breaking bad data: the journey to data-fuelled digital transformation

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

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Business Cases | Automotive

Copyright © Capgemini 2013. All Rights Reserved

6 Connected World IoT Portfolio.pptx

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.“

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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

His

toric

al

Dat

a St

ream

ing

Dat

a (E

vent

s)

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

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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:CapgeminiConsul2ngandCapgeminiInsights&Data,Opera2onsAnaly2csSurvey,December2015

Percentageofcompanieswhichnowfocusmoreonopera2onalanaly2csthanoncustomer/frontofficeanaly2cs

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

Leve

l of I

mpl

emen

tatio

n

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


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


Low High

Strugglers

Laggards

Game Changers

Optimizers Success in Realising Benefits

Leve

l of I

mpl

emen

tatio

n

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

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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 Value Act Insight Analyze Information Provide Source 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 !  Orchestration Stream

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 Applications Business Processes Decision makers

That allows for Big, Agile and Diverse data

Data at rest Data in motion

Data Warehouse Data 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 models Business results Alerts Signals

Granular data

Events Contextual information

Analytical Sandbox

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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

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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

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Jorgen Heizenberg – Capgemini @jheizenb

Breaking Bad Data