Strategies for Big Data

1. What is your
#BigData Strategy?
@mpiskorski
Associate Professor of Strategy
Harvard Business School

2. 2© 2014 Mikołaj Jan Piskorski
Big Data:
Large benefits
 Big data opportunity
– Volume
– Velocity Profitability higher by 5% points
– Variety

3. 3© 2014 Mikołaj Jan Piskorski
Big Data:
But huge strategy challenge
0 5 10 15 20 25 30 35 40
Top execs don't know how to turn data into business value
Management has no bandwidth
Business lines don't know how to turn data into business value
Ability to get the data
Culture does not emphasize data sharing
Ownership of data is unclear
Perceived costs outweigh perceived benefits
No case for change
Percentage of respondents

4. 4© 2014 Mikołaj Jan Piskorski
Big Data:
Strategy challenge
0 2 4 6 8 10
Financial management
Operations and production
Strategy and business
development
Sales and marketing
Customer service
Product research
General management
Risk management
Customer experience
Brand management
Workforce management
Times more likely to implement
0
5
10
15
20
25
30
35
40
Capture
information Aggregate
information Analyze
information Disseminate
insights
Percentageofrespondents
Top performers Low performers

5. 5© 2014 Mikołaj Jan Piskorski
Big Data:
Strategy challenge
Big Data
Opportunity
Big Data
Strategy
Big Data
Advantage

6. 6© 2014 Mikołaj Jan Piskorski
Big Data strategy
creates new products or increases profitability of existing ones
by integrating various datasets and applying analytic models
to create better decision support tools
Big Data:
Strategy statement

7. 7© 2014 Mikołaj Jan Piskorski
Big Data:
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

8. 8© 2014 Mikołaj Jan Piskorski
Four elements of Big Data strategy:
Example from an insurance company

9. 9© 2014 Mikołaj Jan Piskorski
Big Data:
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

10. 10© 2014 Mikołaj Jan Piskorski
Business impact:
Building new products to create data ecosystems
Nike’s devices
collect data
Phone/Computer
Online social
platform
Twitter/Facebook

11. 11© 2014 Mikołaj Jan Piskorski
Business impact:
Building new products from old products with Big Data
 General Electric invested $250,000,000/year over 4 years into industrial Internet
– For example, turbines now have 100 physical sensors and 300 virtual sensors to
help customers identify problems before they occur and improve fuel efficiency
– A turbine generates over 500GB of data per day which General Electric uses to
build its next generation turbines
– 1% efficiency increase = $20,000,000,000 per year in savings for customers…
 Even if GE only captures 10% of these savings, ROI is very high!

12. 12© 2014 Mikołaj Jan Piskorski
Business impact:
Building new products to create data ecosystems
Data
ecosystems
Data shared with
consumer
Consumer uses
product more
Customer
relationship
management
Time to buy new
product
Consumer shares
data with friends
and strangers
Lower acquisition
costs
Create new data
products
Aggregate data
for new product
development

13. 13© 2014 Mikołaj Jan Piskorski
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

14. 14© 2014 Mikołaj Jan Piskorski
Data integration:
Merging various data for business impact
Facebook
has emails and
phone numbers
of its users
Your CRM
has emails and
phone numbers
of your customers
Custom audience
can be exposed
to advertising
on Facebook
or Twitter
or LinkedIn
Use your own loyalty
program to see if those
who were exposed reacted
(great for offline purchase)
No wasted advertising
means higher ROI

15. 15© 2014 Mikołaj Jan Piskorski
Data integration:
Merging unstructured data in real time
Bank card
used here
My phone is
here
 Joint venture between a European bank and a big European mobile phone operator
 Huge value to everyone involved, but…
– Can the operator sell the data?
– Who should pay for this?

16. 16© 2014 Mikołaj Jan Piskorski
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

17. 17© 2014 Mikołaj Jan Piskorski
Analytic models for Big Data:
Typical HR analytics
Business Goals Most organizations
Hiring
Manual interpretation of CV
Person to person interview process
Performance Largely standardized training emphasizing skills
Retention
Managed during annual review
React when employee announces departure
Org design Static organizational designs

18. 18© 2014 Mikołaj Jan Piskorski
Analytic models for Big Data:
Google People Operations analytics
Business Goals At Google
More effective hiring
Algorithms read CVs to compare those accepted to those
rejected and invites some applicants back
Higher performance
Project Oxygen identified eight characteristics of great
leaders which had little to do with skills
Now Google teaches, measures and rewards those
Higher retention
Algorithms that predict when someone will leave allow
Google to be proactive to stop that
Better org design
“What if” scenarios to anticipate who should work with
whom and on what kinds of projects
• “All people decisions at Google are based on data and analytics.”
• “We bring the same rigor to people-decisions that we do to engineering decisions.”

