Since I got the TED app on my iPad, I have had a chance to watch a lot more videos while cooking. I love cooking as some of you may know and I particularly enjoy multi-tasking in the kitchen.
This weekend, Damon Horowitz got my attention. “Data is Power” was a sure way to get it of course. He took a slightly different angle though. Damon did not focus as much on why this premise is true and relevant but he dared raise moral considerations. Without weighting too much on his personal beliefs, he describes different approaches that could be considered for a moral framework. Like most TED talks, this is pleasant and entertaining talk with some nuggets of food for thought. How appropriate when you are cooking!
[youtube=http://www.youtube.com/watch?v=nG3vB2Cu_jM]
Damon is absolutely right though – data is power. With data we can infer a lot of information that can help improve decisions. This is the basis for Analytics of course:
- Business Intelligence can shed light on patterns of behavior or categorization
- Predictive Analytics can tell you who is likely to commit fraud and who might be seduced by a promotion
Beyond the technology, Analytics have disrupted decision-making processes throughout the enterprise. As you may know, Bill Fair and Earl Isaac pioneered credit decisioning back in the mid 1950’s. Back to Damon’s focus on a moral framework, people may have mixed feelings about the principle of scoring individuals and the data used to do so. The fact is, as Larry Rosenberg explained to us in the past, Credit Scores have enabled access to credit to populations that fell into the grey area before. By improving our predictions on the odds of payment, we can improve the conditions for those bad apples that happen to be in a bad basket. There is some good here. There are also other nasty scenarios. If Good vs. Bad was an easy discrimination, Damon would not have had to discuss the topic on stage.
Besides the transactional data we use for decisioning throughout the customer life-cycle, there is also now social data available. It can be used for fraud detection. One typical use case is the detection of fraud rings based on social affinity. Although it makes a lot of sense to contribute to the eradication of fraud if that is possible, one might fear the abuse and unintended consequences. Damon’s comparison of the Utilitarian versus Civil Right perspective here exemplifies that there is no obvious Right versus Wrong. We have opportunities enabled by the technology but we also need to consider the edge cases and how to deal with them to avoid the pitfalls along the way. That, in my mind, will lead to more ad-hoc exceptions in which you will want to empower the Human, the Expert, to make that judgment call when the time comes.
I must admit I did not realize until the last year or so that there was more data to Decision Management than the data we look at for the actual decision. Whether the expert is weighting in at the time of processing or when he/she dumps his/her expertise into the automated system, there is environmental data that bias our perspective. Based on our personal experience, we may not always think outside the box or not 100% objectively. Those concerns are quite well expressed in this other TED talk I recommend.
Eli Pariser brings to our attention a scary perspective on the unintended effect of personalization. He stresses the danger of uncontrolled automated filters on news and information that eventually specialize too much, potentially keeping critical details hidden. Personalization was our tagline coming into year 2000. There is tremendous value here for sure: productivity gains, advertisement, etc. Eli found extreme cases though that ended up providing a biased picture of the topic at hand in the quest for information.
[youtube=http://www.youtube.com/watch?v=B8ofWFx525s]
Whether we consider that this automated personalization and the derived SEO techniques & services has a significant impact or not, we can’t ignore the point that Eli is raising. This is going to become a more important issue for Decision Management as the discipline expands to manual decisions where getting the right information at the right time matters. Mixing fact-based performance metrics with human-centric judgment seems to make sense. There may be more solutions to that problem though. I believe we are in the infancy of those metaphysical considerations on technology and morals.
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