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Analytics, monetization and how to avoid the Big Data swamp

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How do you ensure the data you have is helping your organization achieve its strategy? Learn how your journey doesn’t start with technology, it starts by understanding the decisions you need to make as an organization, and then ensuring you can capitalize on the explosion in available data sources and volumes. Let's open up the discussion – please join our panel discussion to understand how to monetize your data.

Naeem Sarwar
Head of Analytics, Intelligent Enterprise, Business and Application Services, EMEIA

Christian Benson
VP, Head of Intelligent Enterprise and Applications Transformation, EMEIA

Albert Mercadal
Head of Analytics EMEIA, Intelligent Enterprise, Business and Application Services, EMEIA

Publié dans : Technologie
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Analytics, monetization and how to avoid the Big Data swamp

  1. 1. 0 © Copyright 2017 FUJITSU Fujitsu Forum 2017 #FujitsuForum
  2. 2. 1 © Copyright 2017 FUJITSU Analytics, monetization and how to avoid the Big Data swamp Christian Benson VP, Head of Intelligent Enterprise and Applications Transformation, EMEIA Fujitsu Naeem Sarwar Head of Analytics UK&I, Intelligent Enterprise, Business and Application Services, EMEIA Albert Mercadal Head of Analytics EMEIA, Intelligent Enterprise, Business and Application Services, EMEIA
  3. 3. 2 © Copyright 2017 FUJITSU All Business Models are Changing Embracing and Enabling Digital, transforms organizations and business models.
  4. 4. 3 © Copyright 2017 FUJITSU Achieving Successful Digital Transformation Mastering Business Innovation Mastering Wellbeing & Compliance Mastering Customer Experience Enabling Digital Mastering Enterprise Productivity Shaping a better future - together Experience moments that matter Operational excellence through new ways of working Protecting people, reputations and revenues
  5. 5. 4 © Copyright 2017 FUJITSU Wave 1: Data Warehousing Data Management Data Warehouse Operational Data Store Data Delivery Data Delivery ECTML Exploitation Warehouse Data Mining Warehouse OLAP Data Mart Operational Data Mart Operational Systems Financial LOB Operational Sales Getting Data In Getting Information Out
  6. 6. 5 © Copyright 2017 FUJITSU On Becoming a Data Graveyard  Built for Reporting and ‘BI’  Slow build, laborious Modelling  Slow Refinements  Limited time to Value  Always out of date
  7. 7. 6 © Copyright 2017 FUJITSU Wave 2: Enter the Dragon Data Lake  Built for Analytics  Limited Modelling  Rapid Refinements  Short time to Value  Real Time Feeds and Analyses social media monitoring churn analysis profitability modelling customer profiling regulatory compliance reporting continuous planning financial controls management threat modelling forecasting regression analysis optimization budgeting fraud prediction segmentation retention planning propensity modelling sentiment analysis dashboards machinelearning operational risk management correlation analysis resource optimization on line recommendations scenario modelling demand forecasting kpi management predictive analytics virtual assistants scorecards costanalysis clusteranalysis adtargeting Application Interactive Web and Mobile Applications BI / Reporting, Ad Hoc Analysis Enterprise Applications Hadoop Governanceand Integration Data Access Data Management Security Operation Data Systems Sources OLTP, ERP, CRM Systems Documents and Emails Web Logs, Click Streams Social Networks Machine Generated Sensor Data Geo-location Data Statistical Analysis
  8. 8. 7 © Copyright 2017 FUJITSU On the making of a Data Swamp  Poorly-defined purpose  Lack of definition of desired analytics  “Model nothing” mentality  Variable data quality  Challenging navigation  Invest on Use Cases without considering ROI “Without descriptive metadata and a mechanism to maintain it, the data lake is turning into a data swamp. And without metadata, every subsequent use of data means analysts start from scratch.” Source: Gartner “Beware the Data Lake Fallacy”
  9. 9. 8 © Copyright 2017 FUJITSU Wave 3: The Enterprise Data Marketplace  Clearly defined purpose  Clear definition of desired Analytics  Governed and Managed  High levels of data quality  Usable Metadata  Rapid Delivery  Flexible and Adaptive Adapted from: M Social Media IoT Devices and Sensors Log and Clickstream Data Enterprise Applications Mobile Applications Bots Streaming Ai and ml Cloud storage Data warehousing Hadoop Business Users Data Driven Applications Business Decision Makers IT Professionals Data Analysts Data Scientists
  10. 10. 9 © Copyright 2017 FUJITSU Monetizing your Data Assets New revenue streams through new Business Lines, costs reduction through efficiency gains and increase revenue thanks to improving services. CDO reporting to the CEO and Advanced Analytics as a cross- enterprise capability. Defray costs of enterprise Information Management and Business Analytics. Impress investors; improve market-to-book corporate valuations. Enable competitive differentiation. Became a Platform through strengthening partner, supplier and customer relationships
  11. 11. 10 © Copyright 2017 FUJITSU Analytics as the Engine of Information Monetization To grow revenues at scale focus needs to be supporting clients derive business value through better and more frequent decisions from 1st party data combined with additional data assets. Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics How can we make it happen? What will happen? Why did it happen? What Happened? Difficulty Value Imperative! Inertia In order to lead the transformation from Descriptive to Prescriptive analytics within the organization it is necessary to track and evaluate all the roadmap:  Data Value KPIs: how data relates with Business Goals.  Financial KPIs: which is the ROI, NPV of the different initiatives.
  12. 12. 11 © Copyright 2017 FUJITSU11 Let us help you get there Business Requirements Delivering ValueQuick Win Enabling Analytics Detailed Vision and Roadmap Use case discovery through identifying opportunities for increasing revenues and/or gain efficiency Identify a Quick Win within the organization in order to deliver value in the short terms Detailed roadmap to guide investment and articulate working streams to scale Analytics across the organization Analytics as a core capability: embedding analytics on the exiting and new processes and services
  13. 13. 12 © Copyright 2017 FUJITSU Fujitsu Sans Light – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüýþ ÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüý þÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans Medium – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúû üýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl