Jorgen Heizenberg explains how a business can harness data both from within and outside the organization to fuel its journey to digital transformation. Presented at Informatica World 2016 by Jorgen Heizenberg, CTO Netherlands, Capgemini Insights & Data

1. Breaking Bad Data
The journey to data fueled digital transformation
Jorgen Heizenberg
CTO I&D NL Capgemini
25th May 2016

2. #INFA16
Walter White (Bryan Cranston) (c) AMC

3. #INFA16
https://www.fnal.gov/pub/inquiring/timeline/06.html

4. #INFA16http://myjoyonline.com/sports/2014/June-6th/watch-video-elephant-predicts-
doom-for-ghana.php

5. #INFA16Source: www.zdnet.com ©

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Business Cases | Automotive
Copyright ©
Productivity „Change
the fan belt in 6 days
to prevent A/C from
failing.“
Environment. „Improve
your fuel efficiency by
shifting before 2.200
RPMs.“
Performance. „Extend
the life of your vehicle
and get more power by
using higher octane
fuel.“
Performance. „Deactivate
ECO Mode to get more
power for passing.“
Safety. „Avoid another
accident by maintaining a 5
meter distance.“
Service. „Stop
ahead. Roadside
assistance is behind
you.“
Sales. „Upgrade to a 5 series
to get the performance you
need.“
Economy. „Save
money by down-
shifting instead of
breaking.“
Sales. „Change your tires in
2 weeks to get improve
performance and ensure
safety.“
Service. „Add
coolant to prevent
over-heating.“

7. #INFA16
Performance. „Improve
output 2% by opening air vent
„A.“
Sales. „You need a
new A27 fuse in 96
hours.“
Safety. „Avoid accidents
by closing the lid before
activating the machine.“
Environment & Sales
„Decrease emissions by
using our new synthetic
lubircant.“
Productivity. „This
module will fail in 7
hours. A service
technician is already on
the way.“
Productivity & Sales.
„Your hopper will be
empty in 3 hours.“
Efficency. „“Combine
parts in trays to reduce
tray inventory and reduce
conveyor usage.“
Service & Sales. „Order parts
or schedule service. This
module has not been turned
on in 2 days.“
Safety & Productivity.
„The unit will over-heat in
3 hours. Add coolant or
turn-off.“
Safety. „Schedule training.
This unit is not being
operated properly.“

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Big, Agile and Diverse data
GB
TB
PB
GB/s
MB/s
KB/s
Day Hour Min Sec Sub-sec
BIG
FAST
Data Warehouses
NoSQL
Event
Processing
Tools
Hadoop
In-memory
databases
Historical
Data
StreamingData
(Events)
OLTP
Databases
*Source: Capgemini’s TechnoVision 2015

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Makes businesses thrive on insights in many different
ways …
FOUR WAYS
in which data-driven insights are
changes businesses
Efficiency and cost focus
Use of insights to identify
potential operational
efficiencies in the business
and so reduce costs. But
also: IT cost reduction
through modernization of
the data landscape,
leveraging next-generation
Big Data technology.
Growth of existing business
streams
Insights are used to
enhance existing market
offers through better
understanding of customers/
consumers
and of the effectiveness
of marketing & sales.
Growth through market
disruption from new revenue
streams
Big Data is changing
traditional business
boundaries. Enterprises
explore business areas that
were unknown or
unthinkable before.
Monetization of data itself,
with the creation of new lines
of business.
In some industries – such as
in financial services,
media & entertainment and
telecommunications - it is
already apparent that the
data organizations hold is
becoming their major
product.
Source: Big & Fast Data: The Rise Of Insight-driven Business

