ANALYTICS An imperative for Sustaining and

Differentiating.

“A little knowledge that acts is worth infinitely more than much knowledge

that is idle.”

Khalil Gibran

Submitted by:

Madhuja Mukherjee

Nikhil Kansari

PGP2, BIM TRICHY

TEAM NAME- B3 (BONG, BHARTI,

BUSINESS)

Summary:-

With global economy tumbling around contingent issues, industries giving up with their implemented

strategies, organizations are tumbling to deliver an efficient value chain. Be it a B2C market or a B2B

market everyone wants to offer superior business value. Nobody wants to become next SATYAM,

PRICEWATERHOUSECOOPERS, CITIBANK or LEHMAN BROTHERS. In an era where head to

head competition is growing, marketers need something different to sustain. So the question for the

hour is WHAT NEXT? Well the answer lies in Business Analytics. Today when everyone offers

similar kind of products and services, business processes can be the point of difference. Organizations

often face issues in areas like: Customer segmentation, Buyer behavior, Customer profitability, Fraud

detection, Customer attrition and Channel optimization. Various Analytic Applications have been

develop to address those issues, but still there are some areas where we cannot use analytics e.g.

Personnel relations. Enterprise Resource Systems (ERP), Point-of-Sale (POS) systems and Web sites,

have created better transaction data that can be utilized to sustain a healthy Bottom Line. A new

generation of technically literate executives is coming into organizations and looking for new ways to

manage them with the help of technology.

Purpose/Goal:-

Generation next is moving to Cloud, every single organization wants to utilize the Utility Business

model to become more cost effective and customer centric. Rapidly growing organizations have

recognized the potential of business analytics and have aggressively moved to realize it. The purpose

of this white paper is to provide an in-depth view for importance of Analytics. How organization can

achieve sustainability and differentiation and use Analytics as a critical success factor in next

generation technology. It will give you insights regarding risks while choosing options to run: whether

to run with numbers or with guts.

Introduction:-

Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in

areas rich with recorded information, analytics rely on the simultaneous application of statistics,

computer programming and operations research to quantify performance. The most common

application of analytics is the study of business data with an eye to predicting and improving business

performance in the future. Analytics is unique in that it leverages a number of competencies and assets

that can typically be applied to multiple discrete value-creating activities in an organization.

Organizations often delve in questions like:-

Q) What market segments do my customers fall into, and what are their characteristics?

Q) Which customers are most likely to respond to my promotion?

Q) What is the lifetime profitability of my customer?

Q) How can I tell which transactions are likely to be fraudulent?

Q) Which customer is at risk of leaving?

Q) What is the best channel to reach my customer in each segment?

The initial phase of computerized decisions were implemented using (DSS) Decision support systems

like enterprise information systems (EIS), Group support systems (GSS), enterprise resource

management (ERM), enterprise resource planning (ERP), supply chain management (SCM),

Knowledge management systems (KMS) and Customer relationship management (CRM). Then came

an era of Business intelligence where data and systems both were used to take decisions and intelligent

tools were built to mine and extract information from past collected data. However, data is just the

baseline and requires additional tools to make it work for you and your line of business. This is where

the term analytics comes into play.

Basically analytics is observed by inclusion of at least one model. Model is a simplified representation

or abstraction of reality. They are classified, based on their degree of abstraction, as Iconic, Analog or

Mathematical model. But merely application of those models doesn’t provide any thumb rule to come

to a decision. Data mining is the next generation tool to apply business intelligence at its best.

Organizations have huge amount of data in there data warehouses which should be utilized by data

mining algorithms. Big Data is the pretty contemporary concept in line with data mining in Analytics.

Data mining in contrast:

Data mining is the nontrivial process of identifying valid, novel, potentially useful, and ultimately

understandable patterns in data stored in structured databases. Vastly it has 3 major components which

are used extensively in Analytics i.e. Prediction, Association and Clustering. Areas where data mining

can be applied as application;

A) Customer Relationship Management

i) Maximize return on marketing campaigns

ii) Improve customer retention (churn analysis)

iii) Maximize customer value (cross-, up-selling)

iv) Identify and treat most valued customers

B) Banking and Other Financial

i) Automate the loan application process

ii) Detecting fraudulent transactions

iii) Maximize customer value (cross-, up-selling)

iv) Optimizing cash reserves with forecasting

C) Retailing and Logistics

i) Optimize inventory levels at different locations

ii) Improve the store layout and sales promotions

iii) Optimize logistics by predicting seasonal effects

iv) Minimize losses due to limited shelf life

D) Manufacturing and Maintenance

i) Predict/prevent machinery failures

ii) Identify anomalies in production systems to optimize the use manufacturing capacity

iii) Discover novel patterns to improve product quality

E) Brokerage and Securities Trading

i) Predict changes on certain bond prices

ii) Forecast the direction of stock fluctuations

iii) Assess the effect of events on market movements

iv) Identify and prevent fraudulent activities in trading

F) Insurance

i) Forecast claim costs for better business planning

ii) Determine optimal rate plans

iii) Optimize marketing to specific customers

iv) Identify and prevent fraudulent claim activities

Reporting or Descriptive Analytics

Modelling or Predictive Analytics

ClusteringAffinity

Grouping

All the aforementioned applications of data mining are being capitalized by organizations. Business

analytics are the parts and parcel of these applications where the analysts apply various tools &

algorithms to extract useful content and take decisions. The demand for the generation next technology

is to increase the AQ (analytical quotient) of organizations. If we consider the situation in India it can

be a Megatrend, according to a recent discussion in IIM Bangalore panels it was found; if we look at IT

offshoring, half the CMM Level 5 companies are in India but our domestic penetration and application

of IT is abysmal. If you measure the IT spend in India versus Capital expenditure, we rank at number

