This session was presented on May 27th, 2021, in a Webinar organized by Gramener. https://info.gramener.com/5-steps-to-transform-into-data-driven-organization Session Details: Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives? This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation. Pain points we would be discussing: Most organizations stagnate midway in their data journey. Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5). Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas. This webinar will show what they are and how you can tackle them. Who should attend - Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers What Will You Learn? - What is data science maturity, and why does it matter? - How do you assess data science maturity and limitations of the assessment? - How can data science maturity help your organization level up (explained with an example)?

1. 5 Steps to Transform into a
Data-Driven Organization
Ganes Kesari Webinar, May 2021

2. 2
Top Challenges
CDOs Face
How Data Maturity
Can Help You
5-Steps To Become
Data-Driven

3. 3
INTRODUCTION
Ganes Kesari
Co-founder & Chief Decision
Scientist
“Simplify Data Science for all”
100+ Clients
Insights as Stories
Help start, apply and adopt Data Science
@kesaritweets
/gkesari

4. 4
“Digital Transformation” and Data: A pandemic snapshot
• Data leaders are leading or
heavily involved in digital
transformation
• By end of this year, in 75% of
large enterprises, the CDO office
will be a mission-critical function
like IT, HR, and Finance.
• Data teams have grown (49%),
budgets have expanded (43%)
Gartner’s 6th CDO Survey, 2021
Increased
49%
Stayed the
same
45%
Decreased
6%
Role of data leaders in digital transformation
Almost half increased their team headcount

5. 5
Top Challenges
CDOs Face
5-Steps to Become
Data-Driven
How Data Maturity
Can Help You

6. 6
Not every organization achieves the same level of value from data
1. Reference: McKinsey report - Catch them if you can: How leaders in data and analytics have pulled ahead
2. Reference: New Vantage Partners survey - Big Data and AI Executive Survey 2020
The gap between data science ‘leaders’ and ’laggards’ is widening.1
Only 37% executives report that they have created a data-driven organization.2
3. Reference: Gartner report – 5 Pitfalls to avoid when designing an effective data & analytics organization
Top 5 challenges
reported by CDOs3
Funding and
Resources
People and
Skills
Data Science
Strategy
Data
literacy
Culture and
Change

7. These challenges vary throughout an organization’s data journey
• How do we move up from Pilots to
Production?
• How to orient our data science strategy with
our business goals?
• What business problems should
we solve first?
• What skills and tools do we need?
• How to scale up our governance and
processes to improve execution?
• How do we establish clear linkages
between data and business ROI
• How to tap into internal and external
data for the right business insights?
• How can data stories help our users
adopt data for decision making?
• How to make data a habit across
every individual in the organization?
• How can “data value” influence every
investment we make?
Level 1
Level 2
Level 3
Level 4
Level 5

8. 8
One factor that influences the outcomes of all your data initiatives
“ Data maturity is a pre-requisite to
getting the most from your
analytics program.
Capabilities Gaps Benchmark
- Gartner

9. 9
Data Maturity is like a compass on your data journey

10. Top Challenges
CDOs Face
5-Steps to Become
Data-Driven
How Data Maturity
Can Help You

11. 11
ORGANIZATIONS MATURE IN THEIR DATA JOURNEY THROUGH FIVE LEVELS
Gartner Maturity Model for Data and Analytics (D&A)2
D&A is transactional and
managed in silos
Level 1
Basic
Level 2
Opportunistic
Level 3
Systematic
Level 4
Differentiating
Level 5
Transformational
D&A Strategy is not
business relevant
Lacks leadership
support; organizational
barriers
Business executives
become D&A champions
Data types treated
differently
Business-led with clear
data leadership roles
D&A is central to
business strategy
Data value influences
investments
Clear linkages to
outcome and business
ROI
Lacks trust in data;
analysis is adhoc
1. Reference: Gartner press release – 87 percent organizations have low maturity
2. Reference: Gartner research - IT Score for Data & Analytics
Gartner found1 that 87% of organizations were in low maturity (levels 1 & 2)

12. 12
How can you assess your data maturity?
Not very different from SAT/CAT tests.. ..tailored to gauge data capabilities

13. 13
What goes into the data maturity score?
Gramener Data Science Maturity Assessment methodology

14. 14
What can data maturity assessments tell you?
Gramener Data Science Maturity Toolkit

15. 15
“
Amongst organizations that reached
the highest level of Data Maturity,
nearly half of them significantly
exceeded business goals.
- Deloitte
Reference: Deloitte report
“
Analytics maturity IS associated with
company performance. 59 of 72 key
metrics show association.
- IIA
Reference: IIA report

16. 16
Steering your organization on the data-driven path

17. 17
Top Challenges
CDOs Face
5-Steps to Become
Data-Driven
How Data Maturity
Can Help You

18. 18
The 5 steps to a data-driven organization
• Define the Vision & Strategy
• Assess maturity & benchmark
1. Reflect ..on Organizational Strategy

19. 19
The 5 steps to a data-driven organization
• Identify strategic programs
• Build a data roadmap
1. Reflect ..on Organizational Strategy
2. Align ..on Business Priorities

