1.
Big Data & Business Analytics:
Building the Capability
Prof. Bala Iyer
@BalaIyer
March 04, 2014
1

2.
Agenda
 Big Data Context
 Big Data for business
 Building the capability
 Questions to ask
 Ecosystem Analysis
 Recommendations
2

3.
―we now uncover as much data in
48 hours – 1.8 zettabytes (that's
1,800,000,000,000,000,000,000
bytes) – as humans gathered from
"the dawn of civilization to the year
2003."
3

4.
Categories
 People
 Machine
 Social
 Transactional
4

5.
What do we mean by
―Analytical‖?
 Analytical Decision-making: the use of
data, analysis, models & systematic
reasoning to make decisions
 Questions to answer:



What decisions or business areas should
analytics be applied?
What kind of data do we have now & do we
need?
What kinds of analysis do we do?
Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from
Analytics at Work: Smarter Decisions, Better Results, 2010.
5

6.
Source:
6

7.
Environment
Decisions
Your
Data
Models
Decisions
7

8.
8

9.
Virtual Business Environment
Domain
Resources
Programs
Model-base
-
Database
Schema
R
e
s
o
u
r
c
e
s
M
a
n
a
g
e
r
Engine manager
Dialogue manger
Business
Context
Engine
Metaphors
Visualization
Interactive
decision
making
Target
Layered
Knowledge base
Cache
Work
processes
9

10.
Stakeholders
Owner
Resources/Policies
Modeler
Data Scientist
Business User
User requirements
And available services
User requirements
experiments
Requests
Sourcing data
& models
Development
Platforms
Analytics Capability
Models
Models/Data
Validated models/
insights
Validated
Models
Decisions
10

11.
Data Scientist
 A data scientist is an engineer who employs the
scientific method and applies data-discovery tools to
find new insights in data. The scientific method—the
formulation of a hypothesis, the testing, the careful
design of experiments, the verification by others—is
something they take from their knowledge of
statistics and their training in scientific disciplines.
Data Scientists: The Definition of Sexy, Forbes 2013 link
11

12.
Competencies or Stack
Change Management
Insights
(Experimentation/Visualization)
Domain Knowledge
(best practices)
Model Building
(tools and techniques)
Infrastructure
(Data, Models/architecture)
T
O
O
L
S
12

13.
Obstacles
 Shortage of data scientists
 Huge technical challenges
 Accessing talent in India
 Lack of modeling knowledge
 Decision-making culture (HIPPO)
 Use cases emerging
According to Wikibon the market is expected
to reach USD53.4 billion in 2016
13

14.
Target used data mining to predict buying habits
of customers going through major life events
 Target was able to identify 25 products (e.g., vitamin
supplements) that when analyzed together helped
determine a ―pregnancy prediction‖ score
 Sent baby-related promotions to women based on this
score
 Outcome:


Sales of Target’s Mom and Baby products sharply
increased soon after new advertising campaigns
Privacy concerns: Target had to adjust how it
communicated the new promotions
Source: ―How Companies Learn Your Secrets‖, Duhigg, The New York Times, Feb. 16, 2012.
14

15.
Many industries using data analytics
for improving value disciplines
 General Electric using Big Data to optimize the service
contracts & maintenance1 The industrial internet.
 Netflix used Big Data to predict if a TV show will be
successful- ―House of Cards‖ series, Director & promotions2
 LinkedIn used Big Data to develop ―People You May Know‖
products – 30% higher click-thru-rates3
Source: 1―What’s Your Strategic Intent for Big Data?‖, Davenport , CIO Journal in The Wall Street
Journal, 1/23/2013.
2‖The Future of Entertainment is Analytical‖, Davenport , CIO Journal in The Wall Street Journal, 3/6/2013.
Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012.
15

16.
What are the sources of data?









ERP/CRM Transactional Systems
Point-of-Sale/Scanner at Retail
Customer Loyalty Programs
Financial Transactions
Click-Stream Data
Social Media Data
Mobile
Personal analytics
External Data Aggregators (e.g., AC Nielson)
16

17.
What is a capability?
 Firm’s capacity for undertaking a particular
productive activity [Grant 1997]
 Hamel & Prahalad coined the term core
competences to distinguish those capabilities
fundamental to a firm’s performance and
strategy. They:


make a disproportionate contribution to ultimate customer
value, or to the efficiency with which the value is delivered,
and
Provide a basis for entering new markets
17

18.
Key competencies
 Technical




Modeling
Programming
Statistical
Science
 Domain knowledge
 Talent management
 Cultural
 Change management
18

