When President Obama successfully ran for the office as a clear underdog both the first and second time, and won largely by effectively using analytics to match his messages to voters and better target his fund raising, I had a lot of hope that politicians would begin to actually understand this powerful analytics tool. However, those same skills weren’t evident when Obama entered the office and they certainly haven’t been evident with either his opponents or his potential successors.
The whole point to using analytics is to base decisions on the results, take a broad look at the data, use an analytics engine to advise on the best course of action, and then follow that advice. Given how powerful this was in Obama’s efforts, why don’t we see this practice used more often in elections and, more importantly, in decisions made by government?https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=iLet’s talk about that this week.
Flawed Political Decision Process
In politics, and way too often in business, it is less about actually fixing the problem than it is about sticking it to the other side. The goal for both sides is to prevail and neither side has any real focus on fixing the problem. For analytics to work, you have to define the problem and focus the tool on that, otherwise you are simply using the tool to prevail in an argument and convince folks with evidence that you are right when you actually may not be. It is this core problem, the desire to choose an outcome before doing the analysis, that makes analytics very dangerous in politics.
You see, the reason analytics worked in the presidential election efforts was that the goal was pristine: It was to win an election. But as soon as Obama was elected, the use of analytics fell off. This was, I expect, because often the result was a result that the administration didn’t like. You see, the right answer isn’t always right or left, it is simply the right answer.
For instance, analytics was used on the Affordable Care Act to prove that the result was affordable, but they left off the costs for mental health care for the youngest group, which broke the model. When the researchers found this out, they didn’t disclose that result because they’d have looked stupid. It didn’t get fixed. If you are wondering why your insurance costs are going up around 30 percent this year, chances are that is a good chunk of it.
In politics, and sadly in business, it is often safer to look like you are doing something than to actually be getting anything done. You can’t be blamed for a bad outcome if you didn’t change anything, and you can argue that you were right if the outcome from no change is bad.
In thinking about this, I came up with what I think is a guideline for analytics I call the Three Laws of Analytics.
The Three Laws of Analytics
Thanks to all of the robotics news, I have Asimov’s “Three Laws of Robotics” on the brain. I see a parallel with analytics:
- Define the problem to be solved by looking at the root cause, not focusing on the symptoms.
- Don’t corrupt the data: The adage of “garbage in, garbage out” applies.
- Follow the information to the answer; don’t form the answer and then force the information to fit it.
Wrapping Up: The Real Dangers of Poor Analytics Use
What is beginning to concern me is that analytics is a powerful tool but, if misused, it could be a powerful tool to assure bad decisions. Our tendency is to make decisions before we have the data and then use tools like this to defend that decision rather than using analytics to make the decision in the first place. And the tendency to use corrupted information to formulate plans to punish those who disagree with us, particularly if they are right, is frightening.
So remember the “Three Laws of Analytics,” or simply remember that the answer should follow the analysis, not define it.
Our jobs, heck our lives, may depend on the actions of decision makers who are more likely to misuse this powerful tool than use it correctly. Finding a way to prevent that is likely in all our best interests.
Rob Enderle is President and Principal Analyst of the Enderle Group, a forward-looking emerging technology advisory firm. With over 30 years’ experience in emerging technologies, he has provided regional and global companies with guidance in how to better target customer needs; create new business opportunities; anticipate technology changes; select vendors and products; and present their products in the best possible light. Rob covers the technology industry broadly. Before founding the Enderle Group, Rob was the Senior Research Fellow for Forrester Research and the Giga Information Group, and held senior positions at IBM and ROLM. Follow Rob on Twitter @enderle, on Facebook and on Google+