Capgemini Insights and Data

Capgemini Insights & Data
Not time to waste: From Data Warehousing to Modern
Data Architecture in 4 easy sprints
April, 13 2016 – Hadoop Summit
Andrea Capodicasa & Hessel Miedema

2Copyright © Capgemini 2015. All Rights Reserved
Insights & Data: An Introduction | Version 1.0
Bio
• Andrea Capodicasa
@acapo_tweets
andrea.capodicasa@capgemini.com
• Hessel Miedema
@hessel_Miedema
hessel.m.miedema@capgemini.com

Pick a first use case that makes sense
 Global CPG
 Marketing use case
 2 geographies
 Social Media data
 Primarily streaming data
 POS Data
 Brand Master Data
Advanced visualisations
Real Time Campaign
monitoring
Natural language
processing
Statistical modelling

How to get there in 4 easy sprints
Social sensing
capability
PoS, supply chain
,market share
and brand equity
data
Open data;
demographics,
weather,
population etc.
Statistical
methods, network
graphing and
machine learning
Create the business plan building on the results of the first 4 sprints;
Designing the service model with demand and supply processes,
and business transformation management.
Select and implement big data architecture and tools,
using proven design accelerators,
and robust analytics platform hosting
Analytics and data science
Technology enablement
Operating model design and implementation

Take your business users on a journey, stay connected
Physical Consumer insight centres are the core of the operating model

A different use case – Data Warehouse modernisation
Large European Telecom operator
• ~15 million customers
• ~5€B turnover p.a.
• ~10.000 employees
• ~500 direct points of sales
• ~1000 After sales service centers
Initial status vs final results :
High development & maintenance Delay and Cost 3 different approaches for analytics (industrial, Agile and Prototype)
Important data silos with difficulties to cross analytics between business units New analytical assets and incremental value created
Multiple DWH &~350 datamarts all over the company A unified Data Platform & a complete decommissioning of old systems
Many, many business analysis managed in “shadow IT” mode
(x100s SAS tables, XLS sheets …)
No more specific and unmanaged data extracts

How to make the transition successful
 How to ensure decommissioning of the legacy infrastructure, up to shutting down the hardware & software?
 How to build trust around the new system so that our users move to the modern architecture?
 How do we ensure our users will get more value out of the new platform, long term?
 How do we avoid ending up with a “data black hole”?
THE “KILL”
STRATEGY
Objective: successfully
decommission legacy BI
infrastructures
THE USER ADOPTION
STRATEGY
Objective: transition the
analytical services & users to
the new system
THE DATA CONCIERGE
Objective: 1+1=3 and getting
more value, long term, out of
the new platform vs. the old
infrastructure
Rationalized costs, lower TCO, simplified landscape, agile business

Why do you need a “kill strategy”?
 “Unmanaged” data and analytics assets are part of the scope to migrate
• The scope of the migration will evolve during the project when undocumented assets or
dependencies will be discovered.
 Users of the legacy systems don’t want any impact on their daily activities, they
are required to deliver KPIs and numbers, they fear functional regression
• There is an important “trust” factor to build for the new system that requires facts – not
impressions – on the quality of the new system clearly communicated.
 Discrepancies will exist in the data produced in 100% of times, making it then
impossible to compare “before” and “after” functionalities and therefore difficult to
prove “functional equivalence” of the new system.
• Acceptance criteria must be defined in advance at the beginning of the project to agree on
the decision rules to accept the decommissioning of the old system.
This will help ensure (/enforce) that the costs savings you are hoping to do by
rationalizing your data landscape will indeed be realized
THE “KILL”
STRATEGY

The key activities of a “kill strategy”
 Putting measurable facts & KPIs in place to define what “functional
equivalence” means
 Define the acceptance criteria
 Defining constraints in the “kill roadmap” that are as much on the IT side as on
the business side
 Define the best migration roadmap
 Accepting that there will be surprises along the way around unmanaged
queries, interfaces and adherences, and setting up the right governance to deal
with it at the appropriate level and manage evolutions
 Set up the specific project management and governance stream
 Preparing the ground with the management board for the potential impacts on
the strategic KPIs they are used to receive, getting the full buy-in and support of
the management board to be the decision maker at the final shut-down
 Set up the board level communication plan
THE “KILL”
STRATEGY

