The amounts of ink and electrons expended on the topic of big data and predictive analytics appear to be nearly endless.* This is area also includes the evolving trends in evidence based management which posits that the best decisions can be made using the best scientific and numerical evidence available.
Whenever I read a new article that takes the reporting and analysis to a more accessible and practical level, I greatly appreciate the author(s) efforts to provide something more tangible that can readily be put to good use. Such was the case when I recently read How AIG Moved Toward Evidence-Based Decision Making by Murli Buluswar and Martin Reeves posted on the Harvard Business Review blog on October 1, 2014. Here they presented a persuasive brief on how to distill and produce genuinely productive policy insights, competitive advantages and best practices using this methodology.
To briefly summarize this piece (and I strongly urge you to click through the above link and read it in its entirety for its details and insights), while these advances data sciences can generate a multitude of patterns and trends, as well as executives motivated to apply them, there is a significant distance towards then actually applting and producing a meaningful decisions and result once an organization has armed itself with all of this numerical might. What is offered here is a compact case study and the lessons learned by AIG** in a large-scale corporate marketplace.
Two years ago, the company inaugurated a new unit called the “Science Team” which grew to include 130 people to study, implement and support evidence-based decison-making. They all come from a wide variety of specialization. This diversity has worked well in producing demonstrable and actionable results for the company. Their work is done end-to-end from conceptualizing to training to implementation.
The authors then do an expert job of synthesizing the six key factors responsible for the Science Team’s success. To paraphrase, they include identifying and prioritizing problems, defining the Science Team’s mandate, winning support, diversifying “portfolios” of projects, acting and adapting quickly, and planning for the effects of their efforts across multiple time periods. Again, please read proceed to the article to fully read the details and implications of these strategic gems.
I believe that all of these points will scale to accommodate almost any sized organization and field. For anyone currently working with evidence based decision-making or else considering an invest of time resources in them, this article make a very compelling case for proceeding and establishing best practices leading to success. That is, these six points, per se, are all highly useful. Moreover, they point towards adapting them as they appear here, then extending them further to meet an organizations particular needs and, thereafter, the critical importance of deriving your own original best evidence based practices.
* See the April 9, 2014 post here entitled Roundup of Some Recent Books on Big Data, Analytics and Intelligent Systems for just a small sampling.
** Here is the Wikipedia page for AIG detailing their corporate history.