Capgemini Insights and Data

Capgemini Insights & DataNot time to waste: From Data Warehousing to Modern Data Architecture in 4 easy sprintsApril, 13 2016 – Hadoop SummitAndrea Capodicasa & Hessel Miedema

2Copyright © Capgemini 2015. All Rights Reserved

Insights & Data: An Introduction | Version 1.0

Bio

• Andrea Capodicasa

@acapo_tweets

[email protected]

• Hessel Miedema

@hessel_Miedema

[email protected]



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 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



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 sharedThis will ensure that the value creation you are expecting

from a next-generation data platform will indeed be realized



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 kill strategy and user adoption go hand in handOne 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



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 possibleProvide 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 possibleUse 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

IndustrializedAutomatized

AgileIntelligent

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

ExperimentPerimeter

Self service Perimeter

Business Information Catalog

Operations

MDM Transformation Aggregation Transformation

Aggregation

Transformation

Aggregation

Governance

Governance

Corporate

view

Local view

.. Sandbox

space N

Sandbox

space 1

.. Sandbox

space N

Sandbox

space 1

Sources

Ingestion Services

Distillation Services

Data Science & Analytics Services

Business Information Catalog Services

Data Operations Services

Data domainsData domainsData domainsData domains

Data domainsData domainsData domains

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