Big data? No. Big Decisions are What You Want

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Presentation by Stu Miniman of Wikibon from Interop 2012 in Las Vegas, May 9, 2012.

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  • Resources:http://wikibon.org/BigData
  • Abstract: Everyone talks about big data, but big data isn’t really useful unless you can use it. What you need are big decisions. In this session, you will learn what constitutes big data, best practices to store it for retrieval, and how to use it to make business decisions. We will include a few case studies illustrating key points and provide a starting point on how to use big data to make big decisions
  • Gigabytes to Petabytes to Exabytes to ZettabytesToday’s “Big Storage” is tomorrow’s ”Little Storage”
  • Tools to understand data have been around for a long time – even in the 90’s we were learning how to “read the matrix”TRYING to do this isn’t new
  • Structured, well defined questions, typically not agile
  • Hadoop distributions include Cloudera, Hortonworks, MapRNGDW = EMC Greenplum, HP Vertica, Teradata Aster, IBM Netezza
  • High demand for new skills – gap in the workforce
  • Big names include IBM, Intel, Oracle, HP and “pure plays” like the vendors discussed on NGDW + Hadoop distribution slide
  • Everyone is familiar with websites that are crunching massive amounts of data to help provide connections/insight
  • Here’s an example from a large IT player who is “dogfooding” Big Data.
  • All your data – all sources – all locations
  • Understand your customers (ad placement, customer retention and much more)
  • Data = opportunity
  • Scalability, flexibility/extensible, robust architecture
  • Big data? No. Big Decisions are What You Want

    1. 1. Big Data? No. Big Decisions are What You Want. Stuart Miniman Wikibon, Senior Analyst stu@wikibon.org @stuThis presentation and more at http://wikibon.org/BigData
    2. 2. Big QuestionsWhat is Big Data?Evolution or Revolution of Business Intelligence (BI)?Who is Using Big Data?How Should Practitioners Proceed?
    3. 3. Massive Data Growth Source: http://wikibon.org/blog/infographics/
    4. 4. Transforming Data Knowledge BI as we know it has failed.
    5. 5. The Old Way CRM Data ETL Traditional ERP Normalized Data Data Data Data Quality Warehouse Finance Business AnalystData Warehouse Administrator Business User
    6. 6. VOLUME TYPE SPEEDBIG DATA
    7. 7. BIG DATA Process and Analyze ALL Your Data Ask NEW Questions Ask MORE Questions Get Answers FASTER Get CLEARER InsightMAKE BETTER BUSINESS DECISIONS
    8. 8. BIT FLIPSubsets All DataHistorical Near Real-timeStructured (database) Structured/UnstructuredData growth as a Data as a new source of burden & challenge competitive opportunity
    9. 9. Two NEW APPROACHES to BIG DATAHadoop is is open source framework forprocessing and analyzing massiveamounts of distributed data. Next Generation Data Warehouses use massively parallel processing, columnar architectures and data compression to analyze not-quite-so-massive data in close to real-time.These two approaches overlap in some areasand compliment one another in other areas.
    10. 10. Data Scientists10/90 rule for magnificent data success Over-invest in people, because without that investment big data will absolutely, positively, be a big disappointment for your company. Computers and artificial intelligence are simply not there yet. Hence your BFF is natural intelligence. -AvinashKaushik http://www.kaushik.net/avinash/big-data-imperative-driving-big-action/
    11. 11. BIG MONEY in BIG DATA CAGR of 58%Revenue mix today: 44% services, 31% hardware, 25% software
    12. 12. Recommendation EngineUse Hadoop to match and recommendusers to one another or to products andservices based on analysis of user profileand behavioral data
    13. 13. IT: BIaaSPredictions of FutureEquipment Failures
    14. 14. Large Media Company: BIG DATA + WAN Site 1: Advertising Site 2: Content Analysis Customization 10 Hadoop GbpsHadoop Hadoop Clusters Traffic Clusters DC1 DC2 7PB 7PB1 2 3 10 TB’s/day of source 5 TB’s/day for inter- 1 TB/day of data: browsing pattern, cluster sync results sent for click throughs, server logs integrated analysis with structured data
    15. 15. Customer Experience AnalyticsIntegrating data from previously siloed channelssuch as call center, online chat, Twitter, etc. Source: Clickfox
    16. 16. New Revenue from Data The Associated Press is combining a mix of decades of historical news releases with real-time additions to create new monetization opportunities for it’s data using a document-oriented database (rather than traditional relational database).NYSE is delivering analytics on data thatis seeing massive growth that adds up toPetabytes of information that can beoffered as a cloud service to traders.
    17. 17. BIG DATA InfrastructureNetwork optimization (low latency)Share-nothing storage – Bring the computation to the dataMassive compute requirements – Emerging opportunity in the cloud
    18. 18. BIG DATA OrganizationBroad cross-silo impact – Tight coordination needed between business decision makers and technology/analystOrganize for selling/buying data within organization and IT – Next-generation “chargeback”
    19. 19. What’s Your BIG DATA Strategy?Enterprises should … EVALUATE ENGAGE PLAN CULTIVATE EXECUTE REPEAT
    20. 20. Creating a BIG DATA IT Plan• Understand IO-centric technologies that allow near real-time big data processing• Select key vendor partnerships• Start with small projects of integrated design• Investigate opportunities to deliver big data services for your industry
    21. 21. What’s Your BIG DATA Strategy?Vendors should … LISTEN EDUCATE INNOVATE SELL SUPPORT REPEAT
    22. 22. Now Is the Era of BIG DATA Big Data is the new definitive source of competitive advantage across all industries. Special Thanks to David Floyer and Jeff KellyThis presentation and more at http://wikibon.org/BigData

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