Is Managing by Historical Data a Myth?

Today’s guest post on The Dashboard Spy is by Allan Wille, President and CEO of Klipfolio.

Myth: Managing by historical data is like driving while looking in the rear view mirror.

Catch Quote: “Managing through the rear view mirror. A dangerous practice for any business! You’d never drive only looking backwards.”

You’ve heard lines like this one before…it has all the right ingredients to be remembered and retold. It’s catchy, it paints a clear picture, adds a dash of FUD (fear, uncertainty, and doubt), and may even be confused with true wisdom!

Let’s debunk this.

Let’s frame the story by discussing measured data, environmental factors, and how decisions get made.

Measured data (we discussed measures, metrics and KPIs in this blog post, or measurements are values associated with an agreed upon standard (such as km/h). For example, I’m driving at a speed of 50km/h. This is a “real-time” measurement, but it could have been; Yesterday, my top speed was 120km/h, or my average trip speed is 35km/h for the past 30 minutes. Measured data is historical no matter how you spin it. However, knowing when the data was measured is a critical factor that often gets left out of the “rear-view mirror” picture.

Let’s say you’re driving in an unfamiliar neighborhood. You know from experience (historical data) that on a street of this size, with children playing outside, you should probably not exceed 40km/h. Ahead, a road-sign warns of a school zone: 25km/h max. This is new data, and it will cause you to adjust your speed.

In that example, the sign is an environmental factor. When you’re making business decisions, environmental factors might include competitive threats, economic opportunities, or regulatory changes. A simple way to differentiate might be to think of these as the external factors – the O and T of your SWOT analysis. Don’t confuse environmental with a sudden snow storm…although that too, should cause you to drive differently.

Provided you’re not flipping a coin, decision making is, of course, much more involved. Past experiences (measured historical data) are combined with cognitive and personal biases (know any folks at your office?), environmental factors, and time constraints to influence the choices people make. Read up at Wikipedia: Interesting stuff.

I suspect that much of where the rear-view mirror warning comes from has to do with using an appropriate timeframe. My speedometer tells me how fast I’m going now, not yesterday – yes, that would indeed be dangerous. Is that the issue?

Ideally, and this is possible using today’s dashboards and BI software, you would have the following decision support at your disposal:

1. Current performance (real-time or near-real-time)
2. Timeframe-appropriate historical performance data
3. Predictive or conditional alerts (based on goals, history, or environmental factors)

Let’s add a pre-emptive note about predictive analytics – a wonderfully powerful tool to assist with future decision making. Keep in mind though, that predictive models exploit patterns found in historical and transactional data to identify risks and opportunities for future decision guidance.

Nobody has a crystal ball. However, a keen understanding of your history, an understanding of trends and cycles, awareness of your environmental factors, and smart, analytical thinking will help you predict what’s around the next corner.

About the author:

Allan Wille is President and CEO at Klipfolio Inc. An expert in operational data visibility and performance dashboards, Allan helps global enterprises to simplify and grow KPI usage across the workforce to drive higher performance and profitability.

Allan is responsible for the strategic vision, direction and operations of Klipfolio. He is an evangelist of the company’s vision to make operational metrics easier to use.