from data based on statistical significance.

Genetic algorithms - Optimization techniques based on the concepts of genetic combination, mutation, and natural selection.

Nearest neighbor - A classification technique that classifies each record based on the records most similar to it in an historical database.

Real-World Examples

Details about who calls whom, how long they are on the phone, and whether a line is used for fax as well as voice can be invaluable in targeting sales of services and equipment to specific customers. But these tidbits are buried in masses of numbers in the database. By delving into its extensive customer-call database to manage its communications network, a regional telephone company identified new types of unmet customer needs. Using its data mining system, it discovered how to pinpoint prospects for additional services by measuring daily household usage for selected periods. For example, households that make many lengthy calls between 3 p.m. and 6 p.m. are likely to include teenagers who are prime candidates for their own phones and lines. When the company used target marketing that emphasized convenience and value for adults - "Is the phone always tied up?" - hidden demand surfaced. Extensive telephone use between 9 a.m. and 5 p.m. characterized by patterns related to voice, fax, and modem usage suggests a customer has business activity. Target marketing offering those customers "business communications capabilities for small budgets" resulted in sales of additional lines, functions, and equipment.

The ability to accurately gauge customer response to changes in business rules is a powerful competitive advantage. A bank searching for new ways to increase revenues from its credit card operations tested a nonintuitive possibility: Would credit card usage and interest earned increase significantly if the bank halved its minimum required payment? With hundreds of gigabytes of data representing two years of average credit card balances, payment amounts, payment timeliness, credit limit usage, and other key parameters, the bank used a powerful data mining system to model the impact of the proposed policy change on specific customer categories, such as customers consistently near or at their credit limits who make timely minimum or small payments. The bank discovered that cutting minimum payment requirements for

small, targeted customer categories could increase average balances and extend indebtedness periods, generating more than $25 million in additional interest earned,

Merck-Medco Managed Care is a mail-order business which sells drugs to the country's largest health care providers: Blue Cross and Blue Shield state organizations, large HMOs, U.S. corporations, state governments, etc. Merck-Medco is mining its one terabyte data warehouse to uncover hidden links between illnesses and known drug treatments, and spot trends that help pinpoint which drugs are the most effective for what types of patients. The results are more effective treatments that are also less costly. Merck-Medco's data mining project has helped customers save an average of 10-15% on prescription costs.

The Future of Data Mining

In the short-term, the results of data mining will be in profitable, if mundane, business related areas. Micro-marketing campaigns will explore new niches. Advertising will target potential customers with new precision.

In the medium term, data mining may be as common and easy to use as e-mail. We may use these tools to find the best airfare to New York, root out a phone number of a long-lost classmate, or find the best prices on lawn mowers.

The long-term prospects are truly exciting. Imagine intelligent agents turned loose on medical research data or on sub-atomic particle data. Computers may reveal new treatments for diseases or new insights into the nature of the universe. There are potential dangers, though, as discussed below.

Privacy Concerns

What if every telephone call you make, every credit card purchase you make, every flight you take, every visit to the doctor you make, every warranty card you send in, every employment application you fill out, every school record you have, your credit record, every web page you visit ... was all collected together? A lot would be known about you! This is an all-too-real possibility. Much of this kind of information is already stored in a database. Remember that phone interview you gave to a marketing company last week? Your replies went into a database. Remember that loan application you filled out? In a database. Too much information about too many people for anybody to make sense of? Not with data mining tools running on massively parallel processing computers! Would you feel comfortable about someone (or lots of someones) having access to all this data about you? And remember, all this

data does not have to reside in one physical location; as the net grows, information of this type becomes more available to more people.

