Business Analytics and Data Visualization

Business Analytics • Broad category of applications and techniques

• Help enterprise users to make better business and strategic decisions

• Also known as analytical processing, BI tool, BI applications or just BI.

• Is becoming a major tool for most medium and large corporations.

• E.g. Pizza Hut has significantly boosted it’s sales revenue by using BI tools.

Tools and techniques of BA

Vendors Classification1.MicroStrategy’s classification of BA: The five

styles of BI

• Enterprise reporting

• Cube analysis

• Ad hoc querying and analysis

• Statistical analysis and data mining

• Report delivery and alerting

2.SAP’S classification of strategic Enterprise Management

• Operational level-supports transaction processing on the operational level.

• Managerial level-Managers can use SAP R/3 to access all reports, make queries and drill down

• Strategic level-company offers products under title SAP SEM, which includes BA.

Capabilities of EIS/ESS

• Drill down

• Critical success factors

• Key performance

• Status report

• Trend analysis

• Ad hoc analysis

• Exception reporting

• Ability to go to additional details

• Can be organizational, industry, departmental etc.

• Specific measure of each CSF

• Latest data available on KPI

• Short, medium and long term trend of KPI

• At any time, with any desired factor

• Using reports that highlight deviations larger than certain thresholds.

Capability Description

Online Analytical Processing(OLAP)• Activities performed by end users in online systems

– Specific, open-ended query generation• SQL

– Ad hoc reports– Statistical analysis– Building DSS applications

• Modeling and visualization capabilities• Special class of tools

– DSS/BI/BA front ends– Data access front ends– Database front ends– Visual information access systems

Types of OLAP

• Multidimensional OLAP:OLAP implemented through multidimensional database

• Relational OLAP: is implemented on the top of relational database

• Database OLAP and Web OLAP: Database OLAP-RDBMES designed to host OLAP structure.

Web OLAP-OLAP data accessible from a web browser

• Desk OLAP: Performs local multidimensional analysis

Characteristics of OLAP

• Categorical Analysis: Based on historical data

• Exegetical Analysis: Based on historical data. Adds the capability of drill-down analysis.

• Contemplative Analysis: allow user to change a single value to determine it’s impact.

• Formulaic Analysis: permits change to multiple changes.

Benefits of OLAP

• Multidimensional conceptual view for formulating queries.

• Transparency to the user.

• Easy accessibility: Batch and online access

• Consistent reporting performance

• Client/Server architecture: the use of distribution resources

• Generic dimensionality

Continue……

• Dynamic sparse matrix handling.

• Multi user support rather than support for only a single user.

• Unrestricted cross-dimensional operations.

• Intuitive data manipulation

• Flexible reporting

• Unlimited dimensions and aggregation level.

Reports and QueriesReports

• Must be uniform, flexible, adjustable

• Two types:

1.Routine Reports-• Generated automatically and send periodically

• E.g. weekly sales figures, units produced each day, Monthly hours worked.

2.Ad Hoc Reports-• Created for specific user whenever needed

• For different time intervals or for only a subset of the data

Queries

Ad Hoc Queries

• Query cannot be determine prior to the query is issued.

• Allow user to request information from computer which not include in reports.

• To generate new queries or modify old ones.

• Queries can be done on static data or dynamic data.

Multidimensionality• Efficient way to organized raw and summery data

for analysis and presentation.

• It enables data to be organized the way individual managers.

• Factor considered

1.Dimensions:like products, salespeople, market segment

2.Measures: like money, sales volume, inventory

3.Time: are daily, weekly, monthly, quarterly and

yearly

Multidimensional data cubes

• Used to represent data along some measure of interest.

• It can be two dimensional, three dimensional or higher-dimensional.

• Provide an opportunity to retrieve decision support information in an efficient manner.

• Cube analysis: Allow to perform queries by flipping through a series of report views.

Limitations of Dimensionality

• More computer storage.

• Multidimensional products cost significantly more.

• Database loading consumes significant system resources and time.

• Complex interfaces and maintenance

Advance Business Analytics• Data Mining:

– Statistical methods– Decision trees– Case based reasoning– Neural computing– Intelligent agents– Genetic algorithms

• Predictive Analysis:– Helps to determine the probable future outcome for an

event– Identify relationships and patterns

Data Visualization• Technologies supporting visualization and

interpretation

– Digital imaging, GIS, GUI, tables, multi-dimensions, graphs, VR, 3D, animation

– Identify relationships and trends

• Data manipulation allows real time look at performance data.

Visualization Spreadsheets• The major end user tools for programming

decision support applications.

• Widely adopted as an easy-to-use and powerful tool for free-form data manipulation.

• Sophisticated and flexible tool for collecting, analyzing and summarizing data from multiple sources.

• Power of Excel can be leveraged with visualization including enhancing effectiveness, focusing communications, facilitating comprehension, and empowering collaboration.

Geographic Information System(GIS)

• Computerized system for managing and manipulating data with digitized maps– Geographically oriented

– Geographic spreadsheet for models

– Software allows web access to maps

– Used for modeling and simulations

– Sophisticated and affordable

– Provide framework to support the process of decision making and designing alternative strategies.

GIS And Decision Making

• Provide extremely useful information in decision making.

• Graphical format easy to visualize the data.• Countless applications to improve decision

making like:1.The dispatch of emergency vehicle2.Transit management3.Facility site selection4.Drought risk management5. Wildlife management

Real-Time Business Intelligence

• Increasingly demand to access real-time, unstructured, or remote data, integrated with data warehouse.

• Real time data updates and access are critical for an organization’s success and survivals

• Need frequent updating of data warehouse

• Real-time requirement change the view of database, data warehouse, OLAP and data mining tool

Automated Decision Support(ADS)

• Ruled-based system provide solutions to repetitive managerial problems.

• Rapidly builds rules-based applications to automate or guide decision making

• Injects predictive analytics into rules-based applications

• Combines business rules, predictive models, and optimization strategies

• Accelerates the uptake of learning from decision criteria into strategy design, execution, and refinement.

ADS Applications• Product or service configuration

• Yield(price) optimization

• Routing or segmentation decisions

• Corporate and regulatory compliance

• Fraud detection

• Dynamic forecasting

• Operational control

Implementing ADS

Software companies provide the following

components to ADS:

• Rule engines

• Mathematical and statistical algorithms

• Industry-specific packages

• Enterprise systems

• Workflow applications

Competitive Intelligence

• Monitoring the activities of their competitors to acquire competitive intelligence.

• Drives business performance by increasing market knowledge, rising the quality of strategic planning.

• Can be facilitated with technologies such as optical character recognition, intelligent agents and internet

• Internet: Important tool in supporting competitive intelligence.

Web Analytics

• Application of BA to websites

• Includes e-commerce

• Tools and methods are highly visualsClickstream analysis: Analysis of data present inside the web environment Provide trailer of user’s activities and shows the user’s browsing patterns. By analyzing data one can find the effectiveness of promotions Can determine which products and ads attract the most attention.