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… creating direct business value.
High rail usage, complex assets,
increasing data volume
(track sensor data)
Reduce Maintenance Cost;
Improve Asset Availability
& Service Delivery
Reduced Maintenance effort &
Cost; Higher Asset availability;
Improved service & performance
Saved 112 MIO CAPEX
Saved 13 MIO OPEX
& Less delay
Linear Asset Decision Support solution, helps
Network Rail get access to enhanced insight at
the point of action, ensuring reduced
maintenance cost, higher asset availability and
improved service delivery
Linear Asset Decision Support solution;
Consolidated data, consistently available,
Visual, easy to interpret format; in the hands
of the track engineers
Our track engineers across the
country can now access
critical asset-related data
(with LADS solution)
where and when they
need it the most, enabling
them to better target the
most appropriate type of
work to the right place.
Getting our asset
interventions right the first
time, saves cost and helps
us run an even safer,
better performing railway.
– Patrick Bossert,
Director of Asset
Information
#INFA16
INNOVATION
AWARD
WINNER

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What do companies use digital initiatives for?
Previous research: companies were neglecting operations
in their digital transformation
Source: Capgemini Consulting – MIT Sloan Management Review, “Embracing Digital Technology: A Strategic Imperative”, 2013
43%
40% 40%
30%
26%
Enhance existing
products and service
Improve
customer experience
Expand
reach
Launch new
products and services
Automate
operational processes

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70%
18%
12%
Things are changing - 70% of organizations now prioritize
operational analytics over front office
Source: Capgemini Consul2ng and Capgemini Insights & Data, Opera2ons Analy2cs Survey, December 2015
Percentage of companies which now focus more on opera2onal analy2cs
than on customer/ front office analy2cs
49% 50%
68% 68%
75% 75% 75%
Neutral
Focus more on operational analytics than on customer/ front office analytics
Focus more customer analytics than on operational analytics

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Areas where Manufacturing Companies can use Data to Gain Benefits
The size of the prize explains the strategic shift toward
operations from customer-facing initiatives
Source: Technet, “The $371 Billion Opportunity for “Data Smart” Manufacturers”, May 2014
$162B
$117B
$55B
$38B
Employee productivity Operational improvement Product Innovation Customer facing

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However, only 18% of organizations are achieving the
desired benefits across their operations
Source: Capgemini Consulting and Capgemini Insights & Data
Low High
41% 21%
18%20%
Strugglers
Laggards
Game Changers
Optimizers
Success in Realizing Benefits
LevelofImplementation
Analytics initiatives are
extensively integrated into
business operations
Analytics initiatives are still at
Proof of concept stage
Level of Implementation: Low Medium indicates analytics initiatives are still at Proof of concept stage or are integrated into some of business operations. High
indicates Analytics initiatives are extensively integrated into business operations
Success in Realising Benefits: Low Medium indicates firms are not able to realize desired benefits or moderately successful in realising desired benefits. High
indicates firms are highly successful in realising desired benefits from analytics initiatives

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Data
What are Game Changers doing differently?
Characteristics of Game Changers
Governance
Source: Capgemini Consulting and Capgemini Insights & Data
11%
27%
23%
45%43%
59%
48%
68%
Integration of Data to
Achieve Single View of
Operations Data
Routinely Collect
Unstructured Data to
Improve the Quality of
Data
Use External Data to
Enhance Insight
High Utilizaton of
Operations Data
Laggards Game Changers
28%
52%
Analytics is an Essential Component
of Decision making Process
Laggards Game Changers

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Operational Analytics Transformation Path to Value
Source: Capgemini Consulting and Capgemini Insights & Data
Low High
Strugglers
Laggards
Game Changers
Optimizers
Success in Realising Benefits
LevelofImplementation
High
! Develop a structured view of Analytics
Initiatives across the organization
! Build B-case and assess operations-wide
impact of analytics initiatives
! Identify availability and level of integration
of data within organization
! Ensure continuous executive
sponsorship for analytics initiatives
! Build centralized teams to
coordinate efforts
! Appoint analytics champions to steward
analytics initiatives
! Align initiatives to organisation’s strategic
objectives
! Institute governance mechanism to
implement insights across levels
! Set-up a feedback loop with stakeholders
to review performance

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Business Value impacted by Business & IT alignment