Why do you need a “user adoption strategy”?
 The new system is bringing new tools, habits, behaviours and ways of working that
your analysts are not familiar with
• The value of the new system only starts when users are fully operational on the new system
and comfortable with these new habits
 Moving to a new data platform is a complex process, any problem or failure have a
tendency to go viral
• Communicating the progress and first successes is as important as the successes
themselves, to start building the trust in the new system.
 Lack of agility and data silos are the #1 pain of legacy systems. Make the first projects a
total success by enabling users to get quickly what they need
• Using datalabs approach on data assets already provisioned in the lake will enable your users
to “see” the potential of a rationalized data platform where data assets are easily shared
This will ensure that the value creation you are expecting
from a next-generation data platform will indeed be realized
THE USER
ADOPTION
STRATEGY

The key activities of a “user adoption strategy”
 Define your communication strategy at all appropriate level to diffuse the new
and good behaviors
 Set up the Communication plan
 Identify any skills gap and define the appropriate trainings for all users
population types (power users, interactive users, consumers)
 Define the Training plan for tools and data domains
 Understand that around the new data platform you are in fact creating a user
community that can work together to enhance value creation without
bottlenecks
 Set up the Business & IT champions network
 Foster new behaviors and new use cases by using exploratory approaches,
allowing users to mature business needs as needed, and “try new things”
 Set up the Data lab approach
THE USER
ADOPTION
STRATEGY

The kill strategy and user adoption go hand in hand
One cannot succeed without the other
Strong user adoption strategy
- End users understand the new
value they will get out of the new
system
- They are empowered to use it
- Their success is spreading to new
initiatives
- They forget all about the old &
slow stuff fairly quickly
Weak user adoption strategy
- End users fear the new system
will impact their capacity to do
their jobs
- The Known is safer than the new
- First tests on the new systems
disappoint, any failure goes viral
- Evolutions still run on the old
system, “just in case”
Strong kill strategy
- Systems are killed according to
roadmap, costs linked to unused
HW & SW are recovered
- IT & Business impacts are
anticipated, managed and
communicated
- The energy is focused on the
new
Weak kill strategy
- First systems are shut down
ignoring business constraints,
impacting operations
- Endless hours spent to compare
the old and the new and explain
differences
- Unprepared board escalations
when unplanned impacts arise
THE USER
ADOPTION
STRATEGY
THE “KILL”
STRATEGY

Key challenges when moving to large scale
data & analytics platforms
 New tools & analytical techniques proliferate
 Demand for new data assets become intolerable in a classic governance set up
 Even using agile delivery methods, the user stories backlogs are getting longer
and longer
 Deploying new services is taking too long
DATA CONCIERGE
Industrialize and automate data provisioning processes as much as possible
Provide a simple, business-oriented information catalog of all data assets available
Provide a simple and managed way for business users to go “self service” where possible
Use intelligent processes for proactive optimization & recommendation
THE DATA
CONCIERGE

Compressing the time to value, standardizing the
cost to insight
 Business Information Catalog Services
• Repository, search and recommendation services for
business meta-data
 Ingestion Services
• Loading data in appropriate perimeter with corresponding
SLA and on-demand / self-service features for the business
 Distillation Services
• Structuring and providing the business with the information
they need in the right view
 Data Science and Analytics Services
• A bespoke service for data science & analytics with multiple
insights delivery models
 Data Operations Services
• On-going management and support of the data assets
including optimization, quality and governance
Industrialized
Automatized
Agile
Intelligent
THE DATA
CONCIERGE

The Data Concierge services mapped on the EDH architecture
Data Lake
Distillation Layer
Usage Layer
ODS
Applications
Analytics & Data Science
Industrial, certified
Perimeter
Experiment
Perimeter
Self service
Perimeter
Business Information Catalog
Operations
MDM Transformation Aggregation Transformation
Aggregation
Transformation
Aggregation
Governance
Governance
Corporate
view
Local
view
..Sandbox
spaceN
Sandbox
space1
..Sandbox
spaceN
Sandbox
space1
Sources
Ingestion Services
Distillation Services
Data Science
& Analytics Services
Business Information
Catalog Services
Data Operations
Services
Data domains
Data domains
Data domains
Data domains
Data domains
Data domains
Data domains

The information contained in this presentation is proprietary.
Copyright © 2015 Capgemini. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.
www.capgemini.com
About Capgemini
Now with 180,000 people in over 40 countries, Capgemini is one
of the world's foremost providers of consulting, technology and
outsourcing services. The Group reported 2014 global revenues
of EUR 10.573 billion.
Together with its clients, Capgemini creates and delivers
business, technology and digital solutions that fit their needs,
enabling them to achieve innovation and competitiveness. A
deeply multicultural organization, Capgemini has developed its
own way of working, the Collaborative Business Experience™,
and draws on Rightshore®, its worldwide delivery model.
Learn more about us at www.capgemini.com.

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