1. Business Intelligence

2. Concepts
BI is the processes, technologies and tools that helps to convert data to information, information into knowledge and into plans that guides organizations and helps in taking effective business decisions
It tells the organizations
What happened?
Why did it happen?
What is happening?
What will happen?
What do I want to happen?

3. Characteristics of BI Single point of access to information
Timely answers to business queries
Applicable to business departments
Accurate and up-to-date information
No room for guess work
Helps managers to do timely adjustments as per the information available

4. Key stages of BI Data Sourcing Data Analysis Situation Awareness
Risk Analysis
Decision Support

5. Common Modules in BI
Dashboards – customized snapshots of daily operations
KPI’s – tracking of performance indicators
Graphical OLAP – find , filter and analyze data
Forecasting – time-series of historical data
Graphical Reports – performance metrics to one and all

6. BI Applications Changing trends in market share
Changing customer behavior
Changing customer spending patterns
Changing customers preferences
Company capabilities
Changing market conditions

7. BI Conceptual framework
SOURCE DATA
Vertical LOB
ERP
E-Commerce
CRM
Financials
External
Third Party
ETL- Extractions,
Transformations ,
Loading
Hetero - RDBMS
Physical data structures to
optimize queries
End-user-tools
OLAP
Static Reports
Dynamic Reports
Balanced Scorecards
Budgeting
Forecasting
Data mining
Exceptions
Notifications
Process input
Data Warehouse MD
Cube

8. ETL Extraction Consolidation of data from various sources
Conversion of data for further processing
Sources can be text files, excel data, access data, mainframe data etc.
Transformation
Application of rules and functions to the extracted data
Data cleansing, de-duplication, encoding, merging, grouping and summarizing
Loading
Load transformed data into DW (RDBMS)
ETL tool – Microsoft’s Data Transformation Services (DTS) part of SQL Server 2000 where data is consolidated into single RDBMS called DW

9. Multidimensional cube
Complex, efficient, proprietary data structure that includes data and data aggregations and access control.
Works at very high speeds and may several gigabytes in size.
Data within cubes are compressed for physical storage requirements.
Helps in optimizing queries

10. End-user tools
OLAP - Railway reservation system, Online inventory management
Static /Dynamic Reports – Amul, HLL, ITC
Balanced Scorecards -
Budgeting - Ispat
Forecasting - models
Data mining - patterns
Exceptions/notification
Process inputs - triggers

11. Predictive Analysis
Encompasses variety of techniques from statistics, modeling, data mining that analyze current and historical facts to make predictions about future
Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities
PA finds applications in marketing, finance, pharma, retail, travel, healthcare etc.
Credit scoring is a popular example of PA where prediction on future credit payments for a customer is done based on data
The predictive model is built upon the organizations collective experience by leveraging existing logs of customer purchases, behavior and demographics.

12. Types of Analytics Descriptive Analytics Predictive Analytics
Gain insight from historical data
Predictive Analytics
Predictive modelling using statistical tools
Prescriptive Analytics
Recommend decisions using optimization/ simulation
Decisive Analytics
Decision making through visual analytics
Video Analytics
Eg. Retail, Defence & Security surveillance

13. Data Visualization Information visualization through graphics
Study of visual representation of data, meaning information that has been abstracted in some schematic form including attributes or variables for the units of information
Data visualization gives overview of mindmaps (A mind map is a diagram used to visually outline information. A mind map is often created around a single word or text, placed in the center, to which associated ideas, words and concepts are added.), displaying news, data, connections, websites, articles, resources, tools and services.

14. Geographical Information System (GIS)
Traditionally Land use and its optimization
Location intelligence
Where to put up a store
How to market to changing landscape of customers ?
How to optimize the distribution channels ?
How to compare stores performance ?
GIS map reports through Dashboards interface or web service.

15. Real-time Business Intelligence (RTBI)
Decisions based on current and evolving data
When processing moves from classical data warehousing into the realm of real-time then it results in RTBI
RTBI is event-driven business intelligence
On occurrence of an event the results are reflected instantaneously and not minutes or hours later.
Major impediments of RTBI – Latency & Unavailability
Data Latency refers to staleness of data
Data unavailability will bring system to a halt

16. RTBI Applications
Blood bank system for rare blood group operates on the principle of RTBI where the blood banks either stocks or provides information of its availability from other sources in case of stock outs.
Customer pre-authorization for specified amounts via cell phone to ensure purchase is covered through their credit/debit cards.
Fraud Detection for ATM operations

17. Business Performance Measures
Dashboards
Scorecards
Real time Information (hours or minutes)
Used when results are required at point of action
Displays performance
Meant for performance monitoring
Real time feeds
Mostly used at operational level
Example – Total number of complaints in queue
Information on longer time periods (weeks/months)
Used for summarized results
Display progress
Meant for performance management
Snapshots at chosen time interval
Mostly used by top mgt.
Example- No. Of complaints resolved out of no. Of complaints recd in a month