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1 Cops & Robbers Las Vegas Style Jeff Jonas, Chief Scientist, IBM Entity Analytics Blogging at www.JeffJonas.TypePad.com.

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Presentation on theme: "1 Cops & Robbers Las Vegas Style Jeff Jonas, Chief Scientist, IBM Entity Analytics Blogging at www.JeffJonas.TypePad.com."— Presentation transcript:

1 1 Cops & Robbers Las Vegas Style Jeff Jonas, Chief Scientist, IBM Entity Analytics Blogging at

2 2 Background Founded Systems Research & Development (SRD) in 1983 Moved to Las Vegas in early 90s Assisted gaming industry in better understanding who they were doing business with (e.g., the MIT Team) Acquired by IBM January 2005 Now Chief Scientist of IBM Entity Analytics

3 3 My Living Room – December 31, 2005

4 4 Cheating Las Vegas

5 5 The Cold Deck : $250,000 Gone in 15 Minutes [Video Redacted]

6 6 More About Corporate Amnesia

7 7 Prospect Database Employee Database Human Resources Department Corporate Security Department Investigations Database Marketing department is mailing offers to a person currently in jail for stealing from you! Marketing Department Perception Isolation … Produces Corporate Amnesia

8 8 Enterprise Intelligence Requires Persistent Context The Brain!

9 9 For Example

10 10 Marc R Smith 123 Main St M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street Prospect Database Fraud Database Record #A-701 The Query Record #B-9103 Sensors Observations Consider the Query Against these Observations

11 11 Marc R Smith 123 Main St M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street The Query Record #A-701 Record #B-9103 Prospect Database Fraud Database Sensors Observations Some Observations are Discoverable

12 12 Marc R Smith 123 Main St M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street The Query Record #A-701 Record #B-9103 Prospect Database Fraud Database Sensors Observations Other Observables … are Undiscoverable

13 13 M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street FEATURES: Mark Randy Smith, M. Randal Smith 123 Main Street, DOB 06/07/74 EVENTS: Internet Inquiry Arrest Reconstructed Identities If You First Construct Context (Features and Events) Sensors Observations Record #A-701 Record #B-9103 Prospect Database Fraud Database

14 14 Persistent Context Mark FEATURES: Mark Randy Smith, M. Randal Smith 123 Main Street DOB 06/07/74 Accumulating and Persisting this Context M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street Sensors Observations Record #A-701 Record #B-9103 Prospect Database Fraud Database

15 15 Marc R Smith 123 Main St Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 Queries Now the Un-discoverable …

16 16 Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 Persistent Context Observations … After Accumulating and Persisting Context … Marc R Smith 123 Main St Queries

17 17 Marc R Smith 123 Main St FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 Queries Persistent Context Observations Enables Enterprise Discovery

18 18 Marc R Smith 123 Main St FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 Queries Persistent Context Observations Enables Enterprise Discovery

19 19 Marc R Smith 123 Main St The query could be: - A user with a question Or, also could be data: - An account opening - A new watch list entry - A background check - An address change - A vendor application - A customer inquiry Queries EXCEPT: Always Treat Data as a Query

20 20 1 st principle If you do not process every new piece of key data (perception) first like a query … then you will not know if it matters … until someone asks.

21 21 Prospect Database Employee Database Human Resources Department Corporate Security Department Investigations Database The Data is a Query Beats Boil the Ocean Midnight Batch Analytics? Marketing Department

22 22 Emile Swelter San Francisco 12/03/72 Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 ? Queries Persistent Context Observations And … Any Query can be Treated as Data …

23 23 Mark Randy Smith 123 Main Street Record #A-701 M. Randal Smith DOB: 06/07/ Record #B-9103 Persistent Context Observations Queries Emile Swelter San Francisco 12/03/72 … In Which Case the Query can Stick (Persist)

24 24 Persistent Context Notable, Stick in the Same Data Space

25 25 Question answered when it becomes true! Emilee Swelter 321 Ovington Place San Francisco 03/12/72 New Observation Persistent Context Emile Swelter San Francisco 12/03/72 Queries Now, New Observations Answer Persistent Queries

26 26 2 nd principle Treat queries like data to avoid asking every question every day.

27 27 M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street Prospect Database Fraud Database Record #A-701 Record #B-9103 Observations Persistent Context Mark FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 Sensors This is Context Construction (A Librarian Function)

28 28 M. Randal Smith DOB: 06/07/ Mark Randy Smith 123 Main Street Prospect Database Fraud Database Record #A-701 Record #B-9103 Observations Persistent Context Mark FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 Sensors ! The Ideal Moment for Enterprise Awareness

29 29 3rd principle Enterprise awareness is computationally most efficient when performed at the moment the observation is perceived.

30 30 The data finds the data … and relevance finds the user. Towards Enterprise Intelligence Introducing Perpetual Analytics

31 31 Real Technology Scalable to >3B historical observations while handling >2,000 real-time perceptions a second

32 32 Privacy and Civil Liberties – Policy Think What perceptions can or should be placed into context (in one brain)? What if someone steals the brain? What if the librarian is corrupt?

33 33 The Brain! Employee Database Human Resources Department Analytics in the Anonymized Data Space Mark Randy Smith Cd5dced41028cb …

34 34 The Main Think – Towards Enterprise Intelligence Without persistent context you have no brain Treat data and queries with equal rights More intelligence possible when thinking on streaming perceptions More or less perceptions, that is the question

35 35 Battling Corporate Amnesia is Broadly Useful National security Financial services Health care Heavily focused on threat and fraud intelligence

36 36 Cops & Robbers Las Vegas Style Jeff Jonas, Chief Scientist, IBM Entity Analytics Blogging at

37 37 Bonus Section!

38 38 If a.6% difference matters this much… … no wonder traditional information systems lack so much intelligence!

39 39 FEATURES: Mark Randy Smith, Randal Smith 123 Main Street DOB 06/07/74 FEATURES: Mark Randy Smith, Randal Smith, Randy Smith 123 Main Street, Flat 6 20 Lennox Gardens , DOB 06/07/74, Passport: Observations 6 Observations More Observations More More Observations = Better Context

40 40 Stable (e.g., Analytics with Sequence Neutrality) Data Loading Over Time Percent of Error Reload #11 Reload #12 Unstable (e.g., data warehousing which requires periodic reloads to handle data drift) Drift Sequence Neutrality is Critical for Context Stability

41 41 Cops & Robbers Las Vegas Style Jeff Jonas, Chief Scientist, IBM Entity Analytics Blogging at


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