Check out:

http://www.kron.com/nc4/contact4/stories/computer_privacy.html

http://www.privacyrights.org

http://www.cfp.org

 

Explore Further on the Internet

 

Introduction to Data Mining

http://www-pcc.qub.ac.uk/tec/courses/datamining/stu_notes/dm_book_1.html

Information about data mining research, applications, and tools:

http://info.gte.com/kdd/

http://www.kdnuggets.com

http://www.ultragem.com/

http://www.cs.bham.ac.uk/~anp/TheDataMine.html

http://www.think.com/html/data_min/data_min.htm

http://direct.boulder.ibm.com/bi/

http://www.software.ibm.com/data/

http://coral.postech.ac.kr/~swkim/software.html

http://www.cs.uah.edu/~infotech/mineproj.html

http://info.gte.com/~kdd/index.html

http://info.gte.com/~kdd/siftware.html

http://iris.cs.uml.edu:8080/

http://www.datamining.com/datamine/welcome.htm

 

Data Sets to test data mining algorithms:

http://www.scs.unr.edu/~cbmr/research/data.html

 

Data mining journal (Read Usama M. Fayyad's editorial.):

http://www.research.microsoft.com/research/datamine/

 

Interesting application of data mining:

http://www.nba.com/allstar97/asgame/beyond.html

 

Data mining papers:

http://www.satafe.edu/~kurt/index.shtml

http://www.cs.bham.ac.uk/~anp/papers.html

http://coral.postech.ac.kr/~swkim/old_papers.html

 

Data mining conferences:

http://www-aig.jpl.nasa.gov/kdd97

http://www.cs.bahm.ac.uk/~anp/conferences/html

 

Conference on very large databases:

http://www.vldb.com/homepage.htm

 

Sites for datamining vendors and products:

American Heuristics (Profiler)

http://www.heuristics.com

Angoss software (Knowledge Seeker)

http://www.angoss.com

Attar Software (XpertRule Profiler)

http://www.attar.com

Business Objects (BusinessMiner)

http://www.businessobjects.com

DataMind (DataMind Professional)

http://www.datamind.com

HNC Software (DataMarksman, Falcon)

http://www.hncs.com

HyperParallel (Discovery)

http://www.hyperparallel.com

Information Discovery Inc. (Information Discovery System)

http://www.datamining.com

Integral Solutions (Clementine)

http://www.isl.co.uk/index.html

IBM (Intelligent Data Miner)

http://www.ibm.com/Stories/1997/04/data1.html

Lucent Technologies (Interactive Data Visualization)

http://www.lucent.com

NCR (Knowledge Discovery Benchmark)

http://www.ncr.com

NeoVista Sloutions (Decision Series)

http://www.neovista.com

Nestor (Prism)

http://www.nestor.com

Pilot Software (Pilot Discovery Server)

http://www.pilotsw.com

Seagate Software Systems (Holos 5.0)

http://www.holossys.com

SPSS (SPSS)

http://www.spss.com

Thinking Machines (Darwin)

http://www.think.com

 

Go to Top of Page

 

 

 

 

 

q. enterprising portal by blackberry enterprise server

BlackBerry Enterprise ServerFrom Wikipedia, the free encyclopedia

This article is outdated. Please update this article to reflect recent events or newly available information. (August 2013)

BlackBerry Enterprise Server designates the middleware software package that is part of the BlackBerry wireless platform supplied by BlackBerry Ltd. The software and service connects to messaging and collaboration software (MDaemon Messaging Server,[1] Microsoft Exchange, Lotus Domino, Novell GroupWise) on enterprise networks and redirects emails and synchronizes contacts and calendaring information between servers, desktop workstations, and mobile devices. Some third-party connectors exist, including Scalix,Zarafa, Zimbra, and the Google Apps BES Connector, although these are not supported by RIM.

Contents  [hide] 

1 BES Versions

2 BES Components

o 2.1 Log Files

3 Managing BES

4 BPS and BES Express

o 4.1 BlackBerry Enterprise Server Resource Kit

5 References

BES Versions[edit]

2.2: BES for Domino

3.6: BES for Exchange

4.0: BES for Exchange, Domino, and GroupWise

4.1: BES for Exchange, Domino, and GroupWise

5.0: BES for Exchange, Domino, and GroupWise

Prior to version 4.0, BES was largely two separate codebases, with the 2.2 version for Domino and the 3.6 version for Exchange. In version 4.0 and beyond, much of the code is integrated, but separate distributions still remain for each supported mail platform. Beginning with version 4.1.2, the company introduced a new option, BlackBerry Enterprise Server for Applications, which provides a secure wireless gateway for BlackBerry devices without requiring the device owner to possess an email account. The latest major revision, version 5.0, was released in 2009 for Exchange and Domino; support for GroupWise was added in 2010.