30 in the world. It is also true that the application of IT domestically may be lagging behind because of

the lack of demanding customers. However, one must make a beginning and it would be a very good

idea if the B-Schools in the country were to take leadership here1.

Business analytics in simple terms refer to the using of hindsight to better the insight and create a more

sound foresight into business planning.

The types of business analytics in existence are:

----------------------------------------------------------------------------------------------------------------------------- ------------------------------------

1- Murthy, Ishwar; “Business Analytics in India -- Opportunities and Challenges: Discussion”; IIMB Management Review

(Indian Institute of Management Bangalore); Jun2006, Vol. 18 Issue 2, p175-191, 17p.

Descriptive Analytics basically help to mine data to provide business insights. Predictive analytics on the other

hand refers to the predictions about future events based on the historical data and facts with the aid of statistical

techniques like modeling, machine learning, data mining and game theory. In business it is used to identify

risks and opportunities by exploiting the patterns evolved of historical data. Clustering is mainly utilized in

explorative data mining and is deemed to be a common technique for statistical data analysis used in varied

fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics.

Last but not the least affinity grouping is a business tool used to organize ideas and data. Commonly used

within project management, it helps to sort large number of ideas into groups based on their natural

relationships for review and analysis.

Good Data Won’t Guarantee Good Decisions

It is being found that most of organizations have three categories of employees: - ―Visceral decision

makers‖, who seldom trust analysis, they rely on intuitions and make decisions unilaterally. Second

category is ―Unquestioning empiricists‖ – They are kind of people who trust analysis over judgment,

and values consensus. Third kind is called ―Informed Skeptics‖, who applies judgment to analysis; they

listen to others but are willing to dissent. In most of the organizations there is always a skill deficit

among the employees, do they know what data to use and when to use effectively. It is being observed

that organizations face four kinds of problems while deciding over Big Data investments.

1. Analytic skills are concentrated in too few employees. Instead of searching new talent for

adapting analytics organization should train the existing employees at various levels.

2. IT needs to spend more time on the ―I‖ and less on the ―T.‖Firms should not always focus on

streams like Finance, HR or supply chain where business needs are clearly defined. Rather they

should focus in areas where the business needs are ambiguous; at this stage they should use

behavioral understanding and anthropological skills.

3. Reliable information exists, but it’s hard to locate. Organizations lack an accessible structure

for the data they have collected.

4. Business executives don’t manage in-formation as well as they manage talent, capital, and

brand. Executives consider data as something to handle by the IT department only and do not

want to deep dive into it.

So the need of the hour is to develop more of Informed Skeptics in your organization. Organize

knowledge management programs where you can develop Knowledge repository which can be easily

accessed by employees and executives both. Those trained knowledge workers can definitely

overcome those above stated four problems and contribute to the bottom line effectively. Because it

doesn’t matter how many Big Data analytics you have in your organization until and unless they are

backed by big decision makers.

Pros and Cons of Customer Analytics

In service industry a customer is everything, most of service organization devote major pie of their

investments in satisfying customers and building relationships with them. That is what we often call as

CRM (customer relationship management), organization gather customer centric data from point of

sales and various other interactions then those data are mapped in dashboards or scorecards to

understand the trend and the gaps. Today’s distracted consumers, bombarded with information and

options, often struggle to find the products or services that will best meet their needs. Advances in

information technology, data gathering, and analytics are making it possible to deliver something like

or perhaps even better than the proprietor’s advice.

Suppose we consider example of Retail chains like Bigbazar and Spencers where daily lakhs of

customers come for shopping they even get loyalty cards for their purchases. Now if a Credit Card

Company or an Insurance Company buys or hires access to point of sales & Loyalty card holders

data/information it can unleash new chambers for both the companies to understand their customers

better and provide better service than their competitors. Credit histories, demographic studies, analyses

of socioeconomic status, and so on can be used to predict depression, back pain, and other expensive

chronic conditions. Now this information can be mined and analyzed deeply to unveil credit worthiness

and insurers value by various customer centric credit card and insurance companies.

It’s not only about those credit cards or insurance company; customer analytics can be developed in IT

and ITeS, hospitals, hotels, Banks etc. But there needs a decorum to be built while collecting customer

centric information, because if the customers once gets to know that his/her data is shared among

organization there can be a difficulty in maintaining the relation once again. Therefore it is imperative

for organizations to consider the confidentiality of the customer data which is used in analytics.

