The challenge of Data Science initiatives is to turn insights into value. However many initiatives never make it past the stage of analysis, ultimately only creating frustration for all involved stakeholders. Pierre and Koen explore what you can do to avoid this pitfall when you start up your next data science initiative.

1. The proof of AI is the value created
Pierre Marchand & Koen Wildemeersch
Data Strategist & I&D Leader @ Capgemini

2. Apply AI
to augment human capabilities and
improve human performance,
man-machine collaboration,
Business results
OUR
MISSION
AI … an umbrella term
Oxford Dictionary: “Artificial Intelligence is the theory and development of
computer systems able to perform tasks normally requiring human
intelligence, such as visual perception, speech recognition, decision-making,
and translation between languages.”

3. Discover new
ways of working
Innovate
at speed
Enhance productivity
at a lower cost
Improve quality and
predictability
Decrease
time-to-market
Increase efficiency, agility,
responsiveness and resilience
WHERE ?

4. WHAT ?
Service
Orchestration
Blockchain
</>
Algorithms
Process
Automation
Neural
Networks
Robotic
Process Automation
Big
Knowledge
Chat Bots
Internet
of Things
Big Data
Closed
Circuit TV
Natural
Language
Processing
Machine
Learning
Cognitive
Computing

5. WHICH CASE ?
Source : Turning AI into concrete value, Capgemini Digital Transformation Institute

6. What happened ?
DESCRIPTIVE
Why did it happen ?
DIAGNOSTIC
What will happen ?
PREDICTIVE
How to make it happen ?
PRESCRIPTIVE
USE CASES

7. What happened ?
DESCRIPTIVE
Why did it happen ?
DIAGNOSTIC
What will happen ?
PREDICTIVE
How to make it happen ?
PRESCRIPTIVE

8. Stock synchronization issue One of Europe’s Largest
fashion Retailers
Business
Challenges
• Stock measured in the store differs significantly from
what is in the Transaction Log.
• Unknown where the root cause was:
• Distribution centers?
• In the Store?
• Elsewhere?
• As a consequence products were not in the store
resulting in:
• A loss in sales
• Unhappy customers
• Frustrated Staff
What we did
• Loaded transactions for a larger number of stores
• Applied data mining to identify those transactions
that were odd and link those
• Analyzing the missed revenue to build a business
case to change the processes that were not
functioning well
Result
• Identified the issue – which was affecting all the
stores in the Point of Sales Network. Correcting up to
17%.
• Build business case based upon out-of-shelf/missed
revenue
• Fixed the processes at in the bookkeeping and
clothing labelling
• Also: Predicted when stock would reach ‘0’ in time

9. What happened ?
DESCRIPTIVE
Why did it happen ?
DIAGNOSTIC
What will happen ?
PREDICTIVE
How to make it happen ?
PRESCRIPTIVE

10. Pattern recognition for repair of
money transfer instructions
European
Settlement Services
• Hundreds of thousands of money transfer
instructions coming in daily
• Thousands of instructions need repair before
execution
• Repair is ~50% automatic
• Duplicates galore
• 200+ variables
• Structured case converted into unstructured
case
• Features from text mining results
• Predictive model to identify probability of
validation
• 32% improvement in instruction automatic
repair
• 100% duplicates identified
• Potential to shift 2 highly experienced FTEs to
complex high value cases
• New patterns put in production directly
Business
Challenges
What we did Result

11. What happened ?
DESCRIPTIVE
Why did it happen ?
DIAGNOSTIC
What will happen ?
PREDICTIVE
How to make it happen ?
PRESCRIPTIVE

12. Multinational Pharmaceutical
Company
Business
Challenges
• Improve sales forecast
• Multinational scale
• Very short time series for new
projects/products
• Very large number of time series
What we did
• Time Series and pattern recognition through
clustering methods and Dynamic Time
Wrapping
• Optimization on algorithms ensemble, not
individual ones
Result
• Improved forecasting quality by 12%
• Beat niche competitor by huge margin
• Potential to shift manual forecasting team
(50) to complex high value cases
Predicting sales using Time Series

13. Automated e-mail classification European
Settlement Services
Business
Challenges
• Large number of legal documents incoming
daily
• Each document has to be routed to correct
department for processing
• Right-Shore model balancing offshore &
onshore
• Offshore Data Science team not on client
network
• Existing 85% automation already excellent
What we did
• Dedicated VPN connectivity to offshore Data
Science
• Fine tuned OCR
• Common environment across onshore &
offshore
• Large scale optimization
Result
• Increased dispatch accuracy from 85 to 93%
• Reduce inter-department redirections by
10+%
• Potential to shift up to 10 FTEs to complex
high value cases

14. What happened ?
DESCRIPTIVE
Why did it happen ?
DIAGNOSTIC
What will happen ?
PREDICTIVE
How to make it happen ?
PRESCRIPTIVE

15. Insurance data mining for finding
contract non-renewals
Major Belgium Insurance
Company
Business
Challenges
• Major Belgian Insurance Company that
noticed that their brokers stopped
renewing/upselling
• Leading to a loss of revenue
What we did
• Applying SAS the broker data and Policy data
were crossed.
• A time series analysis was performed to
identify broker behavior through time
• Part of a larger project building a greenfield
enterprise data warehouse
Result
• A clear understanding of why brokers
stopped upselling/renewing as their
commission decreased in time
• Clear understanding of what needed to
happen to correct, based on data.
• Renewed renumeration model
• Enabled Analytics of the future

16. Value
Deployments
Engaging
the customer
Boosting
operations
Generating
insights
600+
AI Customer Deployments
350+
Automation & AI task force
deployed to engagements
4,500+
Use Cases
More than 130
vendor products evaluated
3,000+
Robots deployed
10,000+
Automation &
AI Academy
Experts
7,000
Automation Experts in RPA,
Cognitive Technologies and
AI
350+
AI Evangelists
AI @ CAPGEMINI GROUP

17. How we see AI adding value to our customers
Source: Capgemini Research Institute, State of AI survey, N=993 companies that are implementing AI, June 2017

18. OperateDeliverAdvise
AI Consulting services: Assessment and
Roadmap Definition
AI Implementation Services to transform
Business Operations
Run AI Services:
Operate & Maintain
Strategy & Roadmap Creation
Feasibility Assessment &
Business Case Creation
Developing Target Operational Model
Project Planning & Prioritization
Solution Design &
Architecture Definition
Solution Development
Deployment
Standardization & Reuse
Monitoring & Support
Maintenance & Enhancement
Change Management
Continuous
Service Improvements
AI FOR YOU E2E

19. This message contains information that may be privileged or confidential and is
the property of the Capgemini Group.
Copyright © 2018 Capgemini. All rights reserved.
A global leader in consulting, technology services and digital transformation, Capgemini is
at the forefront of innovation to address the entire breadth of clients’ opportunities in the
evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and
deep industry-specific expertise, Capgemini enables organizations to realize their business
ambitions through an array of services from strategy to operations. Capgemini is driven
by the conviction that the business value of technology comes from and through people.
It is a multicultural company of 200,000 team members in over 40 countries. The Group
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