Defining and segmenting KPIs by examining their relationships
Our goal this month is to start building a strategic map for a simple dashboard. In the process we will see a technique to discover the relationships of the metrics and based on this, how to best monitor their progress. After, we are going to place them along the strategic-tactical-operational axis to classify them in future dashboards.
I enjoy many hobbies in my personal life - one of them being the odd sport that is amateur bodybuilding. After 12 years of training I decided to compete in my first contest. Calculating body fat, weight and measurements became important.
Let's start by defining some indicators. They are lagging indicators since they track the past.
Body Weight (BW): The total weight of the individual
Fat Mass (FM) : The total weight of adipose tissue (fat)
Lean Body Mass (LBM): The total lean body mass, including organs, tendons etc. All that is not fat tissues.
Body Fat % (BF): The ratio of fat mass to overall total body weight.
Now, since we see that the metrics are inter-related, we can define a graph to illustrate their relationships.
One strategy to see the individual relationships more clearly is to try to decompose metrics into their constituents. Body weight is the addition of LBM and FM. BF% is the ratio of those same metrics. We can see a dual relationship emerging.
Now, by themselves metrics are neither good nor bad, so let's define them as Performance indicators with some targets.
Body Weight is neutral (up or down can be good, although in this example up is good)
Lean Body Mass needs to be maximized
Fat Mass needs to be minimized
Body Fat % needs to be minimized
Usually, the goal in this sport is to increase body weight while lowering BF%. Being at 6% body fat while weighting 140 pounds is not the same as when at 200lbs.
Let's examine some of the natural relationships that exist between those metrics. This step is usually done by examining the data or by a business analyst familiar with the business processes.
For our scenario here are some relationships:
Usually, gaining weight makes the BF% go up as it is extremely hard for a natural individual that has been training for many years to lose fat and gain muscle at the same time.
Losing weight can lower BF% if the individual loses fat. The BF% can remain stable or go up if the individual is losing fat and too much muscle.
In bodybuilding, we strive to retain the most LBM while lowering BF%. The net effect is a loss of BW (fat).
In essence, the BF% measure constrains the ratio of the other indicators. It ensures an efficient relationship of the other two metrics. Otherwise, one could increase both BW and LBM, disregarding the ratio and end up at a weight of 300lbs and 200lbs of muscle. This, however, gives a ratio of 33% BF, which is considered morbidly obese. For your information, pro heavyweight bodybuilders today often hover at 260lbs at 4%BF.
In contests, the weight classes function by body weight range and the winners are usually the ones with the lower BF% in their class.
This simple example clearly shows that we often need to have a holistic approach to managing KPI relationships, since they are often closely related. Maximizing a KPI is not done in a vacuum.
So from a strategic point of view, we want total Body weight and Body Fat % maximized. These are the metrics at the "end of the line" on our graph above.
Next month we will define the other layers of metrics, looking at the Tactical metrics and Operational drivers.