Consider Microsoft’s success with e-mail offers for its search engine Bing. Those e-mails are tailored

to the recipient at the moment they’re opened. In 200 milliseconds—a lag imperceptible to the

recipient-advanced analytics software assembles an offer based on real-time information about him or

her: data including location, age, gender, and online activity both historical and immediately preceding,

along with the most recent responses of other customers. These ads have lifted conversion rates by as

much as 70%—dramatically more than similar but not customized marketing efforts. So technology

and strategies are used to create next best offers in order achieve differentiation.

Analytics means business so we can move to a next level to decide over a model that can be used to

provide better customer oriented services. In Service marketing we have three value proposition

models that are used by organization with respect to the product/service they offer.

1. Operational excellence: - Companies excel at competitive price, product quality and on-time

delivery.

2. Customer intimacy: - Companies excel at offering personalized service to customers and at

building long-term relation with them.

3. Product leadership: - Companies excel at creating unique product that pushes the envelope.

In generation next technology where almost every business model becoming obsolete day by day,

bottom line and top line of organizations are on peril . Organizations need to choose an effective model

to sustain. We can recommend Customer Intimacy model as most effective to implement, as be it

product or service, ultimately companies spend a lot in creating value propositions and value chains to

satisfy their customers.

Using the above model, customer centric organizations can create value proposition for their

customers. They can differentiate and sustain on the aforementioned attributes and relations. Customer

Analytics can be applied to the data that is being collected in warehouses and accordingly we can apply

our models. Now for such kind of value proposition there must be an equally apt value chain which

should have components to satisfy the customers more effectively than competitors. Due to reverse

engineering process imitators can copy your product or services, so to create the differentiation one

needs to emphasis on value chain too.

Figure shows value chain with respect to business analytics value and opportunity space.

Domains of Business Analytics

The very variation in the domains itself explains the importance that analytics enjoys in the

contemporary business scenario. It has practically pervaded every field enhancing the performance and

yield of the field in concern. An edge over the competitors is what every business seeks, business

analytics categorically responds to that need. The following examples will help comprehend better

exactly how indispensable it is in the process of creating differentiation and providing the necessary

competitive edge.

Marketing it the right way to grasp the target customers mind has always been a challenge in itself.

However, the perk of marketing lies in its challenges. Nowadays retail business with its terrific boom

has enhanced this competition as different brands are available under the same roof. The chance of

becoming shifters according to market changes have increased exponentially. Hence comes in the retail

sales analytics. In the recent past Oracle has set forth an exemplary release with its ―Oracle Retail

Merchandising Analytics‖ that helps to pull data from multiple retail systems and enable retailers to

quickly decide if they should change pricing, product orders, or take other actions to meet sales and

profit performance goals, thereby attesting the mandate necessity of such an web-based business

intelligence application in the given scenario of cut throat competition.

Roping in Oracle yet again the ―Oracle Financial Analytics‖ helps to portray well the role of analytics

in financial services. It helps front-line managers improve financial performance with complete, up-to-

the-minute information on their departments' expenses and revenue contributions. With its numerous

key performance indicators and reports it also enables the financial managers to improve cash flow,

lower costs, meanwhile increasing profitability. It also helps to maintain more accurate, timely, and

transparent financial reporting that helps ensure Sarbanes-Oxley compliance.

The risk and credit analytics can be done using SAS. It helps to access and aggregate data across

disparate systems, seamlessly integrates the credit scoring/internal rating processes with the concerned

companies overall credit portfolio risk assessment, accurately forecasts, measures, monitors and reports

potential credit risk exposures across the entire organization on both counterparty and portfolio levels,

allowing seamless integration of credit scoring with credit risk, evaluating alternative strategies for

pricing, hedging or transferring credit risk, optimizing allocation of regulatory capital and economic

Retail Sales Analytics

Financial Services Analytics

Risk and Credit Analytics

Talent Analytics

Marketing Analytics Behavioral Analytics Collections Analytics Fraud Analytics

Pricing Analytics TelecommunicationsSupply Chain

AnalyticsTransportation

Analytics

capital, meeting the reporting and risk disclosure requirements of regulators and investors for a wide

variety of regulations, such as Basel II and finally managing the entire life cycle of a loan from

origination, to servicing, to collection/recovery. Other example includes that of CMSR Hotspot

Profiling Analysis. This helps to drill-down data; systematically and detects important relationships,

co-factors, interactions, dependencies and associations amongst many variables and values accurately

using Artificial Intelligence techniques, and generate profiles of most interesting segments. Hotspot

analysis can identify profiles of high (and low) risk loans accurately through thorough systematic

analysis of all available data.

The Cognos Talent Analytics as a module for IBM Cognos Workforce Performance helps to provide

standard reports that help in simplifying the analysis and assessment of talent management programs,

providing the industry's most comprehensive workforce performance solution.

The SAP CRM Analytics helps to get to the bottom of marketing analytics. The analysis of information

concerning markets, rivals, and past marketing initiatives, help one to assess and thereby affect the

success of future advertising initiatives and campaigns proper from the planning phase. Advertising

Analytics lets one achieve detailed insights and arrive at detailed analysis results that one can then

deploy within the operational processes in marketing.

