Metrics_ How to Improve Key Business Results - Martin Klubeck [15]
Bonus Material
I fear I may have misled you. By presenting this chapter in the manner I did, you might wonder if I’m leading you down the wrong track. But there was a logical reason for it. I considered presenting the definitions from the top-down order that I’d like you to address them: root question to metric to information to measures and, finally, to data. But I worried that readers would rebel. This is not the normal order in which we come to metrics. Unfortunately, our normal journey to metrics starts with requests for data.
Figure 1-8 depicts the hierarchy between the components, from the bottom up.
Figure 1-8. Hierarchy of metrics components
I’ve worked with many managers who have been tasked to present meaningful information (metrics) on how their departments or units were doing. The first step they all take is to ask, what data do we have? This tack is taken in an innocent attempt to keep from creating more work for the staff. The hope is to fill the “box” with existing data and placate the organization’s leadership. But, because we don’t take the demand for metrics as an opportunity to develop something useful for all levels of the organization, we do as little as possible to satisfy the request. The next question is (if the existing data doesn’t seem to be enough), what data can we get? So, rather than introduce them in the order that I insist is right, I presented them to you in the order I thought you would find familiar. Now, I hope you’ll take the leap of faith to trust me—and start with the big picture first.
So, please accept my apology and now allow me to ensure that you have a proper foundation for the rest of this book.
Misconception 1. Data is not useful.
I may have given you the impression that data is not useful. Or that measures and information, without being part of a larger metric, lack applicability to improving your organization. The Data-Metric Paradox addressed this, but it’s worth pointing out again. Data can be very useful, if your question is extremely specific and requires only a numeric/value answer like, what time is it? If data is all that is required, it is likely that you won’t need a metric or any of the detailed information that accompanies it.
Misconception 2. Start with data and then build toward metrics.
Most of the time, this misconception is born of the misguided belief that you need data, you need answers. The other catalyst for this misconception is the abundance of data available, thanks to technology. You may believe that you should collect data, try to group them into measures, then take the measures and compile them into meaningful pieces of information, and finally, take these components and build a metric to give it all meaning and clarity.
The truth is just the opposite. There are so many possible data that you can collect, that beginning at the data level will almost assure that you fail to create a useable metric. As you will see, it is important to start with the end in mind; the metric is the end (if the question warrants it) and, therefore, also our beginning.
Misconception 3. You have to have a root question before you gather data, measures, or information.
While I wish this were true, you’ll find many times that you are required to gather data, analyze measures, and create informative reports with no idea of the reason why. Like most good soldiers, you may very well have to do what is short of “right.” I highly suggest that you do your best to identify the root question before you start, but if you can’t, of course you can gather information without it. If this happens,