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Capgemini Insights and Data
1.
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
2.
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
3.
3Copyright © Capgemini
2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
4.
4Copyright © Capgemini
2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
5.
5Copyright © Capgemini
2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 Take your business users on a journey, stay connected Physical Consumer insight centres are the core of the operating model
6.
6Copyright © Capgemini
2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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2015. All Rights Reserved Insights & Data: An Introduction | Version 1.0 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
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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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