18. #INFA16
Manage
! Data governance and security
! Collaboration
! Value generation
! Program delivery
! Data-driven culture
! Information strategy
! Skill development
! Master data mgmt
! Metadata mgmt
! Data quality mgmt
! Operations, SLA’s
! Orchestration
Supported by (Business) Architecture
ValueAct
Insight
AnalyzeInformationProvideSource data
Customer
profitability
Operational cost
cutting
Risk prevention
Market share
increase
Business
Applications
! Customer
campaign
! Trigger activity
Business Processes
! Trigger event
! Adjust process
Decision makers
! Approve/reject
business
opportunities
! Develop new
business models
and products
Customer Experience
! Next best offer
! Customer lifecycle
! Customer value
Operational Process
Optimization
! Supply chain optimization
! Asset maintenance
! Quality management
! Process optimization
Risk, Fraud
! Financial risk
! Operational risk
! Fraud
! Cyber crime
Disruptive Business Model
! New products
! New business models
Search
What is relevant?
Explorative
How does it work?
Descriptive
What happened?
Diagnostic
Why did it happen?
Predictive
What will happen?
Prescriptive
How to act next?
Data asset
descriptions
Processed data
! Measures, KPI’s
! Dimensions,
Master data
Granular data
! Events
! Context information
Internal data
! IT managed
applications (ERP,
SCM, CRM)
! Business owned
informal data
! Documents, mail,
images, voice,
video
! Web and mobile
apps
! B2B
! Internet, Social,
Internet of Things
(machine, sensor)
! Third party data:
market, weather,
climate,
geolocation
! Open data
! …
External Data
Business performance
Performance
improvement

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Manage
Provide
Analyze
Act
Information
Source
data
Insight
Value
Explorative
Data Exploration
Descriptive
Reporting
Diagnostic
Ad-hoc Querying
Predictive
Data Mining,
Machine Learning
Prescriptive
Next Best Action
Search
Search, Retrieval
! Data governance and
security
! Collaboration
! Value generation
! Program delivery
! Data-driven culture
! Information strategy
! Skill development
! Master data mgmt
! Metadata mgmt
! Data quality mgmt
! Operations, SLA’s
! OrchestrationStream
Describe, classify
Ingest
Store
Prepare
Refine, blend
Manage lifecycle
Structured data
! IT managed applications (ERP, SCM, CRM)
! Business owned informal data
! Third party data
Unstructured Data
! Social
! Documents, mail, images, voice,
video
Semistructured data
! Internet
! Internet of Things (machine, sensor)
! Server logs
! B2B
Business ApplicationsBusiness ProcessesDecision makers
That allows for Big, Agile and Diverse data
Data at rest Data in motion
Data WarehouseData Asset
Catalog
! Index
! Tags
! Metadata
Aggregated data
Dimensional &
master data
Measures,
KPI’s
Load
Extract
Transform
Manage Quality
Aggregate
Historize
Data Lake
Business rules Predictive modelsBusiness results Alerts Signals
Granular data
EventsContextual
information
Analytical
Sandbox

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‘Breaking’ Insights from Data to Create Actions & Value
Source: Universal Pictures ©

21. #INFA16
While placing a premium on data quality, governance and
Security
Data Improvement Areas:
1. Data Quality (77%)
2. Data Security (75%)
3. Standardization (71%)
Source: Informatica & Capgemini research May 2016

22. #INFA16
The journey to data fueled digital transformation
Defining digital business objectives and
the design of a data management roadmap
to harness new data sources
Digital objectives & data management
roadmap
Ensure executive sponsorship and
leadership of big data initiatives. Anything
below boardroom level will not be enough
to drive lasting change.
Executive Sponsorship
Create a robust, collaborative data
governance framework that enables
organizational agility, while incorporating
data security, and data quality.
Data Governance Framework
Extend existing information architecture by
modernizing data warehousing systems
while integrating new big data
technologies.
Extend data landscape
Work towards a dynamic, data-driven culture
that involves both executives and
employees at the earliest stages in
developing, using and improving big data
solutions.
Data driven culture
and…
Source: Informatica & Capgemini research May 2016

23. #INFA16
Jorgen Heizenberg – Capgemini
@jheizenb
Breaking Bad Data