BES Components[edit]

BES consists of a set of Windows services that carry out the basic operations of the system. These Windows Services can include (additional services may be installed depending on configuration):

BlackBerry Alert

BlackBerry Attachment Service

Retrieves and converts attachments to a format specific for BlackBerry device

For documents with file extensions .doc, .xls, .ppt, .pdf, .wpd, and .txt, the Attachment

Service renders the content into the Universal Content Stream format for viewing on the

device.[2]

BlackBerry Collaboration Service

Provides IM services

BlackBerry Controller

Monitors the status of the BlackBerry services. Services are started if failed or stopped up to

ten times in a row.

BlackBerry Database Consistency Service

BlackBerry Dispatcher

All communication between the BlackBerry components passes through this service

BlackBerry Instant Messaging Connector

BlackBerry Messaging Agent

Performs wireless calendar synchronization

Generates initial encryption key

Provides email and lookup services

BlackBerry MDS Connection Service

Services push requests from intranet applications

BlackBerry MDS Services - Apache Tomcat Service

Sends and receives internet/intranet web browsing to device through BlackBerry Dispatcher

service

Used for data flow with 3rd party Java applications on device

BlackBerry Policy Service

Pushes wireless IT policies to devices

Performs new Encryption Key Generation

Sets command for device locks and remote wipe

BlackBerry Router

Routes all data to wireless device

Link between BES (can be installed on same server) and SRP host

BlackBerry Synchronization Service

Performs OTA backup and synchronization of all PIM data (contacts, tasks and notes)

except calendar.

BlackBerry User Administration Service (Only 3.6 and BlackBerry Resource Kit (BRK) in 4.0 and

4.1)

Log Files[edit]BES also produces a set of log files during operation, called the BES Event Log. The log files include (for a BES v4.0 and 4.1 system connecting to Microsoft Exchange):

ALRT - BES Alert

BBIM - BlackBerry Instant Messenger (4.1)

BBUA - BlackBerry User Administration Service (BRK)

CBCK - Backup Connector

CEXC - MS Exchange PIM Connector

CNTS - Lotus Notes/Domino Connector

CMNG - Management Connector

CTRL - BlackBerry Controller

DISP - BlackBerry Dispatcher

MAGT - BlackBerry Mailbox Agent (aka BlackBerry Messaging Agent)

MAST - Mail Store Service

MDAT - Mobile Data Services

MDSS - MDS Services (4.1)

MDSS-DISCOVERY - MDS Services (4.1)

POLC - Policy Service

ROUT - Router

SYNC - BlackBerry SyncServer

PhoneCallLog (4.1)

PINLog (4.1)

SMSLog (4.1)

Managing BES[edit]

BES is typically deployed and managed within the enterprise by messaging administrators (for example, the individuals already responsible for managing Microsoft Exchange, Lotus Domino, or Novell GroupWise) or sometimes a dedicated IT person, usually called the BlackBerry or BES Administrator.

BPS and BES Express[edit]

As of 2010, RIM is offering the Blackberry Enterprise Server Express edition with no user limitations - for free. It has a few minor feature limitations, but only requires a data plan - not a BES plan. It enables near-instant seamless mail and calendar integration with your existing exchange server (incoming emails often appear on the Blackberry handset beforethey appear in the Outlook or OWA clients), which is an advantage over the existing Outlook Web Access model that the Blackberry Internet Service offers.The free downloadrequires a license code, which may be delayed due to demand.

As of January 2007, RIM is offering a free version of BES called BlackBerry Professional Software, which is a free download from the BlackBerry website and includes 1 user license. BPSE provides a "...wireless communications and collaboration solution designed specifically for small and medium-sized businesses." It comes with one CAL (Client Access License) - meaning one BlackBerry handheld can be activated on the Server, and you can add up to 29 additional CALs (for a total of 30) or upgrade to BlackBerry Enterprise Server at any time.It's only available for Notes and Exchange environments. BPS is based on the 4.1 code, but with a more simplified management tool, and a limited amount of patches are being released. (BPS is "stuck" at v 4.1.4, where full BES is at 4.1.6 plus maintenance Releases). As such BPS is missing out on some functions that full BES did get from SP6 onwards, such as rich content email, free/busy search, remote search.

