1) The role of the CFO is becoming more strategic, with increased expectations to support decision-making through analytics. 2) Building sustainable analytics capabilities requires a 7 step approach: setting a vision, focusing on the right business questions, developing the operating model, creating a self-funding model, establishing governance, creating a roadmap, and managing change. 3) Key aspects include starting small with pilots to prove value, then operationalizing while continuing to drive value, and sustaining analytics by embedding it throughout the organization with change management.

1. Role of CFO in Building Sustainable Analytics Capability
Presented by: Venkat Chandra

2. CHANGING ROLE EXPECTIONS OF CFO
The Role of CFO’s has become very strategic in organizations and thus increased role expectations
with regards organizational performance
Score Keeper Strategic Partner

3. CHANGING EXPECTATIONS ON FINANCE FUNCTION
In addition, there is increasing pressure on finance function to reduce the functional cost while
expanding its role in decision support
Current State
Legend
Analytics
Risk & Control
Reporting
Transaction Processing
Future State
Expectation
Time (and associated cost) spent in Finance function

4. WHY ANALYTICS IS CRITICAL FOR CFO?
High performing organizations apply analytics in key business functions such as Finance and thus
championing it is critical for CFO to manage the expectations placed on finance function

5. WHAT DOES IT MEAN FOR FINANCE?
Finance needs to play a proactive role in the design and
implementation of analytics

6. 7 Steps to Building Analytics Capability
Building Analytics Capability requires considering the following 7 steps
1. Set the vision that reflects your ‘needs’
2. Start with the right business question
3. Analytics is not just about data, tech, stats…
4. Focus on building a self-sustaining model
5. Set-up robust governance and optimal structure
6. Develop pragmatic roadmap
7. Invest in change management throughout

7. 1. Set the vision that reflects your ‘needs’
Beginning
➢ Gut-based decision
making culture
➢ De-centralized ad-hoc
experimentation with
analytics
➢ Early awareness of
potential and some
demand from business to
embed analytics
Evolving
➢ Improved awareness of
role of analytics in
decision making and as a
competitive advantage
➢ Decisions partly based on
analytics
➢ Beginning of crafting
enterprise-wide strategy
2
Maturing
➢ Decisions mostly based
on evidence/data
(analytics-driven)
➢ Improved focus on change
management and culture
to drive organization-wide
adoption of analytics
➢ Defined analytics strategy
and organization structure
set-up to be aligned with
organization’s strategic
goals
Leading
➢ Decision-making primarily
based on analytics
➢ Strong analytics-driven
culture throughout the
organization
➢ Analytics initiatives are
strongly supported and
even led by CxO
➢ Very mature analytics
organization with strong
leadership that is self-
sustaining
41 3
The stage of analytical maturity is determined by ability to use analytics in taking better and quicker
decisions. Building analytics capability requires base-lining current and defining the target maturity

8. 2. Start with the right question
The key to operationalizing analytics is to appreciate the analytics value chain. The ability to identify and
framing right business questions is a critical first step
What’s the problem hypotheses?
Is this worth solving? (Business case)
What is the level of precision required to
solve?
What analysis / techniques will be required ?
(Analytical plan/ Solution framework)
Does this work and is this the right
approach? (Proof of concept testing)
Industrialize….

9. 3. Analytics is not just about data, tech, stats…
Building analytics program is more than just data and technology. It involves building all aspects of the
operating model…
Culture
Talent
Tech / Data
Structure
Performance
Mgmt.
Process /
Methods

10. 3. Analytics is not just about data, tech, stats…
….Human Experience is critical
Source: IBM Watson presentation (2017)

11. 3. Analytics is not just about data, tech, stats…
….and ability to balance qualitative aspects with quantitative aspects (part art- part science) to ensure
data informs strategy and the strategy is explained by the data
Qualitative Quantitative
Functional and
sector experience
Intuition
Visualization
Creativity and
innovation
Data engineering
Programming /
Computational
skills
Scientific
approach
Math and
Statistical
application
Analytics
driven
decision
support

12. 4. Focus on building a self-sustaining model
Leading companies are able to establish self-funding operating models by leveraging analytics to
generate rapid value and use realized value to fund investments needed
Step 1: Identify initiatives
to generate value
Step 2: Prioritize the
initiatives and decisions
Step 3: Implement to
generate value
Step 4: Harvest value to
make investments
Generate value by executing analytics driven projects/solutions
Leverage value generated to build-on analytics operating model capabilities

13. 5A. Set-up robust governance and optimal structure
Successful operationalization requires robust governance model that enables build confidence for all the
stakeholders and ensures that focus is always on right things
Steering Committee
CFOAdvisory and
Innovation
Board
Analytics function Leader COE
Analytics Functions
Cross-
functional
analytics
talent
Analytics
training
Value
tracking and
reporting
Tools /
technology
platform
Shared use
cases /
Knowledge
base
Shared data
assets
1. Data &
Information
management
Analytics delivery and execution : Tools and enablers
2. Tools and
Technology
3. Advanced
Analytics
Programs, services and hypothesis generation

14. 5B. Set-up robust governance and optimal structure
Successful operationalization requires robust governance model that enables build confidence for all the
stakeholders and ensures that focus is always on right things
Centralized
All activities go through
and are managed by group
function
Decentralized
Activities initiated and
managed by the interested
parties
Hybrid
Centralized activities at
key points of the process

15. 6. Develop pragmatic roadmap
In order to most effectively tap into the value of analytics, organizations should focus on a phased
roadmap that begins by starting small and building a case for change and buy-in
Year 3+
The build of various analytics enablers should continue (including some major
investments) while also embedding analytics across the organization.
Year 2
As a next step, operationalization of strategy and exeucition of key foundational
elements of operating model may be initiated while value is generated through
project execution
Year 1
The first step to implementation is to build the case for change by proving the value
of analytics through ‘pilot’ projects.
Momentum needs to be built across the organization by clearly demonstrating the
gaps against target ed maturity of the analytics organization
1. Build the case for change
2. Operationalize while
continuing to drive value
3. Sustain

16. 7. Invest in change management throughout
Often most underinvested but most important for embedding sustainable analytics capability, analytics
programs need to be embraced by people and people need help to move up the change curve!

17. END