This document provides an overview of business intelligence and analytics (BI&A). It discusses the objectives and definition of BI&A, as well as key components like business requirements, data warehousing, data analysis, and business analytics. It also provides a historical perspective on analytics and discusses maturity models. Examples are given of how Netflix, Moneyball, and Capital One have successfully used analytics for competitive advantage.

1. INTRODUCTION
Dr. Sunil D. Lakdawala
Sunil_lakdawala@hotmail.com

2. BI&A Overview
Contents
 Objectives of BI&A
 What is BI&A
 BI&A
 BI Requirements
 Data Warehouse Architecture
 Data Analysis
 Business Analytics
 Historical Perspective
 Maturity Models
 Competing on Analytics

3. Business Analytics
BI&A Overview
Business Analytics
Requirement
DW
Report
Forecasting
Bench Marking
BPR

4. Data Science
BI&A Overview
Data Science
Technical Skills

5. Objectives of BI&A
 Help Management taking better decision based
on Data
 Facilitate closing the gap between the current
performance of an organization and its desired
performance
BI&A Overview

6. What is BI&A
 Decision Aid System
 Not an automated decision making system
 Domain knowledge and skills are must
BI&A Overview

7. BI&A - BI Requirements
 BI Requirement vs Operations Requirement
 BI Requirements through KRA / KPI (Metrics)
 Score Card and Dash Board
BI&A Overview

8. BI&A Overview
BI&A : Data Warehouse
Data Warehouse
 Identify Data Required (To satisfy
Requirements)
 Identify Data Source
 Designing DW Schema suitable for Data
Analytics
 Extracting & transforming data from those
sources and loading into DW / DM Schema

9. Data Warehouse Architecture 9
Data Warehouse Architecture
OLTP 1
RDBMS
OLTP 3
ERP
OLTP 2
VSAM
Data
Warehouse/
Data Mart
Staging
Area
Cube II
Cube I
OLAP
Tool –
Slicing
/Dicing
Query /
Reporting
Tool
ETL

10. BI&A Overview
BI&A : Data Analysis
Exploratory Techniques
 Descriptive Statistics
 Data Visualization
Detailed Techniques
Descriptive
 Management Information System (MIS)
 On Line Analytic Processing (OLAP)
Predictive, Prescriptive, Autonomous
 Data Mining, Machine Learning, Big Data Analytics
 Forecasting
 Hypothesis Testing
 ORMS (Operations Research Management
System)
 What IF Analysis / Scenario Building

11. Data Analysis to Business Analytics
Where Data Analysis is about generating insight from data
driven processes, business analytics is about leveraging
analytics to create measurable, tangible value
Data Analysis
 Data  (Information ) Knowledge
Business Analytics
 Knowledge  Wisdom (application of knowledge)
BI&A Overview

12. Business Analytics
 Identify Business Process
 Identify Relevant Metrics and Measure (Bench Mark)
 Identify Data Analysis techniques that can be applied
 Carry out BPR (Business Process Reengineering)
 Measure Relevant Metrics
BI&A Overview

13. Historical Perspective
 Analytics 1.0 (till 2005)
 Heavy on Descriptive Analytics (Reporting and OLAP)
 Light on Predictive and Prescriptive Analytics (Data Mining,
Forecasting)
 Managers and Analysts did not have strong relationship
 Analytics 2.0 (2006 -2010)
 Big data and Big data analytics
 Also heavy on Predictive and Prescriptive Analytics (Data Mining,
Forecasting)
 Text Mining, Sentiment Analysis, Social network analysis, etc.
 Stronger relationship between Mangers and Analysts (now
known as Data Scientists)

BI&A Overview

14. Historical Perspective (Cont)
 Analytics 3.0 (2010 – 2016)
 Analytics getting integrated with production processes and
systems
 Analytics 4.0 (2016 onwards)
 Autonomous Analytics, limiting human role
 Machine Learning (not only data creates model, but also learns
and adapts from data). (e.g. Neural Network)
 AI, Cognitive Techniques, Deep Learning are other terminologies
used
BI&A Overview

15. Maturity Models : Five Stages
 Stage 1: Analytically Impaired
 Stage 2: Localized Analytics
 Stage 3: Analytical Aspirations
 Stage 4: Analytical Companies
 Stage 5: Analytical Competitors

16. Competing on
Analytics
by
Davenport et al

17. Contents
 Overview
 Case - Netflix
 Case – MoneyBall
 Case – Capital One

18. Overview
 Many of the factors (e.g. when to reorder, optimizing
supply chain, etc. ) are becoming hygiene factors
 One of the major factor based on which tomorrow’s
organizations will compete are on Analytics
 Determine the critical external and internal business
processes
 Identify applications of business analytics that are
strategic and describe the competitive advantage these
would give to the enterprise
 Identify relevant metrics, data sources, what type/kind of
data maybe required, which are the tools which can
handle this data

19. Netflix
 https://www.netflix.com/global
 One of the business: Video Rental online
 Free shipping, fixed monthly rent, no limit on movies
ordered
 Cinematch – Movie Recommendation Engine
 Billions of rating by customers captured
 Create clusters of movies based on customer rankings
and determine customer’s cluster
 Creates customized webpage for each customer
 Recommendation to help customer as well as optimize
inventory (i.e. recommendation includes movie not in
good demand but of customer liking)
 Throttling: Give shipping preference to infrequent
customers (they are most profitable

20. Moneyball
 http://en.wikipedia.org/wiki/Moneyball
 The Art of Winning an Unfair Game Book by Michael
Lewis, published in 2003, about the Oakland Athletics
baseball team and its general manager Billy Beane
 Its focus is the team's analytical, evidence-based,
sabermetric approach to assembling a competitive
baseball team, despite Oakland's disadvantaged
revenue situation.
 Analytics used in player selection
 Different metrics used: Instead of RBI (Runs Batted In),
they used “On-base percentage” and “On-base
percentage slugging Percentage”
 Results: Consistently making in playoffs

21. Moneyball (Cont)
 Baseball statistics are available
 Boston Red Sox also followed Moneyball
 Hired analytics person (underpaid. Analytics not
appreciated much!)
 Not won for 86 years
 Made in American League Championship Series in 2003
 Lost in final with New York Yankees. Why? Statistics told
that pitcher does not perform well after 7 innings or 105
pitches. He was continued to pitch (Manager did not
believe in analytics at heart!. He was fired)
 Boston Red Sox won in 2004

22. Moneyball (Cont)
 Analytics approach applies in various sports (e.g.
football), off the field as well as on field
 It is copied. Hence to stay ahead, need to continue
innovation

23. Capital One
 http://en.wikipedia.org/wiki/Capital_One
 https://www.capitalone.com/
 In 1980, two financial services consultants, Richard
Fairbank and Nigel Morris, identifies major problem in
credit card industry and potential solution
 Problem: Lack of focus on individual customer
 Solution: Technology driven analytics. It will allow to
discover, target and serve most profitable customers and
leave out less profitable customers
 Only Virginia based Signet Bank hired them. (Signet
bank was minor player in credit card business)
 Analytics told that (against then prevailing intuition) that
most profitable customers borrowed large amounts
quickly and paid off the balances slowly

24. Capital One (Cont)
 Created industry’s first balance-transfer card, targeting
debtors as valued customers
 Huge success. Ultimately credit card division was spun
off as a separate company called Capital One
 It has continued the innovation
 It has become Fortune 200 company. Share price
increased 10 times in last decade