“Engagement has consistently fallen for three years.”
This statement was one of three pieces of evidence that I provided to support my recommendation that we needed a new employee engagement strategy.
I thought it was a pretty straightforward. I continued my presentation to talk about our new ideas for engagement. But, the senior executive stopped me.
“Wait, where’s the data?” he asked.
“What data?” I didn’t think I had made a particularly contentious point.
“The data that shows that employee engagement has been consistently falling for the past three years.”
“Do you mean a graph? OK, here you go.” I pulled out a line graph showing employee engagement. Its line was on a steady decline for the past three years.
Satisfied, the executive said, “OK, go ahead.”
I happened to know this executive pretty well. We had talked in the past about how he could improve at being “data-driven”. So, I paused.
“Can I ask you something?”
“Sure.” He could tell something was up.
“What is the likelihood that I would have misinterpreted that graph?”
“Pretty low. It’s a simple and obvious point.”
“Do you think that if engagement was going down, I’d intentionally or accidentally tell you it was going up?”
He laughed. “No, of course not.”
“Were you surprised by what I said?”
“No. I knew that it had gone down the past two years and based on the interim feedback we’ve gotten this year, I expected it to be down again.” I could tell by the expression on his face that he knew what was coming.
“So, you knew that I could interpret the graph, you didn’t think I’d intentionally or accidentally misstate the results, and what I said was exactly what you expected based on other data you’ve seen. So, why did you need to see the graph?”
He was silent. I could see the wheels turning in his head. Finally, he said, “I guess I’m just used to asking to see the data.”
Now was my chance to make my point.
“And that might be one of the reasons that your team struggles to drive decisions. You spend too much time rehashing things everyone already knows and don’t get to the discussion about what to do about it.”
He knew I was right but didn’t want to go down without a fight.
“So, what I am supposed to do? Just believe everything anyone tells me?”.
I actually hear that a lot. Many people think that their only choices are to accept things blindly or drill down on every point.
“Of course not, that would be both irresponsible and ineffective. But, you can be more productive in how you ‘check’ the data.”
If you have a burning need to rehash data to make sure that the person doing the analysis didn’t screw up or miss anything, you need a different person analyzing the data (or they might need a new boss).
Or, if you are going to go back and redo it anyway, just ask for the data and stop wasting everyone’s time.
There is no point in having someone else do the work if you are going to redo it anyway. That’s not efficient. And, it’s incredibly un-empowering.
Most importantly, it grinds decision-making to a halt. If you are talking about numbers, you aren’t making decisions.
“Auditing” the data and analysis moves the conversation backward. It rehashes what others have already done.
More importantly, it takes the focus off of the decision. As soon as the executive asked to see the graph, we were not longer focused on solving the engagement problem. Instead, we were just revisiting the fact that engagement went down.
There is another option which allows you to move the conversation forward while still performing your due diligence.
Focus on challenging people’s assumptions, thinking, and conclusions.
If there is a problem with the data, you’ll still find it. But, in the meantime, you’ll actually move the conversation forward rather than just churning through past work.
So, how can you do your due diligence without rehashing the analysis? Try these five things.
1) Focus on what is surprising, what you don’t know, or what is opposite of what you expected. Based on previous data, the executive in the earlier example knew that engagement was going down. My point was totally consistent with what the data already said. There was no reason to drill into the data. If the data tells you what you already know (note: they key word is “know” which means that it is consistent with other data, not just with your opinion), move forward. If it conflicts with your current understanding, drill down using the additional tips below.
2) Don’t think about data in a vacuum. The presentation or report you are seeing didn’t appear out of midair. It’s part of an on-going story of your business. You’ve been part of that story. Don’t treat the current presentation as if it’s the only source of data on the topic. You’ve heard other reports and seen other presentations. Take advantage of that to make the story more complete.
3) Test your (and the other person’s) understanding and assumptions, not the data. Suppose that you hear a recommendation to focus marketing spend on your Northeast Business unit. However, for the past several months, you’ve been reading about lagging sales in the Midwest. Instead of asking to see the data, challenge the assumption:
“I thought that our sales were hurting more in the Midwest. How did that compare to the Northeast?”
“Why do you think we’ll get more benefit from focusing on the Northeast as opposed to the Midwest?”
Both of those questions will provide more insight than rehashing the data.
By asking to see the data, you move the conversation backward. By challenging the conclusion, you move the conversation forward and improve the quality of the decision. In addition, if there was a mistake in the analysis, it will quickly become apparent.
4) Clarify your decision criteria before and during the presentation. Make sure that the person doing the analysis understands the questions (criteria) that you are using to make your decision. Otherwise, they won’t bring the right data to the table. If you hear a recommendation that doesn’t make sense, instead of driving to the data, ask for the questions/criteria that were used in coming up with it. Make sure you are on the same page on the criteria before you worry about the data.
5) Stop believing that your decision-making process is substantially more complex, sophisticated, or better than everyone else. Most people are overconfident and very inaccurate about the quality of their thinking processes (see The Overconfidence Effect: http://www.psychologyconcepts.com/overconfidence-effect/).
In addition, many people overestimate how thoroughly they actually analyze data. I once had an executive tell me that he needed to review the whole spreadsheet because he looked at and analyzed a lot more data than did his team. I watched him review the spreadsheet. He ran his finger down ONE column stopping at every item highlighted in red (missing target). I pointed that out and he said, “Oh, I guess I’m not using as much data as I thought”.
There is always a chance that you will discover an insight that everyone else missed. But that’s like playing the lottery because you could win. Most of the time you won’t. If your team is competent, they’ll get the analysis right 99% of the time. The value you gain on the few occasions where you discover something new won’t offset all of the rest of the time wasted repeating their analysis and coming to the same conclusion. And, your value doesn’t come from spotting a number on a graph that people might have missed. Your value is bringing business context to the graph’s insights to help drive decisions.
Rehashing someone else’s analysis is inefficient, ineffective, and demonstrates a lack of trust or confidence. If you don’t trust someone or think he or she can do the job, get someone else. If you believe that you have the right person for the job engage him or her in a meaningful, critical conversation. You will both wind up in a much better place.
Brad Kolar is an executive consultant, speaker, and author with Avail Advisors. Avail’s Rethinking Data workshop will help your leaders make more efficient and effective data-driven decisions. Contact Brad at email@example.com.
 These are two very important caveats! Don’t try this at home!