I have a short piece up over at CIO.com about how some of the difficulties candidates have in selling detailed polices mirror the troubles that can arise when trying to push an analytic initiative forward in the business world, and that the solution there, as here is to focus on the results:

Certainly, the challenges that bedevil candidates in trying to sell analytic policies are the same ones that people trying to implement analytic initiatives in the business world often face: Because numbers convey a sense of certainty, how do you use analytics without appearing to know more than you often do, while still clearly communicating the value of analytical thinking?

In order to get there, people who want to bring analytics into their organization need to understand how to talk about the value of analytics to an audience which may not be interested in the behind-the-curtain details.

Effectively conveying the importance of analytics is a worthy cause, but trying to win over the uninitiated shouldn't be done to the exclusion of thinking about where we're going from here. As I mused in the last bit of the piece on CIO.com:

Politics has been on the forefront of some interesting cultural and business revolutions. Polling, cinematic television advertising and relentless "messaging" have all been part and parcel of successful political campaigns. The analytics tools exist to extend into the policy beyond the politics, and it may not be far off.

We've already stated to see the beginnings of such a change in Health Care with the advent of "Evidence-Based Medicine" and I expect it won't be long before the arrival of other areas where it's simply expected that decisions will be based upon the best available evidence.

As with most things, the public sector will likely lag the private, where we can already see the evidence- based methodology appearing in some novel places. For instance, Fog Creek software makes a bug tracking product which incorporates "Evidence Based Scheduling," a very clever method for producing reliable estimates for software development from developers who work at different rates and have different levels of accuracy in their own forecasting.

As for public policy, we already see this move to some extent in the debate over global warming, with both sides of the discussion arguing over the details of research and what it implies for policy decisions (this debate shows how important it is to have good data and lots of it, if you want to avoid the situation where the same data can be used to tell contradictory stories). I have a feeling that as the tools, and in particular, the data sources continue to develop, we'll see much more of this in the future.