Blackberry Professional Software - originally called Blackberry Enterprise Server Express - first appeared with version 4. There was BES (full), and BES Express (same as full, but no cost, limited maximum number of clients, and 1 CAL included free), they then renamed BES Express to BPS, but it was still the same thing, and then later they introduced BES Express 5, which is rather different to the previous BES Express and BPS primarily because it allows non-BES devices/subscribers to have a "full-fat" Blackberry experience which is finally cost/feature competitive with Exchange ActiveSync devices, i.e. provides full synchronisation of read/unread status, deletions, mail subfolders (including sent items), contacts, and calendar, all without license costs and without the requirement of an expensive and sometimes extremely difficult to acquire BES tariff. (For example Orange in the UK may make it difficult to supply BES package/provisioning to less than 10 handsets at a time, and other operators often have extreme difficulty applying the BES package to handsets/subscribers that were originally BIS supplied.)

For GroupWise RIM offered Blackberry Enterprise Server. As of 2011 version 5.0 is available.

BlackBerry Enterprise Server Resource Kit[edit]

The BlackBerry Enterprise Server Resource Kit (BRK) is a collection of useful tools and other resources released to expand the manageability of the BlackBerry Enterprise Server. These tools are available as a free download. Each BRK release is developed and certified compatible for each particular BES service pack version.

The BRK includes tools such as:

BlackBerry User Administration Service (BESUserAdmin)—Allows administrators to

perform user and smartphone administration on the command line level.

NoResponseCheck Tool—Analyzes the logs for threads reporting as non-

responsive to differentiate between non-responsive and slow threads.

MessageFlow Tool—Tracks the flow of mail from the mail server through the

BlackBerry Enterprise Server to the BlackBerry smartphone and provides statistics in a csv file

AvailIndex Tool—Analyzes log files and produces a snapshot report of user activity

for a certain time in a CSV file.

HistoricalStats Tool—Checks usage patterns of individual users and provides

statistics on a per-day per-user basis.

OutOf Coverage Tool—Checks for users who have not sent/received in a specified

period of time.

Pending Tool—Tracks messages pending delivery in BlackBerry Enterprise Server.

Delayed Notifics administrators to detect when BlackBerry Enterprise Server is no longer

receiving notifications for new email in a timely fashion.

MapiCdoErrors Tool—Allows administrators to scan the logs for common MAPI/CDO

errors and custom events they wish to choose.

MDSPush vs Pull Tool—Enables Administrators to monitor whether the BlackBerry Mobile

Data Service is processing more data by push or by pull.

BlackBerry SysLog Service—Provides Administrators with real-time monitoring of

BlackBerry Enterprise Server log events.

BlackBerry Domain Administration History Tool—Audits configuration changes to the

BlackBerry Enterprise Server environment and outputs to a csv file

Log Monitor Tool—Monitors a text file for one or more events and allows Administrators to

specify actions that they want the tool to perform after it finds a value that meets the set criteria.

Message Receipt Confirmation Tool—Provides Administrators with real-time verification

that the Blacsages to BlackBerry devices

Enterprise Activation Status Tool—Provides the ability to monitor the changing activation status

of a BlackBerry smartphone and to troubleshoot activation issues.

Upgrade IT Policy Template Tool—Allows administrators to upgrade the IT policy template for

the BlackBerry Configuration Database with new BlackBerry smartphone policy rules without

upgrading the BlackBerry Enterprise Server software.

There are also special support tools available to those who contact the support group.

Enterprise portalFrom Wikipedia, the free encyclopedia

An enterprise portal, also known as an enterprise information portal (EIP), is a framework for integrating information, people and processes across organizational boundaries. Enterprise portals provide a secure unified access point,[1] often in the form of a web-based user interface, and are designed to aggregate and personalize information through application-specific portlets.

One hallmark of enterprise portals is the de-centralized content contribution and content management, which keeps the information always updated. Another distinguishing characteristic is that they cater for customers, vendors and others beyond an organization's boundaries.[2] This contrasts with a corporate portal which is structured for roles within an organization.

