Technologies and Analyses in CBS’ Person of Interest


Person of Interest is a broadcast television show on CBS where a “machine” predicts a person most likely to die within 24-48 hours. Then, it’s up to a mercenary and a data scientist to find that person and help them escape their fate. A straight forward plot really, but not so simple in terms of the technologies and analyses behind the scenes that could make a modern day prediction machine a reality. I have taken the liberty of framing some components that could be part of such a project.  Can you help discover more?

CBSIn Person of Interest, “the machine” delivers either a single name or group of names predicted to meet an untimely death. However, in order to predict such an event, the machine must collect and analyze reams of big data and then produce a result set, which is then delivered to “Harold” (the computer scientist).

In real life, such an effort would be a massive undertaking on a national basis, much less by state or city. However, let’s dispense with the enormities—or plausibility of such a scenario and instead see if we can identify various technologies and analyses that could make a modern day “Person of Interest” a reality.

It is useful to think of this analytics challenge in terms of a framework: data sources, data acquisition, data repository, data access and analysis and finally, delivery channels.

First, let’s start with data sources. In Person of Interest, the “machine” collects data from various sources such as interactions from: cameras (images, audio and video), call detail records, voice (landline and mobile), GPS for location data, sensor networks, and text sources (social media, web logs, newspapers, internet etc.). Data sets stored in relational databases that are publicly and not publicly available might also be used for predictive purposes.

Next, data must be assimilated or acquired into a data management repository (most likely a multi-petabyte bank of computer servers). If data are acquired in near real time, they may go into a data warehouse and/or Hadoop cluster (maybe cloud based) for analysis and mining purposes. If data are analyzed in real time, it’s possible that complex event processing technologies (i.e. streams in memory) are used to analyze data “on the fly” and make instant decisions.

Analysis can be done at various points—during data streaming (CEP), in the data warehouse after data ingest (which could be in just a few minutes), or in Hadoop (batch processed).  Along the way, various algorithms may be running which perform functions such as:

  • Pattern analysis – recognizing and matching voice, video, graphics, or other multi-structured data types. Could be mining both structured and multi-structured data sets.
  • Social network (graph) analysis – analyzing nodes and links between persons. Possibly using call detail records, web data (Facebook, Twitter, LinkedIn and more).
  • Sentiment analysis – scanning text to reveal meaning as in when someone says; “I’d kill for that job” – do they really mean they would murder someone, or is this just a figure of speech?
  • Path analysis – what are the most frequent steps, paths and/or destinations by those predicted to be in danger?
  • Affinity analysis – if person X is in a dangerous situation, how many others just like him/her are also in a similar predicament?

It’s also possible that an access layer is needed for BI types of reporting, dashboard, or visualization techniques.

Finally, delivery of the result set –in this case – name of the person “the machine” predicts most likely to be killed in the next twenty four hours, could be sent to a device in the field either a mobile phone, tablet, computer terminal etc.

These are just some of the technologies that would be necessary to make a “real life” prediction machine possible, just like in CBS’ Person of Interest. And I haven’t even discussed networking technologies (internet, intranet, compute fabric etc.), or middleware that would also fit in the equation.

What technologies are missing? What types of analysis are also plausible to bring Person of Interest to life? What’s on the list that should not be? Let’s see if we can solve the puzzle together!

Liberal Arts or Business Degree? Or Both?

Business schools are often criticized for teaching aspiring executives what to think, rather than how to think. That’s why some professors suggest that liberal arts education is a superior choice to business school. But this argument misses the mark. What’s needed to operate in today’s complex world, is both business knowledge and a Socratic “mode of inquiry” taught through a liberal arts education.

As quoted in the April 25, 2011 issue of Financial Times, dean of IE Business School David Bach laments business students are taught what professors think they should know and then sent to conquer the world. But Bach says more than assembling and analyzing facts, students need the ability to ask the right questions. Bach states one way to reach this goal is through a liberal arts education where students gain “a way of looking at the world, a mode of inquiry” instead of time tested memorize and regurgitate approaches. Bach claims we’d be better off spending time training executives to make connections, problem solve and communicate—all skills the liberal arts education seems to afford.

In fact, as Bach alludes, executives need both business acumen and “different perspectives” in order to make sense of the world. As executives swim in deep seas of too much data, increasingly they will require content and facts parsed in manageable chunks (perhaps via MapReduce or other type of analytic engine) to help provide distilled fodder for overwhelmed and overworked human brains. Then, analytical tools can help discern correlations and patterns not clearly evident and also highlight information of critical importance requiring further drill-down analysis.

In addition to algorithmic engines and other technologies, to succeed in an unpredictable and complex global environment, executives will need to pair business acumen with modes of inquiry. Today, business managers can take data and information that tools have sorted and prioritized and then utilize these findings to challenge existing assumptions, seek new and different approaches, and apply creative thinking to tasks at hand.

A cartoon from the Financial Times shows two executives having a conversation with one quipping; “I left business school knowing all the answers. They didn’t tell me the questions would change.”

Indeed, in dynamic global economy, the questions, much less the right answers change every day, and sometimes in microseconds. Business and economic facts are important, but what’s really necessary are technological engines to help sort, order and then analyze the monstrous data flood that invades our daily lives.

The use of technology to harness the big data flood coupled with a questioning mind can then help make sense of the world today, and also intelligently predict with a high degree of probability what’s coming next.