Quantivo Behavioral Analytics enables to give behavioral analytics a new shape. It helps to identify

what behaviours are highly correlated and what types of affinities exist in the data, delivers a

comprehensive view of customer behaviours across multiple data sources, and provides query results in

―train-of-thought‖ speed.

Collection Analytics can be best exemplified by the Redwood Analytics Business Intelligence-Billing

and Collections. The billing and collection software helps to make more proactive and informed

decisions on inventory management by a better comprehension of the billings and collections history.

It helps attorney firms to target and track attorney work effort, client billings and collection trends

along with daily and total inventory balances.

Kappa Image LLC Fraud Detection Software is a single package wherein written analysis is done on

all variable data fields and not only the signature. This helps to prevent fraud and also helps to detect in

case of any committed. It ensures completely automated profile creation and maintenance including

representations of multiple stocks types and writers per account.

In terms of Pricing Analytics ACEIT (Automated Cost Estimating Integrated Tools) has indeed proved

beneficial. It is a premier tool in analyzing, developing, sharing, and reporting cost estimates,

providing a framework to automate key analysis tasks and simplify/standardize the estimating process.

In fact Accenture with its shift from descriptive to predictive analytics have also further attested the

fact that pricing analytics is not only necessary but also indispensable in the current business scenario.

In a world where marketing communications success is driven by the perceived relevance to the target

audience, predictive analytics becomes a key to growing and gaining market share.

Genpact has also allowed the telecommunication companies to drive effectiveness, deliver outstanding

sustainable customer satisfaction through smarter analytics. It helps the telecommunication companies

to eliminate inefficiencies, improve operational performance and thereby profit, be cost effective and

enhance operational excellence through our deep granular telecom process management expertise and

Lean Six Sigma rigor, increase customer loyalty and operational effectiveness through our suite of

smarter telecom analytics solutions and accelerate expansion into developing economies through our

innovative global delivery platform spread across 64 centers in 17 countries.

Supply Chain Analytics helps to combine technology with human efforts to identify trends, perform

comparisons and highlight opportunities in supply chain functions despite huge data being involved. It

helps in decision making in terms of inventory management, manufacturing, quality, sales and

logistics. Tools like OLAP play a major role in this sphere.

Analytic capabilities within a ―Software-as-a-Service‖ (SaaS) transportation management system

(TMS) provides insight into shipping operations by compiling and analyzing value-added data from the

network of shippers throughout the life of your contracts, orders, shipment, transactions, and freight

payment activities, providing access to network benchmarks. Business intelligence capabilities within a

TMS gives the edge needed to accurately manage and analyze the transportation costs and execution

performance against the network to help make better operational decisions. The examples will include

procurement and transportation, delivery performance by carriers and suppliers and tracking key

performance indicators in the freight payment and audit process.

Business Analytics

Product Management

Customer Management

Human Resource

Management

Services/

Operations Management

Enterprise Management

Supplier/

Partner Management

Market/Sales Management

The figure shows how business analytics is intertwined with the high-impact business processes. The

areas where analytics partake in the processes are as follows:

1. Product Management: the impact of analytics are namely in product pricing, product

profitability and the portfolio optimization of the product.

2. Customer Management: the sections taken care of by analytics in terms of customer

management are namely customer segmentation, customer lifetime value, customer loyalty,

customer profitability, and churn as well as customer experience. It helps one to gauge and

comprehend them better.

3. Human Resource Management: analytics help to analyze the behavioral pattern of

employees who may be contemplating a switchover. This analysis when done with respect to

previous data; gives an insight into such employee decisions. It therefore helps to curb attrition

through employee motivation and employee retention measures.

4. Services and Operations Management: herein analytics take care of the capacity

planning/demand forecasting, customer experience, capital expenditure, workforce

effectiveness, performance, and leakage/shortfall.

5. Enterprise Management: analytics ensure better operations in terms of fraud, revenue

assurance, asset utilization, security, collections and advanced forecasting.

6. Supplier and Partner Management: the benefits of analytics extend in the fields of contract

compliance, vendor efficiency and vendor optimization.

7. Market and Sales Management: analytics play a vital role in channel optimization, up-

selling, cross – selling and campaign performance.

The above figure depicts: Analytics Solutions based on Challenges and Constraints

It’s imperative for an organization to align decision making with fact-based inputs, but those facts

should also be collected with some kind of analytical tool. Due to wide availability of those tools in the

market, availability of talent has drastically gone down. So organizations should keep in mind the

business challenges and constraints to the corporate strategy that can help in finding a right fit analytics

solution. To get the right fit, it's essential to look at organization as a whole. Determine the budget

constraints, staffing levels, and resource availability for the analytics efforts. Consider risk tolerance

for making decisions. Develop an understanding of data privacy and regulatory issues regarding data

security.

