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Breaking Bad Data
The journey to data fueled digital transformation
Jorgen Heizenberg
CTO I&D NL Capgemini
25th May 2016
#INFA16
Walter White (Bryan Cranston) (c) AMC
#INFA16
https://www.fnal.gov/pub/inquiring/timeline/06.html
#INFA16http://myjoyonline.com/sports/2014/June-6th/watch-video-elephant-predicts-
doom-for-ghana.php
#INFA16Source: www.zdnet.com ©
#INFA16
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.“
#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.“
#INFA16
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
#INFA16
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
#INFA16
… 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
#INFA16
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
#INFA16
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
#INFA16
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
#INFA16
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
#INFA16
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
#INFA16
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
#INFA16
Business Value impacted by Business & IT alignment
#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
#INFA16
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
#INFA16
‘Breaking’ Insights from Data to Create Actions & Value
Source: Universal Pictures ©
#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
#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
#INFA16
Jorgen Heizenberg – Capgemini
@jheizenb
Breaking Bad Data
Breaking Bad Data: The Journey to Data-fuelled Digital Transformation

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Breaking Bad Data: The Journey to Data-fuelled Digital Transformation

  • 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
  • 6. #INFA16 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.“
  • 8. #INFA16 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
  • 9. #INFA16 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
  • 10. #INFA16 … 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
  • 11. #INFA16 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
  • 12. #INFA16 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
  • 13. #INFA16 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
  • 14. #INFA16 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
  • 15. #INFA16 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
  • 16. #INFA16 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
  • 17. #INFA16 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
  • 19. #INFA16 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
  • 20. #INFA16 ‘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