Contents  [hide] 

1 History

2 Employee portal

3 Lean portal

4 Fundamental features

5 Common applications

6 See also

7 References

8 External links

History[edit]

The mid-1990s saw the advent of public Web portals like AltaVista, AOL, Excite, and Yahoo!. These sites provided a key set of features (e.g., news, e-mail, weather, stock quotes, and search) that were often presented in self-contained boxes or portlets. Before long, enterprises of all sizes began to see a need for a similar starting place for their variety of internal repositories and applications, many of which were migrating to Web-based technologies.[3]

By the late 1990s, software vendors began to produce prepackaged enterprise portals. These software packages would be toolkits for enterprises to quickly develop and deploy their own customized enterprise portal. The first commercial portal software vendor began to appear in 1998. Pioneers in this marketing included "pure play" vendors likeEpicentric, Plumtree Software and Viador. The space, however, quickly became crowded by 2002, with the entry into the market of competing product offerings from application server vendors (such as BEA, IBM, Passageways, Oracle Corporation and Sun Microsystems), who saw portals as an opportunity to

stave off the commoditization of application server technology, and Open Source vendors such as Liferay or eXo Platform. In 2003, vendors of Java-based enterprise portals produced a standard known as JSR-168. It was to specify an API for interoperability between enterprise portals and portlets. Software vendors began producing JSR-168 compliant portlets that can be deployed onto any JSR-168 compliant enterprise portal. The second iteration of the standard, JSR-286, was final-released on 12 June, 2008. Enterprises may choose to develop multiple enterprise portals based on business structure and strategic focus while reusing architectural frameworks, component libraries, or standardized project methods (e.g. B2E, B2C, B2B, B2G, etc.).

Employee portal[edit]

A study conducted in 2006 by Forrester Research, Inc. showed that 46 percent of large companies used a portal referred to as an employee portal. Employee portals can be described as a specific set of enterprise portals and are used to give an interface for employees to personalized information, resources, applications, and e-commerce options.[4]

Lean portal[edit]

In 2009, Gartner introduced the concept of the portal-less portal or the “lean portal”. Lean Portals offer an alternative to the traditional portals available for the last 15 years, which have become very difficult to deploy and maintain. Traditional portals are bloated with features that aren’t necessarily cost-effective to businesses. This leads to a lot of frustration for companies thinking of investing in a portal as the traditional model forces them to exceed their budgets for features they don’t want or need, without being able to deliver the results they wanted. In contrast, a Lean Portal is lightweight and easy to deploy. It’s built using modern Web 2.0 technologies, such as AJAX, widgets, representation state transfer (REST) and WOA/SOA approaches. Lean Portal offerings from vendors like Backbase and Liferay replace the traditional container-oriented portal model while maintaining the main purpose of a portal — providing a personalized point of access that allows customers to find relevant information, read about business processes and reach people. According to Gartner, organizations who opted for a Lean Portal found that it delivered more than 80% of the required functionality within months of launching, without compromising security or advanced integration requirements.

Fundamental features[edit]

An enterprise portal has two main functions; integration and presentation.[5] It must be able to access information from multiple and varied sources and manipulate that information through the portal.

Other common features include;

Single Sign-On — enterprise portals can provide single sign-on capabilities between their users

and various other systems. This requires a user to authenticate only once.

Integration — the connection of functions and data from multiple systems into new

components/portlets/web parts with an integrated navigation between these components.

Federation — the integration of content provided by other portals, typically through the use

of WSRP or similar technologies.

Customization — Users can customize the look and feel of their environment. Customers who

are using EIPs can edit and design their own web sites which are full of their own personality

and own style; they can also choose the specific content and services they prefer. Also refers to

the ability to prioritize most appropriate content based on attributes of the user and metadata of

the available content.

Personalization — Personalization is more about matching content with the user. Based on a

user profile, personalization uses rules to match the "services", or content, to the specific user.

To some degree, you can think of the two like this: customization is in hands of the end user,

personalization is not. Of course actual personalization is often based on your role or job

function within the portal context.

Access Control — the ability for portal to limit specific types of content and services users have

access to. For example, a company's proprietary information can be entitled for only company

employee access. This access rights may be provided by a portal administrator or by a

provisioning process. Access control lists manage the mapping between portal content and

services over the portal user base.

Enterprise Search — search enterprise content using enterprise search

Common applications[edit]

Content Management System

Document Management System

Collaboration Software

Business process management systems

Customer Relationship Management

Business Intelligence

Intranet

Wiki

Blog

RSS

Employee portal