Measurement programs today have a lot in common with shopping trips to a big box grocery store before a dinner party. You can stroll the aisles and buy lots of different food products, but whether they’ll end up as a tasty meal pretty much depends on your personal culinary abilities.
If you’re doing anything having to do with measurement these days, chances are about 100 percent that you’ll end up with a large pile of data. Problem is, most measurement platforms are great at spewing out lots of data points, but very bad at helping you interpret it. Never mind discover any insight.
I’ve spent decades helping clients find insight in many, many grocery stores full of data. So here are a few expert tips on how to turn all that data into a yummy meal full of insight and recommendations.
#1. First things first: What changed?
The last thing that senior leadership wants to see is a list of things you did. They want to know what you changed: What did you do for the organization that helps it achieve its financial and/or strategic goals? So, throw out that task list and start sifting through your data for signs of change.
#2. Look for the “So what?” and the “Now what?”
To continue the grocery store analogy, raw data is what is on the shelves. It’s the unprocessed information that reflects what happened at a point in time. It is only useful if you can use it to answer “So what?”
In other words, “So what if you found farro on your grocery shelves?” Sure, it is an ancient mixture of wheat species that is full of protein. But if I tried to get you to buy it, you’d probably say “So what? What can I make with it?” If you look up a recipe, or read the one on the back of the package, you’d know that it makes a great summer salad with cucumbers and tomatoes and basil. That’s the “So what.”
Say you did a survey to find out how your employees were viewing your new organizational changes. Your package of farro is like the raw data that you got back from your employees.
So what? Suppose that, at first glance, your survey results were pretty good across all demographics. That’s a start, but hardly a tasty tidbit. So, you say “So What?” by taking a deeper dive into the worst performing cohort and look for some anomaly.
Perhaps it turns out that the worst-responding cohort was women who had worked at the company ten or more years. And, contrary to expectations, the most recently hired employees—those millennials you are worried about—were the most positive about the changes. So, what does that mean? Perhaps:
- The newly hired are fresh out of well-managed orientation meetings, so they “get” the whole concept;
- The managers of long-standing employees assume they’ve already “got” the messages, and so don’t try very hard; and
- Long-time female employees have suffered the latent sexism of years of frustrating non-promotion, and so are more skeptical of promised changes.
These are the sort of Ah ha! moments you’re looking for.
Now what? Once you have your data analyzed you need to put together the “Now what?” in the form of recommendations as to what needs fixing. Do you need to set up a task force to identify the root cause of a problem, or is better communications the key? Once you have that answer, your full meal is ready.
And if you can’t come up with a “Now what?” then don’t use the data. You don’t want to waste your audience’s attention on something they don’t need to act on.
#3. Adapt your presentation to your target audience.
Put yourself in the head of the boss of the person who requested the report. Ask yourself what type of person he or she is:
- Are they a word person, like a former journalist, editor, or PR person, who is most comfortable getting descriptive data?
- Are they a numbers person, who will want just the facts and numbers and maybe a chart or two?
- Are they a more visual person, who will need to see data in graphs and word clouds?
And if you can’t figure out what kind of person they are, then make sure you use all three forms of presentation in your report.
#4. Find the story that is most interesting to your audience.
All numbers are not equally valuable. Too often I’ve worked with clients who insist upon presenting all possible variables in a study. Ultimately, I only let them present the ones that are the most interesting.
For example, I recently analyzed a huge volume of employee survey data that had traditionally been analyzed from the perspective of the gap between two of the variables. We ranked all the elements by the size of the gap, since the common wisdom is that the bigger the gap in people’s perceptions, the lower the credibility of the managers in question. That analysis revealed some large gaps among certain groups. But the problem was that everyone expected those groups to perform badly, because they were the “problem children” who always ranked towards the bottom of studies like this. And they also consistently performed the best from a financial perspective. So that data was easy to set aside.
Instead I focused on a different perspective on the data: the percent of respondents who had not heard any of the messages and had not seen anyone taking action to achieve the company’s goals. We discovered that more than half the employees didn’t even know what the company’s goals were—a statistic that was lost in the gap data. This new perspective proved to be of great interest to senior leadership.
#5: Use your data to illustrate your story.
Regardless of what your data is, your job is to make it tell a story. You wouldn’t serve your guests a meal of nothing but white food on white plates; that would be the ultimate bland and unappetizing meal. That’s just what data can be if you don’t use it correctly in charts and graphs.
Whoever your audience is, they will need some illustrations to understand your points. This is where the charts and graphs come in. But with one enormous caveat: Never include a chart or graph that doesn’t reinforce your message, no matter how interesting the data may seem to you.
During a typical presentation, you’re probably going to get through four maybe five total slides—if you’re lucky. Make good choices. Use your slides to make your biggest points, and make them count.
If you have a lot of information to present in one slide, instead of using tiny type on a chart, substitute graphic icons or conventions. For instance, use images to represent men vs. women, or colors instead of words like “positive” or “negative.” Then put the key or the explanations at the bottom of the slide.
If your data represents changes over time, make sure you add a bubble over any spike or dip to explain what was going on at the time. This will save you endless questions.
#6: Don’t drown in your data.
Beware the hypnosis that can result from looking through a large pile of data. Make sure you end up with conclusions and recommendations.
Anyone who has ever had a hankering to cook knows that it is easy to spend hours pouring through recipes and finding the perfect ingredients. At some point, you’re going to have make up your mind what you’re going to serve. Analyzing data isn’t that different. I can happily analyze data for days trying to suss out that one salient nugget of knowledge that I know the client needs to hear. But at some point there is a deadline, and I have to decide which nugget of good data I’m going to serve up.
Do not start with your big successes, start with the flops. Take your results and sort them from worst to best, lowest to highest. Now take a good long look at what didn’t work and figure out what you’ve learned from it.
#7: Start your presentation with an elevator pitch.
The big difference between gourmet cooking and data is that almost everyone loves to eat, but very few people love to rummage through data. Compounding the problem is that, in my experience, the higher up in organizational leadership you go, the more likely it is that you’ll find people with the attention span of a two-year-old and the patience of a puppy. (No offense to any senior leadership team members that might be reading this.)
So don’t feed them the data. Don’t even feed them the long story of what the data says. Start by feeding them the essence of the story.
Distill the story that your data tells into a simple 30-second elevator pitch. Hit them with that first. Because, as soon as their attention starts to wander, your presentation is over. Even if you’re still talking, you’ve lost your audience. So feed them your best while they are still hungry. ∞
