You need to measure your processes. It doesn’t matter what type of process, whether it be a human process, a systems process or a manufacturing process, everything needs to be measured. In my experience, you’ll often find humans resistant to metrics that measure themselves. There’s a lot of emotion that gets caught up in collecting metrics on staff because unlike computers, we intuitively understand nuance. I’ve worked hard to be able to collect metrics on staff performance while at the same time not adding to the team’s anxiety when the measuring tape comes out. A key to that is how we interpret the data we gather.
At Centro, we practice Conscious Leadership, a methodology to approaching leadership and behaviors throughout the organization. One of the core tenants of Conscious Leadership is this idea of Facts vs Stories. A fact is something that is completely objective, something that could be revealed by a video camera. For example, “Bob rubbed his forehead, slammed his fist down and left the meeting”. That account is factually accurate. Stories are interpretations of facts. “Bob got really angry about my suggestion and stormed out of the meeting.” That’s a story around the fact that Bob slammed his fist down and left the meeting, but it’s not a fact. Maybe Bob remembered he left his oven on. Maybe he realized at that exact moment the solution to a very large problem and he had to test it out. The point is, the stories we tell ourselves may not be rooted in reality, but simply a misinterpretation of the facts.
This perspective is especially pertinent with metrics. There are definitely metrics that are facts. An example is number of on-call pages to an employee. That’s a fact. The problem is when we take that fact and develop a story around it. The story we may tell ourselves about that is we have a lot of incidents in our systems. But the number of pages a person gets may not be directly correlated to the number of actual incidents that have occurred. There is always nuance there. Maybe the person kept snoozing the same alert and it was just re-firing, creating a new page.
There are however some metrics that are not facts, but merely stories in a codified form. My favorite one is automatically generated stats around Mean Time to Recovery. This is usually a metric that’s generated via means of measuring the length of an incident or incidents related to an outage. But this metric is usually a story and not a fact. The fact is the outage incident ticket was opened at noon and closed at 1:30pm. The story around that is it took us 1.5 hours to recover. But maybe the incident wasn’t closed the moment service was restored. Maybe the service interruption started long before the incident ticket was created. Just because our stories can be distilled into a metric doesn’t make them truthful or facts.
Facts versus stories is important in automated systems, but even more so when dealing with human systems and their related workflows. Looking at a report and seeing that Fred closed more tickets than Sarah is a fact. But that doesn’t prove the story that Fred is working harder than Sarah or that Sarah is somehow slacking in her responsibilities. Maybe the size and scope of Fred’s tickets were smaller than Sarah’s. Maybe Sarah had more drive-by conversations than Fred, which reduced her capacity for ticket work. Maybe Sarah spent more time mentoring co-workers in a way that didn’t warrant a ticket. Maybe Fred games the system by creating tickets for anything and everything. There are many stories we could make up around the fact that Fred closed more tickets than Sarah. It’s important as leaders that we don’t let our stories misrepresent the work of a team member.
The fear of stories that we make out of facts is what drives the angst that team members have when leaders start talking about a new performance metric. Be sure to express to your teams the difference between facts and stories. Let them know that your measurements serve as signals more than truths. If Fred is closing a considerable larger number of tickets, it’s a signal to dig into the factors of the fact. Maybe Fred is doing more than Sarah, but more than likely, the truth is more nuanced. Digging in may reveal corrective action on how work gets done or it might reveal a change in the way that metric is tracked. (And subsequently, how that fact manifests) Or it might confirm your original story.
Many people use metrics and dashboards to remove the nuance of evaluating people. It should serve as the prompt to reveal the nuance. When you take your issue to your team, make sure you are open about the fact and your story around those facts. Be sure to separate the two and have an open mind as you explore your story. The openness and candor will provide a level of comfort around the data being collected, because they know it’s not the end of the conversation.
