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Posted by Mike Gilpin on May 12, 2013
Ever wonder how Las Vegas casinos catch card-counting teams at Blackjack tables, like the MIT team immortalized in the film “21” with Kevin Spacey? They use many techniques, some of which are confidential, but one we know about is their use of Entity Analytics on many intersecting streams of information about their patrons or potential employees. I recently had the chance to learn more about Entity Analytics and Big Data from one of the top industry thought leaders, Jeff Jonas of IBM.
Jeff Jonas Kevin Spacey in 21
This opportunity came when Marcel Jemio, Chair of the Fiscal Service Data Stewards at the US Treasury Dept. (and a Forrester client), invited me to a presentation Jeff gave at a special internal event at the Fiscal Service in Washington, D.C. So of course I leapt at the opportunity! Marcel opened the session with an overview of why Treasury is interested in data and analytics: Treasury is charged with helping the nation guard against the kind of national or global financial collapse that triggered the 2007-2009 recession. Therefore it’s crucial that the stewards of the nation’s financial data, like Marcel and his colleagues, continuously improve the insights we gain from this data.
This data is more connected and interoperable all the time, across multiple public and private sector organizations with common goals. Making key insights from this data available more openly, but securely, increases transparency and visibility of potential issues to key decision makers in government and commercial enterprises. But to link all this related data, to gain these insights, requires the Fiscal Service to leverage global industry data standards to gain deep insights into integrated information. If you can’t link and reuse data, it’s much less valuable!
A Lifetime Spent Linking Data Together
Jeff invented a really cool way to link information called NORA (Non Obvious Relationship Awareness), and he’s implemented it multiple times on different platforms in different eras since the first version in the mid 80s, through the course of his career as an innovator, scientist, and entrepreneur. Jeff also found time to found Systems Research & Development (SRD), later acquired by IBM in 2005, which is how Jeff ended up as IBM Fellow and Chief Scientist of the IBM Entity Analytics Group at Watson labs.
Vegas casinos used a version of NORA to bring down that MIT card-counting team led by Bill Kaplan, through surveillance of live Blackjack play. Casinos routinely aim to connect eighteen or more different lists of people who are known to have defrauded or otherwise attempted to take advantage of the casinos, and they do this through the kind of entity matching that NORA enables. For example, I may connect information about a person who was arrested for a crime under an alias, with a person applying for a job as a croupier under her real name, because they both have the same phone number. Jeff also helped intelligence agencies deal with the “connect the dots” problems they faced after 9/11.
Making these connections requires an integrated view across observations from many different sources, including structured, semi-structured, and unstructured data, and even advanced sources such as video with facial recognition. Jeff pointed out that one flaw in conventional business intelligence tools is that they require smart people to ask smart questions, and only then can these tools give answers. There’s no way your organization has enough smart people to ask all the right questions all the time, so you need analytics that find relevant connections and bring them to your attention, telling you things you would otherwise never have known, such as the connection between the arrest record and the croupier’s job application. Entity Analytics are also quite valuable for developing richer "views of the customer" as well as for householding and other techniques crucial to success in the era of Digital Disruption.
Jeff used a story about jigsaw puzzle pieces to convey a powerful metaphor for linking information and observations. He has used groups of people assembling jigsaw puzzles to conduct experiments that reveal important insights about the way humans’ analytical thinking enables them to link pieces together to make a picture, just as analysts want to link disparate observations together to form a cohesive picture of an intelligence threat, to find a perpetrator after a bombing, or even to learn enough about you to make you offers that you just can't refuse. But Jeff's presentation happened the week before the Boston Marathon bombing, and when that happened I wondered what role NORA’s descendants might have played in analyzing video feeds and finding the bombers.
Unfortunately, Jeff sees many organizations getting dumber about their data – the algorithms they have developed to help them make sense of their data are not growing and innovating fast enough to keep up with the flood of new data from new sources, such as location data, which is a potential source of deep new insights. He calls this gap “enterprise amnesia,” and told the story of retailers that have been known to hire associates who were previously arrested for shoplifting – from the same store location!
Lessons Jeff Learned From A Lifetime Of Linking Data Together
This emerging picture should inform your collection efforts – you might need to obtain a new information source to follow up a lead from an earlier analysis, or to discard an information source (and the cost of collecting and analyzing it) once you realize it’s not helping.
One of the most interesting new sources of Big Data insights is data about the interactions of people with systems – even their mistakes! That’s how Google knows to ask “did you mean this?”
So as adversaries get smarter and more capable of avoiding detection all the time, savvy analysts must continually push the edge of the envelope of applying new techniques and technology to the game.
How To Stay Ahead Of The Game
Jeff pointed out that location data presents tantalizing new possibilities for insight. There are 600 billion location records created every day in the US alone! This data is being routinely de-identified and shared with multiple third parties, in volume and in real time, and it’s amazing what you can figure out from it. Consider the example of Malte Spitz, who as an act of political protest over his privacy concerns sued Deutsche Telekom for release of his location records. They revealed that over six months, he “hung out” 2400 times at 130 unique places. Know three of those locations – home (sleeps at night), work (goes in the daytime), and pub (goes to meet friends – links to other trails of location data) and I can tell you who the person is, despite the anonymized data – and who his friends are.
Although there’s a strong trend toward analyzing data in memory and delivering insights in real time – to inform “sense and respond” systems – don’t imagine that the world is going all real-time. Instead, Jeff advises that you should view batch approaches to analysis as an important complement, as delivering “periods of reflection” that can deliver insights that you can then use to improve the accuracy and usefulness of the model that drives your “sense and respond” systems. Jeff labels these two sides of the analytical world with catch phrases: “sense and respond“ (relevance finds the sensor) vs “explore and reflect” (relevance finds you). Jeff advises we use both sides together, which should inform future architectures for doing advanced analytics.
In contrast, today we do analytics in stovepipes – we have one set of algorithms to analyze structured data, different algorithms for unstructured data, and still more (different) algorithms for social data! Jeff believes that in the future we must take a more integrated approach to analytics, with algorithms that reason over datasets that mix all types of data, and link them all. It’s only through this broader view that we can do what casinos do, and catch the bad guys while they are still playing Blackjack.
What This All Means For You
Below find my take on how you should act upon Jeff Jonas’ insights, but I also urge you to engage with Forrester’s analysts who spend every waking moment thinking about business intelligence, Big Data, and the potential for deeper business insights that these and other innovations can bring: Boris Evelson, Martha Bennett, Mike Gualtieri, Noel Yuhanna, Michele Goetz, Brian Hopkins, and others. In my view:
This opportunity to integrate multiple sources of insight is too important to our business success, good governance, and security, to let it go by. Be sure you enhance your strategy for analytics and business intelligence to exploit the opportunities that Jeff Jonas’ research and innovation shows us are real and compelling.
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