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Deriving Real Value Out Of Big Data Analytics by Frank Keterlaas at Big Data Spain 2015

Deriving Real Value Out Of Big Data Analytics by Frank Keterlaas at Big Data Spain 2015

Big Data Analytics has started to see adoption by many customers across Europe. In this presentation, Frank Ketelaars, who is working as the IBM Big Data Technical Lead in Europe, will discuss a number of customer cases he has been involved in over the past 4 years. He will focus on patterns that have best fit a big data approach, such as 360 degree view of a customer and data warehouse modernization and how these have added significant business value to customers implementing the solutions.

Additionally, Frank will cover a few lessons learned and best practices.

Session presented at Big Data Spain 2015 Conference
16th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/fri/slot-28.html

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Big Data Spain

December 29, 2015
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Transcript

  1. None
  2. © 2015 IBM Corporation 1 © 2015 IBM Corporation Deriving

    Real Value out of Big Data Analytics Frank Ketelaars Big Data Technical Lead – Europe IBM Analytics 16 October 2016
  3. © 2015 IBM Corporation 2 We all walk past solvable

    problems and leave opportunities untapped every day TM
  4. © 2015 IBM Corporation 3 Optimizes planning with deeper insight

    into long-term traffic and usage patterns Reduces congestion by increasing visibility into traffic delays and speeding decision making by controllers Improves bus service for citizens by helping ensure buses stay on schedule The solution displays the near-real-time position of each bus on a digital city map, and can instantly drill down to live camera feeds to identify root causes. Predictive analytics generate up-to-date estimates for bus arrival and transit times. Dublin City Council improves traffic flow by using big data analytics to predict bus arrival and transit times
  5. © 2015 IBM Corporation 4

  6. © 2015 IBM Corporation 5 Start with your people to

    build a culture that infuses analytics everywhere CDO/CIO Data Science/ Developers Business Users Develop a curiosity driven workforce Move from elite few to empowered many Imagine what’s possible! Lead a data-driven transformation Fuel curiosity and creativity Innovate faster and scale securely Challenge the status quo with the thought: “what if we could...”
  7. © 2015 IBM Corporation 6 Big Data Maturity Value Operations

    Data Warehousing Line of Business and Analytics New Business Imperatives Most Businesses Are Here Lower the Cost of Storage Warehouse Modernization • Data lake • Data offload • ETL offload • Queryable archive and staging Data-informed Decision Making • Full dataset analysis (no more sampling) • Extract value from non-relational data • 360 view of all enterprise data • Exploratory analysis and discovery Business Transformation • Create new business models • Risk-aware decision making • Fight fraud and counter threats • Optimize operations • Attract, grow, retain customers Plot where you are on your journey
  8. Research and experiment  Allow people to experiment and take

    small risks  Let them fail fast and learn  Position “science” instead of “experiment”
  9. © 2015 IBM Corporation 8 Swiss Bank – Detection and

    prevention of data theft Drivers • Theft of clients personal details from a private banking organization • New regulations from FINMA around Customer Identification Data • Current systems are too inflexible • High effort for maintenance • High effort to change definitions and ingest new data • High effort for investtigations • Complete picture of CID access difficult Why “Big Data”? • Requirement for real-time alerts • Very high velocity and volume of data (TBs per hour) • No possibiity to store, then analyze
  10. Sell to Everyone in the Organization  Most of the

    times, funding must be made available by business units, not just IT  This means you have to bring value to the business  Technical advantages of analytics at scale may not immediately translate to business value  Data warehouse modernization is not a business imperative  Look for an ROI!  The ability to detect €4m of fraud that would otherwise have gone unnoticed is a clear return on investment  Start with the business problem!
  11. © 2015 IBM Corporation 10 Trading Bank Drivers • Initial

    challenge: IT problem resolving and root cause analysis of their applications is lengthy • Very distributed application • Very large volumes of web logs • Business unit need: Know more about the customers, not just what they buy and sell, but also their interests • Providing recommendations on trades “similar” people were interested in, micro-segmentation Why “Big Data”? • Very large volumes of web logs and application logs • Log files are semi-structured and also contain unstructured information • Need to access the logs in a timely manner for analysis • Finding new patterns of customer behaviour in raw log data
  12. © 2015 IBM Corporation 11 Pick the right business area

    to apply analytics and improve your core competencies Create new business models (CEO) Attract, grow, retain customers (CMO) Transform financial & management processes (CFO) Manage risk (CRO) Prioritize IT investment for innovation (CIO, CDO) Optimize operations (COO) Fight fraud and counter threats (CSO) Gain the insight to drive decisions, fuel interactions, power processes
  13. © 2015 IBM Corporation 12 Real-time analytics in Oil Drilling

    Drivers • Protect the environment • Respecting nature • Secure the “license to drill” • Increased productivity, yet respecting the environment • Reduce risk Why “Big Data”? • Very high data volumes • Data coming from multiple sources, some structured, some unstructured • Building of models is complex • Real-time monitoring on very high volumes
  14. © 2015 IBM Corporation 13 Pick the right place to

    start with your IT initiative Harness new sources of data (IoT, streaming, unstructured), and make all data available for analytics Find and integrate diverse data, and prepare it for use in information- intensive projects Derive new customer insights by assimilating information from all sources Transform from evidence- based reporting to predictive analytics Prepare data for analytics Delight customers by understanding them better Create a new data foundation for the business Predict the future for the business Derive business value from unstructured content Align data management strategy with business expectations Analyze unstructured data, and deliver to people across processes to improve decisions and reduce risk Keep operational data secure and always available
  15. Lessons learned from experiments and projects  Obtaining data takes

    time – usually more time than expected  Work towards addressing a business problem  Work closely with the business users, avoiding  Losing time and credibility  Spurious correlations  Correlations that are trivial to the business user  Envision the future  The project does not stop after the technical proof or experiement  Think about how to deploy successfully in the organization
  16. © 2015 IBM Corporation 15 Proactively set your course Identify

    the high value Scale by expanding Transform to a data-driven culture Establish the right architecture Prove value to business
  17. © 2015 IBM Corporation 16 We all walk past solvable

    problems and leave opportunities untapped every day It’s time to seize this moment
  18. © 2015 IBM Corporation 17 17 Thank you

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