Business Challenges

Constraints Solutions

Efficiency

Cost

Risk

Budget

Staffing

Infrastructure

Licensing

Risk Tolerance

Urgency

Security

End Users

CRISP-DM, SQL Server, UNIX,

CART, SVM, SOLARIS,

WINDOWS, SAS, S/CMM,

ORACLE, SPSS, REGRESSION,

Experian, Clustering,

RAPIDMINER, Linux

The Competition: Google Analytics (GA) being top in the e-commerce is a free service offered by

Google that generates detailed statistics about the visitors to a website. A premium version is also

available for a fee. The product is aimed at marketers as opposed to webmasters and technologists from

which the industry of web analytics originally grew. It is the most widely used website statistics

service, currently in use on around 55% of the 10,000 most popular websites. Another market share

analysis claims that Google Analytics is used at around 49.95% of the top 1,000,000 websites (as

currently ranked by Alexa).

GA can track visitors from all referrers, including search engines, display advertising, pay-per-click

networks, e-mail marketing and digital collateral such as links within PDF documents. If your site sells

products or services online, you can use Google Analytics e-commerce reporting to track sales activity

and performance. The e-commerce reports show you your site’s transactions, revenue, and many other

commerce-related metrics.

SiteTrail lets you see a quick snapshot of any competitor website at no cost.

Omniture has various enterprise website analytic tools.

InQuira from ORACLE provides an integrated software platform that has three core capabilities:

knowledge base management (including authoring and workflow), natural language search, and

advanced analytics and reporting.

Adometry is the leading provider of ad analytics, delivering actionable insight to improve the

performance of online advertising. Adometry provides scoring, auditing, verification, and fractional

cross-channel attribution metrics to optimize results and improve return. Formerly known as Click

Forensics, Inc., Adometry has been improving online traffic quality for over half a decade.

Survey of Literature:-

The Literature review further helps in understanding the utility and relevance of business analytics’ in

the real world scenario.

1) An analytic capability is especially critical in healthcare because lives are at stake and there is

intense pressure to reduce costs and improve efficiency. We can use antecedents and catalysts

for developing an analytic capability based on an in-depth study of the cardiac surgical

programs.

Ghosh, Biswadip , Scott, Judy E ―Antecedents and Catalysts for Developing a Healthcare

Analytic Capability‖ Communications of AIS; 2011, Vol. 2011 Issue 29, p395-410.

2) It is imperative that rather than having the right tools, technology and people, organizational

factors is one of the most important predictors of the ability to create competitive advantage.

Data-oriented organizational cultures have three key characteristics: (1) analytics is used as a

strategic asset, (2) management supports analytics throughout the organizations and (3) insights

are widely available to those who need them.

KIRON, DAVID, SHOCKLEY and REBECCA ―Creating Business Value Analytics‖ MIT

Sloan Management Review; Fall2011, Vol. 53 Issue 1, p57-63, 7p.

3) Business analytics turns traditional retail experience from pushing products to empowering and

pulling customers on products based from their buying activity. The analytics require continual

update of consumer’s data to better know their spending habits and limits. Experts says that

organizations will need to have clear objectives or identifying how they will harness the

analytics to their business problems and make sure that their service delivers consumers'

expectation. Benefits for using social media like Facebook to gather consumer’s response and

analyze their sentiments regarding a company or its brands.

Hodge, Neil: ―Harnessing analytics‖ Financial Management (14719185); Sep2011, p26-29, 4p.

4) Business users, while expert in their particular areas, are still unlikely to be expert in data

analysis and statistics. To make decisions based on the data collected by and about their

organizations, they must either rely on data analysts to extract information from the data or

employ analytic applications that blend data analysis technologies with task-specific

knowledge. Analytic applications incorporate not only a variety of data mining techniques but

provide recommendations to business users as to how to best analyze the data and present the

extracted information. Unfortunately, the gap between relevant analytics and users' strategic

business needs is significant. The gap is characterized by several challenges like cycle time,

analytic time and expertise, business goals and metrics and goals for data collection and

transformations.

Kohavi, Ron, Rothleder, Neal J &Simoudis, Evangelos ―EMERGING TRENDS IN BUSINESS

ANALYTICS‖ Communications of the ACM; Aug2002, Vol. 45 Issue 8, p45-48, 4p.

5) Analysis of consumer-related and consumer-generated data is a very important way to measure

the success of on-line retailing. The software packages for data analysis have two major

shortcomings: (1) solutions are not offered as a service reachable by standard procedures over

the Internet, but as isolated standalone applications or ERP system modules; (2) privacy

restrictions need to be integrated into a framework of business analytics for Web retailers. The

first aspect can be addressed with standardized developer software for Web services, but the

second must consider privacy legislation, privacy specifications on Web sites (P3P), and data re

identification problems.

Berendt, Bettina, Preinbusch, Sören, Teltzrow, Maximilian: ―A Privacy-Protecting Business-

Analytics Service for On-Line Transactions‖ International Journal of Electronic Commerce;

Spring2008, Vol. 12 Issue 3, p115-150, 36p.

6) HR analytics' benefits and strategic value to business, pointing out the wrong notions about the

concept, and explaining the proper way to execute the process to achieve maximum value.

Mondare, Scott, Douthitt, Shane, Carson, Marisa: ―Maximizing the Impact and Effectiveness of

HR Analytics to Drive Business Outcomes‖ People & Strategy; 2011, Vol. 34 Issue 2, p20-27,

8p.

7) Web analytics as a process for making better decisions in business as well as notes the essential

role of the web analyst in translating information into relevant key performance indicators

(KPI).

Stoller, Jacob: ―Not just for techies anymore Web analytics goes mainstream‖ CMA Magazine

(1926-4550); May2012, Vol. 86 Issue 3, p18-19, 2p.

8) Managers have used business analytics to inform their decision making for years. And while

few companies would qualify as being what management innovation and strategy expert

Thomas H. Davenport has dubbed 'analytic competitors,' more and more businesses are moving

in that direction. Which best practices do the most experienced project managers involved in

business analytics projects employ, and how would they advise their less experienced peers?

The authors found that the most important qualities could be sorted into five areas: having a

delivery orientation and a bias towards execution; seeing value in use and value of learning;

working to gain commitment; relying on intelligent experimentation; and promoting smart use

of information technology. Although many of the business analytics project managers the

authors interviewed report to the IT department, they identify with the business side of their

organizations. Best-in-class CIOs realize that IT and business can't afford to continue to be at

loggerheads with one another. IT should pursue opportunities to deliver faster implementation

cycles, maintaining just enough process and architectural hygiene to ensure quality and

professional support.

VIAENE, STIJN,DEN BUNDER, ANNABEL VAN: ―The Secrets to Managing Business

Analytics Projects‖ MIT Sloan Management Review; Fall2011, Vol. 53 Issue 1, p65-69, 5p.

9) Chief information officer (CIO) FilippoPasserini at the Procter and Gamble says that he has

created the Decision Cockpits, the illustration of the business conditions for making faster

business decisions. Passerini believes that he faced difficulty in implementing the business

tools due to culture change. He notes that he is expanding business intelligence where there is

competition.

Watson, Brian P: ―How P&G Maximizes Business Analytics‖ CIO Insight; Jan2012, Issue 121,

p18-20, 3p.

10) The article offers the author's insights on predictive analytics. The author states that business

enterprises draw generalizations from analyzed data in predictive or business analytics to adjust

business strategy and customer experiences. He mentions that the practice of predictive

analytics is more beneficial to small companies than large firms.

Kirchner, Matthew: ―Predictive Analytics‖ Products Finishing; Mar2012, Vol. 76 Issue 6, p52-

53, 2p.

11) The article explores the potential of automated web analytics for deriving business intelligence

(BI). BI is defined as the ability to apprehend the links of facts to guide action towards an aim.

It interprets data and transforms it into insights that can be used to guide strategy formulation.

The common elements for effective measures and outcomes using online analytical tools are

also discussed, including dashboard usage and customer relationship management.

Bhatnagar, Alka: ―Web Analytics for Business Intelligence‖; Online; Nov/Dec2009, Vol. 33

Issue 6, p32-35, 4p.

12) Probability can augment the application of predictive analytics. Businesses have used predictive

analytics to prevent losses that may result from fraud, operational errors, or low productivity.

Analysts convey that business predictions should also be supported with probabilities and an

awareness of various reactions to probabilities. This article explains how actions for using

predictive models can be supported by probability in real case decisions such as customer

lifetime value (CLV), clinical treatment, and churn management.

McKnight, William; ―PREDICTIVE ANALYTICS: BEYOND THE PREDICTIONS‖;

Information Management (1521-2912); Jul/Aug2011, Vol. 21 Issue 4, p18-20, 3p.

13) The article discusses how big data changes the way organizations use business intelligence and

analytics. It states that big data has characteristics that add to the challenge including high

velocity, high volume and a variety of data structures. Early adopters of big data include

scientific communities with access to expensive supercomputing environments which aimed to

analyze massive data sources. An exciting source of big data is said to be social network data

which companies would like to leverage. The article discusses an open source framework

created by Doug Cutting called Hadoop that has become the technology of choice to support

applications supporting petabyte-sized analytics utilizing large numbers of computing nodes.

Rogers, Shawn; ―BIG DATA is Scaling BI and Analytics‖ ; Information Management (1521-

2912); Sep/Oct2011, Vol. 21 Issue 5, p14-18, 5p.

14) Visual analytics (VA)—the fusion of analytical reasoning and computational data analysis with

rich, interactive visual representations—promises to provide many relevant techniques for

business-ecosystem-intelligence systems. However, the effectiveness of such systems requires

the careful vigilance of complex, heterogeneous, and distributed data; an in-depth

understanding of the business ecosystem context and end-user domain; and the corresponding

design of relevant visualizations and metrics.

Basole, Rahul C, Hu, Mengdie; ―Visual Analytics for Converging-Business-Ecosystem

Intelligence‖; IEEE Computer Graphics & Applications; Jan2012, Vol. 32 Issue 1, p92-96, 0p.

