IBM IOD Conference 2011 Opening Keynote Deck

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Opening keynote at the IBM Information On Demand (IOD) Conference, Las Vegas, 2011. 12,000 peeps.

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  • 10/25/11
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  • IBM IOD Conference 2011 Opening Keynote Deck

    1. 1. Big Data. Deep Analytics. New Physics. The Journey from Enterprise Amnesia to Enterprise Intelligence Jeff Jonas, IBM Distinguished Engineer Chief Scientist, IBM Entity Analytics Email: [email_address] Blog: www.jeffjonas.typepad.com Twitter: http:// www.twitter.com/jeffjonas
    2. 2. State of the Union: Enterprise Amnesia
    3. 3. <ul><li>Amnesia , definition </li></ul><ul><li>A defect in memory, especially resulting from brain damage. </li></ul>
    4. 4. Enterprise Amnesia , definition A defect in memory, resulting in missed opportunity, wasted resources, lower revenues, unnecessary fraud losses, and other bad news.
    5. 5. Trend: Organizations Are Getting Dumber Time Sensemaking Algorithms Available Observation Space Computing Power Growth <ul><li>Structured data </li></ul><ul><li>Unstructured data </li></ul><ul><li>Social media </li></ul><ul><li>Cyber audit logs </li></ul><ul><li>Geospatial data </li></ul>Enterprise Amnesia
    6. 6. Data Volumes Exploding “ Every two days now we create as much information as we did from the dawn of civilization up until 2003.” ~ Eric Schmidt, CEO Google
    7. 7. Trend: Organizations Are Getting Dumber Time Sensemaking Algorithms Available Observation Space Computing Power Growth WHY?
    8. 8. Algorithms at Dead End. You Can’t Squeeze Knowledge Out of a Pixel.
    9. 9. No Context [email_address]
    10. 10. <ul><li>Context, definition </li></ul><ul><li>Better understanding something by taking into account the things around it. </li></ul>
    11. 11. Information in Context … and Accumulating Top 200 Customer Job Applicant Identity Thief Criminal Investigation [email_address]
    12. 12. The Puzzle Metaphor <ul><li>Imagine an ever-growing pile of puzzle pieces of varying sizes, shapes and colors </li></ul><ul><li>What it represents is unknown – there is no picture on hand </li></ul><ul><li>Is it one puzzle, 15 puzzles, or 1,500 different puzzles? </li></ul><ul><li>Some pieces are duplicates, missing, incomplete, low quality, or have been misinterpreted </li></ul><ul><li>Some pieces may even be professionally fabricated lies </li></ul><ul><li>Until you take the pieces to the table and attempt assembly, you don’t know what you are dealing with </li></ul>
    13. 13. Puzzling 270 pieces 90% 200 pieces 66% 150 pieces 50% 6 pieces 2% (pure noise) 30 pieces 10% (duplicates)
    14. 16. First Discovery
    15. 17. More Data Finds Data
    16. 18. Duplicates in Front Of Your Eyes
    17. 19. First Duplicate Found Here
    18. 22. Incremental Context – Incremental Discovery <ul><li>6:40pm START </li></ul><ul><li>22min “Hey, this one is a duplicate!” </li></ul><ul><li>35min “I think some pieces are missing.” </li></ul><ul><li>37min “Looks like a bunch of hillbillies on a porch.” </li></ul><ul><li>44min “Hillbillies, playing guitars, sitting on a porch, near a barber sign … and a banjo!” </li></ul>
    19. 23. 150 pieces 50%
    20. 24. Incremental Context – Incremental Discovery <ul><li>47min “We should take the sky and grass off the table.” </li></ul><ul><li>2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.” </li></ul><ul><li>2hr10m “Wait, there are three … no, four puzzles.” </li></ul><ul><li>2hr17m “We need a bigger table.” </li></ul><ul><li>2hr18m “I think you threw in a few random pieces.” </li></ul>
    21. 28. How Context Accumulates <ul><li>With each new observation … one of three assertions are made: 1) Un-associated; 2) placed near like neighbors; or 3) connected </li></ul><ul><li>Must favor the false negative </li></ul><ul><li>New observations sometimes reverse earlier assertions </li></ul><ul><li>Some observations produce new discovery </li></ul><ul><li>As the working space expands, computational effort increases </li></ul><ul><li>Given sufficient observations, there can come a tipping point. Thereafter, confidence improves while computational effort decreases ! </li></ul>
    22. 29. Big Data [in context]. New Physics. <ul><li>More data: better the predictions </li></ul><ul><ul><li>Lower false positives </li></ul></ul><ul><ul><li>Lower false negatives </li></ul></ul><ul><li>More data: bad data … good </li></ul><ul><ul><li>Suddenly glad your data was not perfect </li></ul></ul><ul><li>More data: less compute </li></ul>
    23. 30. Enterprise Intelligence One Plausible Journey Enterprise Intelligence How to Get From Here to There
    24. 31. Observation Space Sense and Respond What you know New Observations
    25. 32. Observation Space Sense and Respond Decide ? Relevance Finds the Sensor (<200ms) Data Finds Data
    26. 33. Explore and Reflect Observation Space Data Finds Data Sense and Respond Decide ? Directed Attention Relevance Find You Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms)
    27. 34. Observation Space Directed Attention NEW INTERESTS Data Finds Data Explore and Reflect Sense and Respond Decide ? Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms)
    28. 35. Observation Space Data Finds Data Directed Attention Explore and Reflect Sense and Respond NEW INTERESTS Decide ? Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms) ILog Netezza BigInsights Cognos InfoSphere Streams
    29. 36. Report and Manage Explore and Reflect Sense and Respond Observation Space Decide ? Directed Attention NEW INTERESTS Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms) Data Finds Data Info Management Systems
    30. 37. Content Management Case Management Data Warehousing Report and Manage Observation Space Decide ? Directed Attention NEW INTERESTS Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms) Data Finds Data Info Management Systems
    31. 38. Observation Space Data Finds Data Identity Insight/Sensemaking SPSS Directed Attention Explore and Reflect Sense and Respond NEW INTERESTS Decide ? Deep Reflection Curated Data Pattern Discovery Relevance Finds the Sensor (<200ms) ILog Netezza BigInsights Cognos InfoSphere Streams
    32. 39. Closing Thoughts
    33. 40. <ul><li>The most competitive organizations </li></ul><ul><li>are going to make sense of what they are observing </li></ul><ul><li>fast enough to do something about it </li></ul><ul><li>while they are observing it. </li></ul>
    34. 41. Wish This On The Competition Time Sensemaking Algorithms Available Observation Space Computing Power Growth Enterprise Amnesia
    35. 42. The Way Forward: Enterprise Intelligence Context Accumulation Time Sensemaking Algorithms Available Observation Space Computing Power Growth
    36. 43. Big Data. Deep Analytics. New Physics. The Journey from Enterprise Amnesia to Enterprise Intelligence Jeff Jonas, IBM Distinguished Engineer Chief Scientist, IBM Entity Analytics Email: [email_address] Blog: www.jeffjonas.typepad.com Twitter: http:// www.twitter.com/jeffjonas

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