Effective data analysis comes before a decision
Perhaps the saddest decision I was ever part of was IBM selling ROLM. I was part of the analysis team-and my report on how to turn the unit around instead convinced Ellen Hancock to sell the unit in the first place, since it showed a number of areas that needed substantial work.
What was sad about the sale was that our internal study clearly said selling this business unit to Siemens would be a catastrophic failure for the unit. This should have prompted IBM to either sell to someone else or make the sale final.
Instead, IBM sold half of ROLM to Siemens and carried 50 per cent of the resulting losses for five years. The unit lost more money over that time than IBM initially paid to acquire it. Subsequent analysis showed that, had IBM just shuttered the business, it would have been billions of dollars ahead.
The ROLM mistake happened because the decision was made before the research was done. Apparently the IBM executive team forgot it had even commissioned the research in the first place. If you're going to do research, it needs to come before the decision is made afterwards, as this decision shows, it has the high probability of making executives look like idiots.
However, executives have to be able to accept that the decision they want to make-quickly divesting a troubled unit in this case-may be a bad one. If they aren't, then you still have another problem: confirmation bias, or the tendency to only see information that agrees with your world view.
We saw this play out in spades in the recent U.S. presidential election, which the Republicans were convinced they were going to win, and win big, but they lost big.
The GOP had used analytics, but not only was it using companies inexperienced in political campaigns, it was cherry picking and reporting the results that supported the belief that Mitt Romney was going to win. As a result, Republicans focused on the wrong geographies, under-resourced their efforts and lost an election against a relatively unpopular incumbent.
You can't handle the truth, so you make bad decisions
The famous courtroom scene from the movie A Few Good Men highlights the core problem: Often "the truth" is at best inconvenient and at worst highly embarrassing. Analytics, done right, provides an incontrovertible view of the truth.
There are executives and entire companies that largely exist by avoiding the truth. Look at Apple. Here's a company that was designed around the vision and skills of one man, Steve Jobs, but has done so little to adjust for his passing.
Apple is running around saying the guy who was executive of the decade really didn't play much of a role while acknowledging Apple couldn't have been successful without him. Both statements can't be true, but they coexist because Apple can't handle the truth that, without dramatic changes, the firm is crippled without Jobs.
Here's another example: a surprisingly small number of the companies that sell analytics tools actually rely on those tools for major decisions. Such companies are unable to handle the truth, even though they could become the best-standing examples of why a company should deploy their products.
This leads me to two recommendations if your executives, like most, tend to make decisions on their gut, rather than look for analysis up front. First, avoid analytics as a decision tool. It'll make executives look bad, and they probably won't appreciate that. (It goes without saying that you might want to move to a company with less foolish tendencies.)
Second, pick a solution from a company that uses analytics heavily internally, and have the company help you guide your executives to become smarter decision makers. In that case, not only will the tool be more successful, but so will the company.
It is far more satisfying to be partially responsible for making your company successful than it is to show that your executives are idiots. Trust me.
Rob Enderle is president and principal analyst of the Enderle Group. Previously, he was the Senior Research Fellow for Forrester Research and the Giga Information Group. Prior to that he worked for IBM and held positions in internal audit, competitive analysis, marketing, finance and security.
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