19. 19© 2014 Mikołaj Jan Piskorski
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

20. 20© 2014 Mikołaj Jan Piskorski
Decision tools for Big Data strategy:
Caesars Casinos (previously known as Harrah’s Casinos)
 Leader in customer loyalty through analytics
– Total Rewards program became industry standard
 Customers never come back if
– Lose when playing slot machines
– Have to stand in lines to get to food
 Can prevent if customer receives offer or gets expedited
– Must do before customer decides to leave
 Slot machines deliver information  real time analysis  free food offer
 Real time video analysis  staff informed  expedite top players to get food

21. 21© 2014 Mikołaj Jan Piskorski
Decision tools for Big Data strategy:
Bank of America
Big data
• Record voice
conversations with
customers
• Transcribe them
Analytical models
• Natural language
processing to understand
meaning
• Identify patterns of
dissatisfaction
• Correlate with “customer
journey” through other
channels
Decision tools
• Next time customer
calls, the agent can offer
incentives to stay…
• … or new products to
prevent defection
• Can also be used to
target customers
proactively
Over $500 million in revenue
that would be otherwise lost

22. 22© 2014 Mikołaj Jan Piskorski
Big Data strategy process
Business
impact
Data
integration
Analytic
models
Decision
tools

23. 23© 2014 Mikołaj Jan Piskorski
Big Data strategy process:
Strategy development over time
Business impact  data integration
* Start today…
* Pick an average performing
business
* Identify sources of competitive
disadvantage for that business
* What kinds of data + analytic
models + decisions tools will
alleviate those
Data integration  business impact
* ½ way through the previous
process challenge them to figure
out what new data integrations
will get business results.
Implement if good ideas
* Set up another team and
challenge them to figure out new
data integrations for business
results. Implement if good ideas
New products  data  business
* Give the winning team the
challenge to develop new
products that will collect data for
business impact
* Note that this team will need a
lot of support for new product
development
* In some cases you might need
to set up a separate division for
them

24. 24© 2014 Mikołaj Jan Piskorski
Big Data strategy process:
Data integration  business impact
 What is your unique data?
– LinkedIn = unique career data  product can help you choose career
– Google search = data on what people don’t know  identify concerns
– Netflix = data on what movies people like  can make your own hit
– What will your unique data be?
 How can your data be merged with outside data?
– Location
– Sentiment data
– Purchase history
– Weather

25. 25© 2014 Mikołaj Jan Piskorski
Big Data:
Four elements of Big Data strategy
Business
impact
Data
integration
Analytic
models
Decision
tools

26. 26© 2014 Mikołaj Jan Piskorski
Implementing Big Data strategy
People
Org
structure
Culture

27. 27© 2014 Mikołaj Jan Piskorski
People:
Data scientists
Data
Scientists
Hacker
• Codes
• Understands
technology
Analyst
• Statistics
• Unstructured
data
Scientist
• Makes sound
decisions
• Improvises Advisor
• Strong
communication
skills
Business
• Knows
business
strategy

28. 28© 2014 Mikołaj Jan Piskorski
People:
Data scientists
Data
Scientists
Hacker
• Python, Hive, Pig
• Hadoop, MapReduce
• Hackathons
Analyst
• Machine Learning
• Natural Language
Processing
Scientist
• No need for PhD
• But experimental
physics helps
• Do it yourselfer! Advisor
• Translate data and
science into business
goals
• Tell story with data
Business
• Customer insights
• Understands
competitive reaction

29. 29© 2014 Mikołaj Jan Piskorski
People:
Data scientists
Hacker + Analyst + Scientist + Advisor + Business
Hacker + Analyst
Hacker + Scientist
Scientist + Advisor + Business
Analyst + Advisor + Business
 Team approach ok, but must have overlapping skills
 Where do I find them?
– Junior university scientists
– More EU universities begin to offer data science programs
– Get IBM, Accenture, Deloitte to train them for you
 How do I retain them?
– Train, train, train and
– give them really ambitious projects…
– … or expect to lose them within 18 months

30. 30© 2014 Mikołaj Jan Piskorski
Organizational structure:
Where does your BigData team sit and who leads it?
 Center of excellence or embedded in divisions?
– Depends on the underlying strategy
 Business impact  data: Embedded in business unit
 Data  business impact: Center of excellence
 Product  data  business impact: Matrix approach
 Leadership
– What do you call them in top executive functions?
 Senior VP of Big Data, Social Design, and Marketing: Intuit
 SVP of Analytics, Insight and Loyalty: Charles Schwab
 Chief Data Officer: Bank of America

31. 31© 2014 Mikołaj Jan Piskorski
Organizational structure:
Where does your BigData team sit and who leads it?

32. 32© 2014 Mikołaj Jan Piskorski
Culture
 Belief that technology disrupts business models
 Top management commitment to
– internal innovation  it is OK to fail
– external innovation  best inventions do not have to be invented here
 Data settles disputes in the organization
– “what do the data say?”
– “where did these data come from?”
 Open access
– many people have access to data and can experiment with it