Creating a Culture of Variance Explanation
This series of posts has identified numerous leadership traps in the form of common misuses or misunderstandings of metrics. In this post I’d like to touch on the power that can be harnessed within your teams by avoiding these traps and creating a culture of variance explanation.
Let’s start by thinking about how performance review meetings typically work. Imagine a group of people talking about the performance of a given process or function. On a big screen is a hot mess of a metrics dashboard. Very few (if any) people understand how the metrics are calculated, if or how they relate to each other, or if they actually measure the performance of the process against its goals.
People assume they know what a metric means simply from its name.
Leaders will ask questions and make assumptions, and the rest of the folks will do their best to respond. Typically, the best talkers win – by sheer rhetorical might they convince the room either that their opinion is correct, or that nobody else knows enough to contradict them.
Avoiding a “Fact-Free Environment”
This is what I call a “fact-free environment.” How could it not be? There is no understanding of the differences between Reporting and Operational Metrics, so they are all being treated equally as “KPIs.” The metrics are not constructed or organized in a way that shows how they relate to each other, or what to look at if a given metric underperforms. The metrics aren’t defined clearly or communicated, and no governance exists over those definitions to ensure changes to the calculations are vetted and understood. And of course there is no regular review of the metrics to ensure they are valid and aren’t causing unintended and counterproductive behaviors within the business. In other words, the analysis of what’s happening within the process or function is entirely ad-hoc, unguided and fundamentally uninformed. And yet in this fact-free environment the course of a business function, issues that affect the customer or even employees themselves could be decided.
We’ve already looked at some possible solutions to these issues. Let’s imagine another review meeting. In this new meeting the company leaders aren’t falling into the numerous leadership traps we have discussed. On the screen is the minimum number of Reporting Metrics required (3-5) to determine if a business function is performing. Within two seconds it becomes clear that performance is lackluster. To determine a possible cause, the people in the room begin to traverse down a well-defined root-cause tree of metrics. Because everyone knows what they are looking at (how the metrics are defined, and how they relate to each other) the discussion of possible causes is tight and focused. Rhetorical skill is not required, and certainly doesn’t win the day in the face of facts. People are arriving at the same realization of where the problem likely resides, and are now constructively asking “Why is that happening?”
We have the beginnings of something really great. People are asking “why” by looking through the window of transparent, understood metrics. You are well on your way to making variance explanation part of how your teams work every day.
Realizing the Benefits of Variance Analysis
Once your teams start to find and understand variances, they need tools and incentives to enable them to take effective corrective action. This will further embed the culture of variance analysis and move your teams towards continuous improvement as a way of doing business.
Make basic root-cause analysis tools available, taught, and used within your organization. When even simple tools, such as process mapping or fishbone diagrams, are taught and used broadly, real substantive analysis will begin to inform corrective action and improvements.
Going a step further, improvement methodologies (Lean, Six Sigma, Theory of Constraints, etc.) should also be taught and used within your organization. These are more in-depth, and likely would need to reside with fewer people (perhaps in a continuous improvement organization). These methodologies can be brought to bear on larger or more systemic issues – issues that will now become clear as the use of solid, defined and understood metrics becomes more common.
Put the performance responsibility of metrics formally with appropriate managers and leaders. I’m a fan of bonuses and other incentives being attached to anyone who can directly or indirectly affect a metric’s performance. With one caveat: if your organization does not regularly and honestly review metrics and their unintended consequences, attaching performance incentives to them can be extremely counterproductive to the performance of your organization as a whole.
Structure your review meetings so that people arrive having already done a basic level of analysis on variance. That frees the meeting up to discuss corrective actions, rather than tie everyone up in analysis. If a basic level of analysis isn’t satisfactory, time can be spent within the meeting figuring out next steps for in-depth analysis (which might include bringing an improvement methodology to bear on the problem).
And finally, get professional continuous improvement help. It’s possible to do a lot of reading and research to make all of this happen, but it won’t be as efficient or effective as having someone who has made a career out of improvement train you and your teams. Hopefully your organization has a continuous improvement team, or people who have experience in that field. If not, strongly consider building that capability in your organization.
Doing all of this will take your teams to the next level. Not only will they embrace variance explanation culturally, they will begin to move into the august territory of sustainable continuous improvement.
The Conclusion of “Leadership Traps in Business Process Metrics”
That wraps up my four-part series on leadership traps in business process metrics. In case you missed the rest of the series, here are parts one, two, and three. Have you experienced a “fact-free environment”? What other leadership traps have you encountered working with metrics? Let me know your thoughts in the comments below!