1. FOUDROYANT ANALYTICS
Data Strategy
Developing a Modern
Enterprise Data Strategy
Sai Venkatesh Attaluri
by

2. Data Doldrums
• Big Data
• Data Science
• Data Mining
• Data Marting
• Data
Warehouse
• Data Clusters
• Data Integrity
• Data Security
 Data Bases
 Relational
 Structured Data
 Unstructured
Data
 Semi Structured
Data
 ETL
 Data Integration
 Resources
 Data Scientists
 Data Engineers
 Data Architects
 Chief Data
Officer
 The list goes
on.......

3. The Big Mystery
• Big data and analytics have climbed to the top
of the corporate agenda.
• Together, they promise
–To transform the way companies do business
–Delivering the exceptional performance
gains
–Assuring competitive differentiation
• But what is required to fully exploit data and
analytics?????

4. Courtesy: Silicon Valley

5. Courtesy: Silicon Valley

6. CONNECTING TECHNOLOGY AND BUSINESS VALUE
Courtesy: Silicon Valley

7. 10 THINGS A DATA STRATEGY SHOULD INCLUDE
Courtesy: Silicon Valley

8. Courtesy: Silicon Valley

9. 4 PRINCIPLES OF A SUCCESSFUL DATA
STRATEGY
• How does a Data Generate Value?
• What are our Critical Data Assets?
• What is our Data Ecosystem?
• How do we Govern Data?

10. Philosophy
• Prioritize for highest business value when using
emerging technology.
• Provide highly productive teams of data
scientists and engineers.
• Design with outcomes in mind.
• Be agile: deliver initial results quickly, then
adapt and iterate.
• Collaborate constantly with cross functional
teams.

11. Revealing The Mystery
• It requires three mutually supportive
capabilities
1.Identify, Combine & Manage Multiple
Sources Of Data.
2.Capability to build Advanced Analytics
Models for Predicting And Optimizing
Outcomes
3.Management must possess the muscle to
transform the organization so that the data
and models actually yield better decisions.

12. MAKE SURE IT’S FLEXIBLE
• Technology moves incredibly fast, and
competitive landscapes are highly
dynamic.
• Your data strategy should be a living
document, revisited often and revised as
conditions change.

13. MAKE SURE IT’S ACTIONABLE
• If it isn’t clear how you’re going to
execute your strategy, then you don’t
have the right one.
• Must work within the realm of the
possible and practical.

14. Business Value Addition
Courtesy: Silicon Valley

15. Data Strategy Checklist
Courtesy: Silicon Valley

16. Clear Strategy
• A clear strategy for how to use data and
analytics to compete.
• What the data can do for you?
• How it can yield business results to the
organization?
• Which deployment of the right
technology and capabilities to be
applied?

17. Choose The Right Data
• Leaders should invest sufficient time and
energy in aligning managers across the
organization in support of the mission with a
clear vision of the desired business impact
–Data sourcing
–Model Building for Panoramic and Granular
View
–Discovering the Invisible
–Organizational Transformation.

18. Approach
• Source Data Creatively
• What decisions could we make if we had all the information we
need?
• What’s the least complex model that would improve our
performance?
• Get The Necessary IT Support by Build models that predict and
optimize business outcomes
– Business leaders can address short-term big-data needs by
working with CIOs to prioritize requirements.
– The most effective approach to building a model usually starts,
not with the data, but with identifying a business opportunity
and determining how the model can improve performance.
– Although advanced statistical methods indisputably make for
better models, statistics experts sometimes design models that
are too complex to be practical and may exhaust most
organizations

19. Approach
• Transform your company’s capabilities
– Manage Resistance
– Resistance arises due to a mismatch between an organization’s
existing culture and capabilities and emerging tactics to exploit
analytics successfully.
– Provide a clear blueprint for realizing business goals
– Avoid Complex Tools designed for experts rather provide simpler
ones for the frontlines.
– Bottom line: using big data requires thoughtful organizational
change, and three areas of action can get you there.
• Develop business-relevant analytics that can be put to use
– By necessity, terabytes of data and sophisticated modelling are
required to sharpen marketing, risk management, and
operations.

20. Turning Data into Useful Information

21. Transforming Information into
Knowledge  Focus on a specific process as a
starting point.
 Create common business language
across the enterprise.
 Embed that language in tools,
systems and processes to create
new business capabilities and
agility.
 Create governance and change
management programs to
leverage these capabilities in day-
to-day work processes.
 Apply accepted practices to
unstructured content processes to
promote better information
hygiene
 Measure the information mgmt
process.
 Measure business impact of new
practices.
 Repeat on a department by
department basis.

22. Approach
• Embed analytics in simple tools for the front lines
– Separate the statistics experts and software
developers from the managers who use the data-
driven insights.
– Bottom line: To give frontline managers intuitive
tools and interfaces that help them with their
jobs.
• Develop capabilities to exploit big data
• Adjusting cultures and mind-sets typically requires a
multifaceted approach that includes training, role
modelling by leaders, and incentives and metrics to
reinforce behaviour.

23. Conclusion
• Executives should act now to implement big data and
analytics.
• Rather than undertaking massive change, executives
should concentrate on targeted efforts to source data,
build models, and transform the organizational
culture.
• Such efforts help maintain flexibility. That’s essential,
since the information itself—along with the
technology for managing and analyzing it—will
continue to grow and change, yielding new
opportunities.

24. Thank You