To his credit, he did a great job of laying out the problem using data.  He showed that every business unit was struggling to hit customer satisfaction targets (as opposed to it being an isolated problem) and that their slippage was not an anomaly but part of a clear downward trend.  He also had strong data that showed that the root cause of the problem had to do with a lack of engagement among staff.  His recommendations focused on a set of initiatives to boost employee engagement.  Overall it was a reasonable report that made sense and seemed credible.

But his report was incomplete.  His data only supported the middle of his argument – that there was a customer satisfaction problem (and its causes).  He overlooked providing data on the front and back end.

I often see presentations that make broad assertions on the front end (e.g., “Changes in the economy are forcing us to rethink the way we go to market” or  “Absenteeism is a major driver of poor productivity in our department”) without any evidence.  It’s as if people treat these contextual remarks as throw-away statements that are used to ease people into the presentation.  But contextual statements are important.  They set the premise upon which an argument will be built. 

Proving that there are changes in the economy doesn’t automatically mean that your company has to rethink the way it does business.  Showing a high level of absenteeism, by itself, does not prove that you have a productivity problem (or that your productivity problem is due to the absenteeism).  Both assertions require data that demonstrate that these situations (change and absenteeism) drive an undesired outcome. 

In some cases, like with absenteeism, providing data might seem like overkill.  After all, doesn’t everyone know that absenteeism hurts business?  That’s a risky premise.  Too often such wide-sweeping generalizations are used without proper due diligence.  I’m surprised at how many leaders dig a mile deep questioning the data on how the business is performing but take these sweeping, introductory comments at face value.

Supporting your recommendations (back-end)

I’m not sure if people just run out of steam by the time they finish an analysis, but for some reason recommendations often aren’t supported by data.

For example, suppose that one of the root causes of the engagement problem is that employees do not feel that their contributions are recognized.  A common recommendation in such a case is some type of “instant recognition” program.  Such programs allow leaders to provide ad-hoc monetary or other types of rewards in the moment as opposed to through formal performance and compensation processes.

On the surface, that sounds like a data-driven recommendation.  The data says that engagement is a problem.  The root cause is recognition and therefore, the solution is a recognition program.  But from a data-driven perspective it’s missing something.  What evidence is there that the instant recognition program will solve the problem?  Sure, it’s supposed to.  It may be designed to.  But will it?

Why would the managers do this?  Has there been success with other ad-hoc programs in the past?  Are managers incented and do they have the band-width to take on such a program?  Is this the type of recognition that employees want?  Are there some leaders or departments who already do similar things with success?  Is there data from outside the organization that shows that such programs work in other places?  Are those other places similar enough from a structure, culture, etc. standpoint to use as a benchmark?

Of course, we can never know for sure if a recommendation will solve a problem.  But, we should at least have some evidence that it has a chance.  As you can see, when speculating about a recommendation, sometimes the data aren’t as robust or “hard” as those describing the problem.  However, that doesn’t mean they should be ignored.  Every recommendation should have some evidence as to why you believe it will work.  Otherwise, why did you select it in the first place?

In my work over the past few years, I’ve been impressed with how leaders are becoming more sophisticated at using data.  More seem to be willing to roll up their sleeves and dig into the numbers.  However, there is an opportunity for many leaders to expand their view.  Data shouldn’t just support the problem, it needs to support the context in which we think the problem exists and the recommendations for resolving that problem.

Take a look at your presentations.  Do you provide data to support the assertions that you make in the introduction?  Do you provide evidence as to why you think your recommendations will work? If not, you may need to do some more digging.