19.
How do companies build an
analytics capability?
 People: Data Scientist (need analytical + social
+ communication skills)
 Leadership: Help decision-makers shift from adhoc analysis to ongoing conversations with
data
 Technology: for data management, programming
and modeling
 Process: workflows and methodologies for
models and experiments
Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012.
19

20.
Choices
 Insource
 Outsource
 Hybrid
 Challenges with traditional IT
outsourcing?
20

21.
Sourcing intent
 Augmentation
 Adding new capacity
 Validation
 Knowledge transfer and IP
 Building platforms
21

22.
On Shore
Off Shore
Models of outsourcing
 A company with its HQ in NY
opens a analytics center in Chennai
(India).
 A company with its HQ in NY gets a
third-party to do its work in Chennai
(India).
 Often called “Captive Centers” or
“Captives.”
 A company with its HQ in NY
opens a analytics center in San
Diego / Durham (NC).
In-House
 A company with its HQ in NY gets a
third-party to do its work in San
Diego.
B
P
O
Outsource
BPO = Business Process Outsourcing
22

23.
What should you outsource?
23
Strategic Sourcing From Periphery to the Core. By: Gottfredson, Mark; Puryear, Rudy; Phillips, Stephen. Harvard Business Review, Feb2005, Vol. 83 Issue 2, p132, 8p

24.
Lessons from Outsourcing IT
 Clear specifications
 Increases flexibility in changing markets
 Fast response
 Fixed to variable costs
 Proximity between onshore and offshore hub




matters
Infrastructure and connectivity
Language and technical skills
IT adoption
Contingency planning
24

25.
Sourcing analytics
 Core vs. periphery
 Analytics for competitive advantage vs.
parity
 First time vs. in-house availability
 Source all vs. source add-on capabilities
25

26.
What challenges should one anticipate?
 Problem definition complexity
 IT implementation challenges
 Modeling complexity
 Change
 Data regulation and compliance
26

27.
Look for
 Model building skills
 Business domain knowledge
 Technical or programming skills
 Scientists vs. order takers
27

28.
Client sophistication
 Based on data management, talent
management and analytics penetration
in biz strategy.
Tom Davenport
 Analytics challenged
Stage 5

First time users
 Analytically superior

Internal capability exists
Analytical
Competitors
Stage 4
Analytical
Companie
Stage 3
s
Analytical Aspirations
Stage 2
Localized Analytics
Stage 1
Analytically Impaired
28

29.
Questions
 Unique vendor capabilities
 Data protection
 Analyst churn and satisfaction
 Re-badging dedicated analysts
 Cultural fit
 Sourcing model
 IP ownership
 Low end vs. high end work
 M&A risks
29

30.
Variables to consider
 Capability costs
 Risk of failure
 Size of vendor


Large body shops
Small – niche skills and eager
 Domain knowledge
 Skills
 RFPs
30

31.
Traditional relationship
framework
 Includes setting detailed specifications
 Pursuing costly renegotiations and
 Participating in limited information
exchanges
 Discourage flexibility
 Stifle innovation and
 Erode trust
31

32.
Analytics sourcing
 Strategic importance to customer
 Vendor has more expertise
 Evolution and outcome of relationship is
uncertain
32

33.
33

34.
Strategic Adaptive Framework
 Incentives,
 Information and
 Collaboration mechanisms.
34

35.
Additional agreements
 Exit options
 Non-compete
 Rights of first refusal
35

36.
Centralization vs. Decentralization
 One brain
 Distributed knowledge
 Federated model
36

37.
Ecosystem Analysis
37

38.
Analytics Ecosystem (840 nodes)
Component
Platform
38
Platform with high brokerage

39.
High brokerage nodes
Cloudera
Pentaho
IBM
Fractal
MuSigma
Rapidminer
SAS
Cognizant
MTECH
Accenture
Tableau
SPSS
Infosys
AbsolutData
Capgemini
Genpact
KXEN
Oracle
Wipro
Opera
TCS
HCL
LatentView
Guavus
39

40.
Types of service providers









Augmentation or spot services
Pure play consultant
Technology platform provider
Change management services
Digital thought leadership
 Training for data scientists
 Smart Lab
 CoE
Infrastructure and libraries
Methodologies and Frameworks
Assessment
Data
40

41.
Investments
 Training/Recruitment



Data Scientist
 Certification based on competency and project experience
Techniques
Domain knowledge
 Product/platforms
 Visualization metaphors
 Knowledge communities
 Build absorptive capacity
41

42.
Risks
 Privacy and ethics of data - ―Big
brother‖
 New skills for production and selling
 Managing a pool of modelers
 Communication between biz, modelers,
programmers and scientists
 Model management
 Installed base of analysts/engineers
42

43.
Questions?
43

44.
44