15) About the opportunities and challenges faced by business analytics in India. Issues that were

discussed including infrastructure and manpower needs for India, user needs in business

analytics and technological challenges associated with integrating data from multiple sources;

Challenges in the field of analytics in financial services in India.

Murthy, Ishwar; ―Business Analytics in India -- Opportunities and Challenges: Discussion‖;

IIMB Management Review (Indian Institute of Management Bangalore); Jun2006, Vol. 18

Issue 2, p175-191, 17p.

16) The paper investigates the relationship between analytical capabilities in the plan, source, make

and deliver area of the supply chain and its performance using information system support and

business process orientation as moderators. The findings suggest the existence of a statistically

significant relationship between analytical capabilities and performance. The moderation effect

of information systems support is considerably stronger than the effect of business process

orientation. The results provide a better understanding of the areas where the impact of business

analytics may be the strongest.

Trkman, Peter, McCormack, Kevin; ―The impact of business analytics on supply chain

performance‖ ; Decision Support Systems; Jun2010, Vol. 49 Issue 3, p318-327, 10p.

17) The article explains deep analytics and the role of tools and technologies in predictive analytics

and modeling. It defines business analytics as the skills, technologies, applications and

practices for continuous, iterative exploration and investigation of previous business

performance in order to obtain insight as well as drive business strategy. Investment in more

advanced analytics technology solutions is said to be prompted by the need to remain

competitive. The core principles that support an effective implementation of deep analytics

technologies are discussed including signal detection and visualization. It emphasizes the need

to promote high quality information across the enterprise.

GRIFFIN, JANE; ―Deep Analytics: What is it, and how do I do it?‖Information Management

(1521-2912); Sep/Oct2010, Vol. 20 Issue 5, p53-54, 2p

18) Good Data Won’t Guarantee Good Decisions: by Shvetank Shah, Andrew Horne, and Jaime

Capellá.

19) The Dark Side of Customer Analytics: by Thomas H. Davenport and Jeanne G. Harris

Relevance/Usefulness:-

The relevance of business analytics lies in the very fact that innovation is the mother of differentiation,

and it is the differentiation that provides the cutting edge in this era of survival of the fittest. The above

examples amply prove the fact beyond a shadow of doubt that it is not a mere coincidence that business

analytics has become the be all and end all of efficient and speedy operations irrespective of its field.

Real-time dashboards to monitor every detail and highlight areas that require immediate attention are

but one of the miracles that business analytics is performing. With wafer-thin margin of two to three

percent cost effectiveness has become a rule to live by for all operating in the market, the supply chain

analytics help managers to understand key issues in the field of :

Correctly analyzing barriers to market entry, which vary widely from product to product

Responding to competition within a well-defined supply tier structure

Dealing with high threat of product substitutes

Continually driving product innovation

Managing product life cycles to maximize returns

By leveraging the power of technology even fraud detection can turn out to be a proactive process

allowing organizations to detect potential frauds thereby reduce the negative impact of significant

losses owing to fraud.

Use of business analytics in billing and collection can help in enabling the analytical skills across

businesses in the most contemporary fashion; help to automatically update data at regular intervals as

per requirement. These tools are also subject to customization providing functionalities specifically

useful to the concerned organization. The relevance of the financial analytics is even more prominent

when the example of Oracle is taken into account. The benefits rendered are:

Payables: assess cash management and monitor operational effectiveness of the payables

department to ensure lowest transaction costs.

Receivables: Monitor DSOs and cash cycles to manage working capital, manage collections,

and control receivables risk

General ledger: Manage financial performance across locations, customers, products, and

territories, and receive real-time alerts on events that may impact financial condition

Profitability: Identify most profitable customers, products, and channels and understand

profitability drivers across regions, divisions, and profit centers

Retail analytics came into prominence and relevance owing to the fact that the current business focus

has shifted from mass marketing to target marketing. Target marketing requires slicing the potential

market into segments. It helps businesses to promote the right product or service to the right segment

of customers; thereby saving costs pertaining to efforts and space of targeting the customers who may

never be interested in buying the product. This requires effective customer intelligence and actions in

alliance with the same. This is performed by the retail analytics.

The SAP CRM tool will help to plan market financing, market campaigning, target group optimization.

It will also ensure campaign monitoring and success analysis, advertising plan evaluation, lead analysis

and external record evaluation.

All these put together will create an invincible edge beyond a shadow of doubt that will not only help

create business but also retain customers and sustain business in the competitive market scenario.

Data/Method Analysis:-

In order analyze the power of analytics we have collected data from National Institute of Diabetes and

Digestive and Kidney Diseases, a data set of Diabetic patients which can be used for various analysis.

We have downloaded the ARFF (Attribute relation file format) ―diabetes.arff‖ and used WEKA 3.7 as

a mining tool. After feeding the data to Classification and clustering algorithms we got the outputs

which we will observe with the screen shots. Before we move into analysis, let us understand the basic

components of the file diabetes.arff.

