Data Lakes are emerging as a critical data management component aimed at limiting traditional enterprise data silos and enabling agile access to all the data needed for faster decision making. However, if we don’t ensure the trust in the underlying data, and track the trust worthiness of data throughout the lineage, the decision makers will not trust the insights generated from these data. This session will share implementation best practices developed by Unilever on building and operationalizing trustworthy insights and ML models on a data lake.

1. WHY TRUST IN DATA IS IMPORTANT
WHEN WE BUILD ML ALGOS?
BUILDING THE FOUNDATION FOR AI FOR UNILEVER
NALLAN SRIRAMAN (SRI)
GLOBAL HEAD OF TECHNOLOGY (DATA & ANALYTIS)
SEP 2019
Copyright (c) Unilever 2019

2. Copyright (c) Unilever 2019 2
51 Billion Euros
in annual turnover 2018

3. Lots of Operational
Data
Copyright (c) Unilever 2019 3

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Why almost every AI prediction
made public got it wrong?
Is there a lesson for us in the
Corporates to Learn?
What can we do about it?
§§

5. Copyright (c) Unilever 2019 5
AI World Cup 2018
predictions almost all wrong
The World Cup 2018 was the top
sporting event of year, and AI
researchers at Goldman Sachs,
German Technische University of
Dortmund, Electronic Arts, Perm
State National Research University
and other institutions ran machine
learning models to predict outcomes
for the multi-stage competition.
Most however were totally wrong,
with only EA — which ran its
simulations using new ratings for its
video game FIFA 18 — correctly
favouring winner France. The EA
game engine is backed by numerous
machine learning techniques
designed to make player
performance as realistic as possible.
https://medium.com/syncedreview/2018-in-review-10-ai-failures-c18faadf5983

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https://www.firstpost.com/tech/news-analysis/game-of-thrones-ai-algorithm-predicts-who-will-finally-
sit-on-the-iron-throne-6436491.html
The new algorithms developed at the Technical University of Munich (TUM) in
Germany predict that Daenerys Targaryen has the highest chance (99 percent)
of surviving the harrowing world of Game of Thrones.
Copyright (c) Unilever 2019

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How we used social media analytics
to call Brexit.

8. Copyright (c) Unilever 2019 8

9. WHY THIS HIGH DEGREE OF WRONG PREDICTIONS
LEADING TO ERODING TRUST IN AI?
Copyright (c) Unilever 2019 9
Simple reason – Lack of Data Coverage
supporting aspects of prediction
Lack of data – Game of Thrones
Conscious bias – Trump and Brexit
Unconscious bias –World cup
The bias erodes trust in “Predictors”

10. CONSUMER
INSIGHT &
MARKETING
INNOVATION
SUPPLY CHAIN
TALENT &
ORGANISATION
E2E DIGITISED
OPERATIONS
ABUNDANCE OF DIGITAL SIGNALS ACROSS THE UNILEVER VALUE CHAIN
SUPPORTED BY THE DATA LAKE FOUNDATION
TECHNOLOGY
& DATA ANALYTICS
Copyright (c) Unilever 2019 10

11. HOW ARE WE ESTABLISHING TRUST ?
Protect the Lake becoming
swamp from in coming data
Establish transparency in
what data makes up the
predictions and what is not
Hire data scientists without
“Tunnel" VisionPic curtesy © Networkworld
Copyright (c) Unilever 2019 11

12. ESTABLISH TRANSPARENCY IN
DATA THAT MAKES UP THE PREDICTIONS
via Data Catalogue and Business glossaries
Copyright (c) Unilever 2019 12

13. © Scott Adams