1. The document discusses how to hire and retain analytics talent in the consumer packaged goods industry. It emphasizes the need for strong analytics leadership to develop a clear talent strategy and define analytics roles. 2. It highlights the importance of "analytic translators" who can communicate between business and technical teams to identify high-impact use cases. It also stresses prioritizing analytic workstreams and building expertise within each. 3. The document provides examples of when to buy versus build analytics capabilities and outlines what data scientists, engineers, and visualizers want in their roles to aid retention. It emphasizes delivering on promises and a culture of innovation.

1. Hiring & Retaining analytics talent in the
CPG world
CDAO Summit – Boston
1
Nov 18th, 2019

2. Clear gap between leaders and laggards on creating
value with Analytics ….
2
1. The executive team doesn’t have a clear vision for its advanced-
analytics programs
2. No one has determined the value that the initial use cases can deliver
in the first year
3. There’s no analytics strategy beyond a few use cases
4. Analytics roles—present and future—are poorly defined
5. The organization lacks analytics translators to create action
6. Analytics capabilities are isolated from the business, resulting in an
ineffective analytics organization structure
7. Costly data-cleansing efforts are started en masse
8. Analytics platforms aren’t built to purpose
9. Nobody knows the quantitative impact that analytics is providing
10. No one is hyperfocused on identifying potential ethical, social, and
regulatory implications of analytics initiatives
Source : McKinsey & Co

3. It starts with strong Analytics leadership ….
3
Provide the Executive
leadership with a clear view of
the Advanced analytics agenda
…
Analytics
Strategy
Leader
…
Develops the first few use cases
with an eye towards expected size
of prize
Develop a clear talent
strategy
Develop the operational
ways of working on the
analytics transformation

4. The leader needs to drive a clear talent strategy …..
Analytic
strategy
leader Data
Scientists
2nd & 3rd
party data
vendors
Data
Engineers
Data
Analysts
Data
Visualizers
Data
Scientists
Data
Engineers
Data
Analysts
Data
Visualizers
End users/
Business
stakeholders
Analytic
translators
ExternalInternal
Fullserviceanalyticvendors
Specializedanalyticvendors

5. Buy vs. Build – Case 1 ( Marketing Mix Modelling)
5
In-house expertise HighLow
HighLow
HighLow
HighLow
HighLow
Data complexity
Data science complexity
Cost of buying
Incremental Cost of
building
HighLowData privacy/ sharing risk
Analytics vendor sets up data pipes
with help from media, sales and
Kellogg IT
Analytic vendor does the
modelling and basic reporting
K-team develops more
strategic scenario planning
tools
On-going On-going 2019 2019
KelloggAnalytics invests on
data visualization and
activation

6. Buy vs. Build – Case 2 ( Revenue Growth Management)
6
In-house expertise HighLow
HighLow
HighLow
HighLow
HighLow
Data complexity
Data science complexity
Cost of buying
Incremental Cost of
building
HighLowData privacy/ sharing risk
BuyBuild
6
Kellogg invests withTop
tier consulting firm in
pilot markets
Kellogg DS shadow & learn ;
build when data sharing /
privacy issues
Kellogg DS build with oversight
from consultants
100% in-house build ;
no oversight needed
from cosultants
2018 2019 FH 2019 BH 2020

7. Critical to have strong analytic translators
7
Translates the business needs
to data scientists, data
engineers, and other tech
experts
…Analytics
Translator
…
Best filled by someone on the
business side with knowledge of
industry ; who can help identify
high-impact analytics use cases
Translators are also expected to
be actively involved in scaling
the solution and get buy-in with
business users
Skills needed are a mix of business
knowledge, general technical
fluency, and project-management
excellence

8. 8
What data scientists
want ….
• Technology - Industry standard with ability to
influence
• Business Engagement - Direct engagement vs.
behind the sheet
• License to Innovate - Innovation needs to
encouraged and engrained in our culture
• Meaningful Application

9. Analytic translator vs. Data scientist
Translator
9
Data Scientist
Industry Knowledge
Company knowledge
Statistics / Big data Analytics
skills
Business Engagement & story-
telling skills
Analytic work-stream knowledge
Technology knowledge
Deep expertise
Low expertise

10. 10
Setting up data engineering for success in
your organization …..
• Data junkie : Ability to quickly clean, manipulate,
transform and compile data
• Ad-hoc flow to work Needs to have capacity and
ability to operate in an ad-hoc manner
• Balance between agile execution and ongoing data
accuracy and sustainment. Domain knowledge is nice
to have but not critical
• Data hygiene is an expectation – nothing hurts
credibility more than “wrong” data

11. 11
Data visualizer…..The makings of an analytic
translator
• Customer centric – obsessed to deliver a
strong user experience
• Design thinking & Active prototyping – test
with users, get feedback and improve
• Should have the making of an analytic
translator - telling stories with data, domain
knowledge is important

12. Plan for building domain expertise within each prioritized
analytics work-stream
Market
Performance
Analytics
Brand
Building
Analytics
RGM &
pricing
analytics
Integrated Commercial Analytics
Strategy
Development
Analytics
1 2 3 4
5

13. 13
Summary
• Defining the talent strategy – in-source vs. out-source. Need the leader to
define
• Invest on hiring and growing strong analytic translators … they are the GLUE
between the business and technical
• Prioritize the analytic work-streams …. Cannot do everything !!!
• Build critical mass expertise on prioritized work-streams
• Invest on retention “hot buttons” for data scientists, data engineers and
data visualizers – ultimately stakeholder feedback is what matters !
• Build a culture of “promises made-promises kept”