Number of Instances: 768

Number of Attributes: 8 plus class

For Each Attribute: (all numeric-valued)

1. Number of times pregnant (preg)

2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test (plas)

3. Diastolic blood pressure (mm Hg) (pres)

4. Triceps skin fold thickness (mm) (skin)

5. 2-Hour serum insulin (mu U/ml) (insu)

6. Body mass index (weight in kg/ (height in m) ^2) (mass)

7. Diabetes pedigree function (pedi)

8. Age (years) (age)

9. Class variable (0 or 1) (class- 1 means tested positive, 2- means tested negative)

Missing Attribute Values: None

Doctors were fairly certain that diabetes does not cause "number of times pregnant," age, and

―diabetes pedigree function" (heredity). But still there is need for more in depth analysis for

root cause.

The "plasma glucose concentration" and the "serum insulin" measurements are both tests for

diabetes, so they have been included.

An interesting part of the dataset is that it has two measures related to being overweight:

"triceps skin fold thickness" and "body mass index." These measurements don't cause you to be

overweight, rather being overweight causes these measurements to be high. Unfortunately, this

makes "overweight" a hidden variable in the network. After further examination, skin fold

thickness looked like very poor evidence for diabetes, so they used body mass index as the

value of overweight.

Analysis:-

1) We fed the diabetes.arff file into WEKA 3.7 and applied the Classification algorithm OneR to

it, and it gave a following output.

Now there are 182 incorrectly classified instances, which gave an error rate of 23.7%. At the

bottom of the window is ―Confusion Matrix‖. The rows in this matrix correspond to the correct

classes (a = does not have diabetes; b = has diabetes). Hence, there are a total of 447 + 53 = 500

patients without diabetes in the test data, and 129 + 139 = 268 patients with diabetes. The

columns correspond to the predicted classes. Hence, 447 of the 500 negative patients were

correctly classified as negative and 53 of them were incorrectly classified as positives (called

"false positives"). This gives a false positive rate of 0.48. Conversely, 129 of the 268 positive

patients were falsely classified as negatives (called "false negatives") and 139 were correctly

classified as positives.

2) Now to improve the correctly classified instances we have fed the data set to another algorithm

called J48. It can be observed that the correctly and incorrectly classified instances have

improved by application of this algorithm. We can analyze the output in similar way as we did

in the previous one.

3) Similarly we can apply Clustering algorithm SimpleKmeans to analyze the clusters for tested

negative and tested positive people. Those who are more prone to diabetes are having relation

between the attributes. A visualized graph is attached so that we can estimate relation between

insulin level and Age.

4) Above output of the data set can be utilized by Doctors and pharmacists to determine the main

root causes of diabetes and the derived problems which arouses due to diabetes. The data set

can be analyzed with more number of mining algorithms with analytics involved for new

findings. It can not only provide insights for cure, also can led to new areas which can be

considered while treatment of a diabetic patient. 5) Not only Hospitals, Pharmaceutical Companies who are dealing with Sugar supplements, E.g.

Sugar Free etc. can utilize this data and redefine their products and improve the value

proposition for their target group.

Conclusions\Recommendations:-

The future potential being:

Business analytics is broad enough to include capabilities and solutions that benefit a variety of

disciplines. Interestingly, it is observed that business analytics is not just primarily an IT or business

function, but is a function of both IT and business. With this approach, there is an increased need for

collaboration across organizations on issues relating to business analytics, as well as the need for cross

departmental management teams for oversight.

From the study now it is clear how Analytics is imperative for sustaining and differentiating in the

generation next technology. We have come up with some recommendations after the study which is as

follows:-

1) Organizations should transform into learning organization and imbibe Analytics into the

employees rather than searching for new talents in the market. Train every member to

fit into best analytical practices in order to align their goals and objectives with that of

the organization.

2) Provide better practices to fresh minds from technical/Business schools by means of

internships or corporate lectures so that they can provide better insights in the new era

of Analytics.

3) Develop Analytics oriented strategies at strategic, tactical and operational levels.

4) Whatever business you are be it product or services; understand your customer better

for competitive advantage with better analytical tools. Develop a value chain that must

be superior to competitors. This in return will create superior customer lifetime value

(CLV).

5) Implement HR analytics and Identify the resources who can take Analysis based data

oriented decisions.

6) Trans-creativity and Innovation in Analytics is the demand of the hour. There is a vast

opportunity of predictive analytics in India due the diversity in demography, consumer

behavior, and regional preferences.

7) Develop Analytics based Innovative business models for sustaining and differentiating

because business model contains the core competencies. Improving capabilities is

another option but they can be copied easily. The bar for entry level barriers can be

raised with the help of analytics.

8) Not only corporations, Economies and Industries can also implement Analytics to

forecast economic activities that can sustain growth and development.

9) Cost based optimized Analytics can contribute to both Top and Bottom lines of

business.

10) In Technology trends Analytics goes at par with cloud computing, organizations can

sort out solutions to so many kinds of problems, for which often they don’t have any

answer.

To quote Benjamin Franklin ―An investment in knowledge pays the best interest‖. It therefore becomes

mandatory for every manager to have a clear understanding and firm grip over business analytics. This

further vindicates Peter Drucker’s thought that a manager is responsible for the application and

performance of knowledge.

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