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CWIN17 san francisco-thomas dornis-2017 - Data concierge-The Foundation of a Digital Business

Data concierge-The Foundation of a Digital Business

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CWIN17 san francisco-thomas dornis-2017 - Data concierge-The Foundation of a Digital Business

  1. 1. Data Concierge – The Foundation of a Digital Business Thomas Dornis San Francisco, CA – December 7th 2017 #CWIN17
  2. 2. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 2 Table of Contents  Introduction/ Insights Drive Organization  Data Concierge Approach  Balancing Business Value with Industrialized Capabilities
  3. 3. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 3 Becoming an Insight Driven Organization – Intelligent Enterprise A fact-driven, highly industrialized quick-scan of your insights & data portfolio, giving you all you need to make informed, value-driven decisions about the next steps of your insight-driven journey.  A high-speed, metrics-driven 6-12 weeks intervention that delivers a business case, to-be design and roadmap for key portfolio areas:  BI Modernization  Business Insights Service Center/ Data Concierge  Big Data/ Data Science  Cognitive & AI  Sector and Domain Analytics What We Do The Value  Client sees many opportunities to leverage insights & data, for example around Big Data, Modernizing the BI Landscape, creating advanced analytics and exploring Cognitive & AI  Client has trouble in making decisions, being hesitant about the financial impact, the technology choices, the to-be design and most feasible next steps to make Challenges & Opportunities  Multiple years of tried & tested approach, delivering solid results in a short timeframe, facilitating decision-making  Dedicated and specialized Center of Excellence in Bangalore  Collaborative process, involving all key stakeholders from day 1  Tools-supported, compelling visual report outs
  4. 4. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 4 Key transformation challenges for large scale data & analytics platforms  End user profiles/ requirements vary widely across the organization  Demand for new data assets become intolerable in a classic governance set up  User stories backlogs are getting longer and longer even when employing Agile methods  Deploying new services is taking too long  New tools & analytical techniques proliferate DATA CONCIERGE Industrialize and automatize 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 & recommendations
  5. 5. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 5 Compressing the time to value, standardizing the cost to insight  Business Information Catalog Services  Repository, search and recommendation services for business meta- data  Data Operations Services  On-going management and support of the data assets including optimization, quality and governance  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  Use Case Catalog Services  Repository of solutions/ use cases that have been implemented with business value and impact to bottlers  “Art of the Possible” Industrialized Automatized Intelligent Data Concierge Framework
  6. 6. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 6 Data Operations Services Data Concierge: Business Information Catalog Services  Business description of datasets, structures & services  Through a web-based portal, users can search for data assets within the lake, and use recommendations provided by the tool; shopping cart approach for data assets  Communicate data assets characteristics  Ownership – IT & Business champions in charge of the data assets & contact info  Perimeter & SLAs – industrial/certified, experiment, self-service (shareable)  Access – which user population have access to this data asset, type of access  Current status of accessibility/usability within the lake  The data lake governance instances periodically review and curate the additions & modifications made to the catalog  Curation of data assets  Governance of self service & experiment perimeters – discard data assets when initiatives are finalized  Major communication tool to support user adoption Ingestion Services Distillation Services Data Science & Analytics Services Business Information Catalog Services Use Case Catalog Services
  7. 7. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 7 Use Case Catalog Services Data Concierge: Data Operations (Governance) Services s Intelligence behind the Data Concierge, masking complexity to the user • Logs data assets search requests by user population • Builds recommendations for data assets searched and used by similar populations of users • Manage access rules between stakeholders (sharing of information, use etc.) • Logs access to datasets, structures & services, distillation & transformation processes • Detects anomalous search & access behaviors • Apply quality & governance rules for data assets, structures & services • Publish data assets and structures to perimeter, deploys services • Enable & publish data lineage for each service • Propose schema/structure optimization within a Data Hub/ Business Data Lake structure • Cluster performance monitoring and optimization recommendations Ingestion Services Distillation Services Data Science & Analytics Services Business Information Catalog Services Data Operations/ Services
  8. 8. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 8 Use Case Catalog Services Business Information Catalog Services Data Operations Services Data Concierge: Ingestion Services  “Industrialized” Ingestion Services  Support multiple modes of ingestion: • Real-time streaming • Micro-batch • Batch • Replication  Govern choice of software vendors: Data Integration Platform, API Services, Open Source tools  Service Ticket driven approach  Users request a system and data asset that they didn’t find in the catalog  They receive a defined response time for loading  Stage Key Data Sources  80/20 Rule: 80% of analytics requirements are driven by 20% of data  Stage common/ most used data sources to “seed” the data lake Ingestion Services Distillation Services Data Science & Analytics Services IngestionServices Events Web API File App API Adaptor RDBMS StreamingBatchSOA/EAI CDC TechnicalMetaData Audit DataLake