20. 20
The 5 steps to a data-driven organization
• People, process & technology needs
• Define governance & execute
1. Reflect ..on Organizational Strategy
2. Align ..on Business Priorities
3. Define ..the Execution Process

21. 21
The 5 steps to a data-driven organization
• Improve data literacy, adoption
• Measure ROI from initiatives
1. Reflect ..on Organizational Strategy
2. Align ..on Business Priorities
3. Define ..the Execution Process
4. Adopt ..to realize Business Value

22. 22
The 5 steps to a data-driven organization
• Promote data-driven culture
1. Reflect ..on Organizational Strategy
2. Align ..on Business Priorities
3. Define ..the Execution Process
4. Adopt ..to realize Business Value
5. Radiate ..across the Organization

23. The 5-step RADAR Methodology will help your business level-up
23
Reflect Align Define
RADAR Data-to-Value Methodology
Adopt Radiate
on Organizational
Strategy
on Business
Priorities
the Execution
Process
for Business
Outcomes
across the
Organization

24. 24
How does the 5-step RADAR framework address the top 5 challenges?
Top 5 challenges
reported by CDOs
Funding and
Resources
People and
Skills
Data Science
Strategy
Data
literacy
Culture and
Change
Reflect
Align Define
Adopt
Radiate

25. How are data science projects typically picked and executed?
25
• Consolidate data in a spreadsheet
• Plot the data
• Forecast using built in regression techniques in
Excel
Now, the organization has sales forecasts which
possibly are much better than intuition.
Consider a small-sized retail chain who wants to start off on the
analytics journey. Here’s what typically happens:
But, is this the best approach? What
can go wrong?
• Alignment with Biz strategy is
assumed, as forecasting is a
relatively common issue for retail
• Lack of sales forecasts is assumed
to be the most pressing business
problem
• Sales teams often don’t know how to
action the statistical outputs of
forecasting models

26. Let’s revisit the same scenario using the 5-Step RADAR approach
26
• Leadership sees supply-demand bottlenecks
increasing as they scale
• From use cases, (a) Sales forecasting, (b) Supply
– Demand optimization & (c) Scenario planning,
they chose (b) based on org priorities
• A simple database which snapshots Sales and
Inventory data is planned
• This is used to create an optimization algorithm in
3 weeks. Processes were tweaked to allow for
Sales & Warehouse teams to consume the outputs
• Sales & Warehouse teams were taken through a
data literacy training to better leverage the outputs
Consider a small-sized retail chain who wants to start off on the
analytics journey. Here’s how it should be done:
1. Reflect
2. Align
3. Define
4. Adopt
5. Radiate

27. With each “Radiate” phase there is multiplying impact & value from data
RADAR sets off a positive spiral of business value
27
Reflect
Align
Reflect
Align Define
Adopt
Radiate
Reflect
Align
Define
Adopt
Radiate

28. 28
Discovering the treasure islands in your data-journey

29. 29
A LOGISTICS LEADER BECOMES DATA-DRIVEN AND SLASHES
WAREHOUSE TURN-TIMES BY 16%
Context
The client is a leading provider of logistics solutions in the US.
They have a presence across the country and operate several
dozen cold storage warehouses.
29
Challenges
§ The organization aspired to become data-driven and level-up into
data science to transform the business operations:
§ Choosing the most impactful initiatives
§ Acquiring the skills, processes, and tools
§ Measuring ROI from data science investments
§ Organizing data science teams and promoting collaboration
§ Managing change and improving adoption

30. 30
Transforming into a data-driven organization, step by step
1. Reflect
2. Align
3. Define
4. Adopt
5. Radiate
Cycle 1
• Reduce warehouse turn-times
• Optimize warehouse operations
to improve customer satisfaction
• Augment skills, processes
• Build appointment scheduler
• Pilot in 3 warehouses
• Storytelling for 80%+ adoption
• Productionize in all locations
• Manage change

31. 31
Transforming into a data-driven organization, step by step
1. Reflect
2. Align
3. Define
4. Adopt
5. Radiate
Cycle 1
• Reduce warehouse turn-times
• Design an innovation funnel
• Optimize warehouse operations to
improve customer satisfaction
• Augment skills, processes
• Build appointment scheduler
• Pilot in 3 warehouses
• Storytelling for 80%+ adoption
• Productionize in all locations
• Manage change
Today
…
• Expanded beyond quick-wins
• Robust experimentation pipeline
• Improve Ops efficiency
• Drive revenue impact
• Data maturity at Level 3
• Streamlined governance
• 16% reduction in turn-time
• ROI Tracking framework
• Data-driven leadership
• Marketing, success stories

32. 32
Free Toolkits and Useful References
• Take our 5-minute Data Science Maturity Assessment to find
out where you stand and what you should do next.
Whiteboard Series:
Executive insights with
data in under 5 minutes
Webinar: The best way
to Choose your Data
Science Projects
The 5 roles that
every data science
team must hire
When should
you not invest
in AI?
References to learn more:

33. 33
Thank You!
@kesaritweets
/gkesari
gramener.com
Reach out for a free discovery session: https://gramener.com/data-advisory-workshop