I have a confession to make. Despite my role as Trilix’s resident data evangelist, I am uncharacteristically NOT data-driven in my approach to getting dressed every morning.

Like many of you, I have a weather app on my phone. And I even look at it occasionally. But when I’m figuring out what to wear for the day, my logical process goes something like this:

  1. Look out the window. Is anything falling out of the sky? If so, dress accordingly.
  2. How was it yesterday in terms of temperature? Dress based on that. Over-correct if I happened to be wildly off the day before.
  3. Go outside, and unless I immediately realize I was way off, declare “good enough” and proceed with the day.

Now if I lived somewhere like, say, Arizona, such behavior would be acceptable. But this is New England, and it’s currently autumn. It’s a time and place where the climate is notoriously volatile. It’s not unheard of to have a 30-degree day sandwiched between two 60-degree days.

This is obviously a foolish way to go about selecting attire. I mean, a weather forecast for my specific neighborhood is RIGHT THERE on my phone. And yet, this is how many of us proceed with business decision-making.

The problem is, I was using a lagging metric—yesterday’s weather—instead of a leading metric—today’s forecast. If that’s all I had to work with, that would be defensible. It’s much better than looking at the weather from a quarter ago, or else I might be kicking fallen leaves around in sandals and shorts. It’s moderately better than dressing based on how it was last November, which might have been a milder autumn season. And it’s exponentially better, for both comfort and fashion, than selecting articles of clothing from my drawer at random. But if I had a better feel for what was going to happen today, I could modify my actions accordingly.

Related Reading: Building a Data-Driven Culture

Lagging indicators are driven by results, whereas leading indicators are driven by behavior. Many of our KPIs tend to be lagging, for several reasons. Lagging metrics are typically more universally understood and non-controversial. Want to know how your sales numbers were last year? Look at sales revenue. They also tend to be more easily obtained. I need a meteorologist to tell me what the weather is going to be like today, but if I want to know how the weather was yesterday, I can just ask anyone who left their house. It’s not rocket surgery.

Leading indicators are more actionable, and therefore more powerful, but they present some challenges. Let’s say you’ve decided to set and track a goal for each member of your sales force to make 20 calls per day. This is a solid leading indicator because it tracks behavior rather than results – calls that don’t lead to closed deals count just as much as successful calls. If anyone on the team is struggling to meet those numbers, you can take action and correct course BEFORE the quarterly sales report comes out. But because the link to revenue is not as obvious, it can be more easily dismissed by detractors. Think of how often you hear people complain about meteorologists when they get a forecast wrong! The key is to build consensus around your approach, making sure the connections are clear between the behaviors that ultimately drive your desired results.

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This is not to say that lagging metrics ought to be dismissed out of hand, but the two should complement one another. After all, most people will be able to see the direct correlation between a lagging indicator and what actually occurred. Use your lagging indicator to verify and validate the hypothesis of your leading indicator. Using the sales example, compare last quarter’s revenue with the number of calls. Look at the numbers closely, and see if trends emerge between the two. If you find that your top salesperson is making fewer calls, dig deeper. Perhaps you’ll find that increased sales revenue is less correlated with call volume and more correlated with lead-to-opportunity conversion. You can sacrifice a little on the lagging-to-leading spectrum in the name of accuracy.

Think of your company’s most significant KPIs. How many are lagging, and how many are leading? For any lagging indicators, is there a leading indicator you can think of that tracks the behavior driving those numbers?

Do you have a data strategy that tracks the right metrics? Does your team embrace a culture that approaches data with an open mind? If you’re interested in discussing building a data strategy that aligns with your business goals, join us at Innovate Newport on November 8 for a free workshop about building a data-driven culture. We hope to see you there!

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