  9. 9. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 9 Use Case Catalog Services Ingestion Services Business Information Catalog Services Data Operations Services Data Concierge: Distillation Services  Conversion of RAW data into usable data  Users request data assets to be converted into SQL data stores with the right view for their needs  Users can also request Sandboxes for investigation and analytics  Users can also request Excel and interactive reports dashboards  Users receive a defined response time for deployment  Incorporate MDM and X-Ref data to create single views of given domains (re-usable components)  Aggregate massive data sets down to manageable results volumes  Includes the provisioning, and de-provisioning of distillations on an automatic or scheduled basis Distillation Services Data Science & Analytics Services Master Data & X-ref Transformation Aggregation Data Lake Distillation Layer Usage Layer Extraction SQL SandboxSQL Excel Provisioning
  10. 10. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 10 Ingestion Services Distillation Services Business Information Catalog Services Data Operations Services Data Concierge: Data Science & Analytics Services  Question-based Data Science  “Can we improve the forecast for next years sales? (12+ month view not in SAP/ APO?)”  “What are the primary drivers for missed deliveries?”  “Can we predict factory downtime?”  Multiple delivery modes available depending on complexity and business users’ autonomy  Users can request to be fully autonomous, or work in integrated team, or request a fully delivered service  For an integrated team or fully delivered service, a CoE Data Science team can work collaboratively to define the problem space, data requirements and definition of the outcome required  A fixed price is then provided for the ‘proof of value’  Once the model has been proven it can then be industrialized via the Ingestion and Distillation Services  The different delivery modes can be used as a framework to progressively ramp up the end users on the new system  Enabling new data usages and new tools: • Initial use cases are delivered in integrated mode; formal delivery by CoE or similar structure • Over time, stakeholder may deliver their own use cases  Use of data lab approach and exploration to help users get familiar with the data assets available and the functions of the new system vs. legacy Data Science & Analytics Services Use Case Catalog Services
  11. 11. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 11 Data Concierge – Use Case Catalog Ingestion Services Distillation Services Business Information Catalog Services Data Operations Services Data Science & Analytics Services Use Case Catalog Services Existing Use Cases New Use Cases+ Improve Insights Catalog Industrialize PoC Filter and Eliminate Scale & Communicate
  12. 12. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 12 Data Concierge – Use Case Catalog Advanced Analytics/ Data Science Example (CPG) Connected Assets & Service Recommendation • Analyze customer usage pattern • Recommend optimal services offer • Real-time Asset control Product Design Analytics • System Reliability Modeling • Intelligent target setting & allocation • Cost & Weight Analytics • Approximate models using CAE/Test Data • Product Benchmarking EngineeringDirector Supplier Risk Analytics • Supplier quality analysis & Risk Driver Identification • Quality & Risk Scoring • Rationalization & optimal selection EngineeringDirector Factory Analytics • Machine Performance & Control • Energy Consumption Analysis • Predictive Machine Maintenance • Stochastic demand & supply planning Head-Manufacturing Asset Performance & Control • Performance Analysis • Segmentation • Adaptive Control Limits • Real-time monitoring Head-Operations Predictive Maintenance • Correlation of events/usage to failures • Root cause analysis & driver identification • Failure Prediction & Recommendation Explore failure anomalies Head-Operations CMO Advanced Planning & Scheduling • Analyze disruptions & operational impact • Stochastic demand & supply planning of resources (e.g. assets, manpower, services, etc.) Head-Operations Service/ Issue Analytics • Financial budgeting & reserving • Claims & supply side optimization • Recall & product improvement modeling • Coverage & pricing strategy formulation CMO&CFO Service Optimization • Dealer/service provider performance analysis • Predict the profitable customers who may sign/renew services contracts • Service price optimization CMO • Data Science “Applications” to address specific use cases • Contributions by entire system: • Business Units • Corporate • CoE • Vendors/ Integrators • Shared Service manages the industrialization/ scaling • Apply when ready – driven by stakeholder maturity Development Approach:
  13. 13. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 13 Balancing Business Value & Industrialized Capabilities End State Quick Wins Ideal Architecture Corridor of balance BusinessServiceValue Capability Value Score- carding Catalog of Services Training Organisation and Roles routerequest Customer Business Partner Engagement Support Engagement Core Advanced Custom Interpretation Internal delivery Walk up or Ad- Hoc Engagement Walk up Template Capability Centre Person/screen Capability Centre or Cluster Capability Centre or Cluster Brief Demand Management Supply Management Brief EngagementEngagement Projects EngagementProject Plan Data & Technology Allocation Resource Allocation Governance Delivery Prioritisation Enterintodemandbacklog Service Selection Brief Type Delivered By Business Question Defined Delivery Plan Delivery Processes SUBSCRIPTION PAY-AS-YOU-GO PAY FOR FLEX RESOURCES Funding
  14. 14. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 14 Thank You! Phone: +1 (520) 661-7333 thomas.dornis@capgemini.com Thomas Dornis NA Leader – Information Strategy Insights & Data Speaker 1 Photo
  15. 15. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 15 Appendix
  16. 16. CWIN San Francisco 2017 – Data Concierge| December 7th 2017 Copyright © 2017 Capgemini. All rights reserved. 16 Data Concierge: Services Mapped to the Data Hub/ Business Data Lake 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 Use Case Catalog Services

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