Are Your Metrics Meaningful?

A set of data can be made of any information at all, for example, “On average, how long does it take for me to brush my teeth? A set of data can also be paired with any other set of data such as, “On average, how long it takes to brush my teeth and how long it takes to curl my hair, in the morning.”

What makes a data point meaningful is 1) the subject matter is relevant to your objective, 2) only strong correlations to other data are considered and 3) measurement occurs across a significant period of time, options, and scenarios. So, if I need to report back to my dentist an estimate of the time that I spend brushing my teeth, the data collected and considered will differ significantly from data collected because I need to shorten the time that it takes for me to get to work in the morning. If I only need information so that my dentist can assess whether or not I am spending enough quality time with my teeth, why waste time collecting data about the amount of time I spend curling my hair? In this case, the secondary information is irrelevant as it has no bearing on the objective, at all. Therefore, we complete the following steps:

1.      The purpose, objective, assumption must be made first, before brainstorming trackable variables.
2.      Track each variable for a sufficient period time, over sufficiently varied situations.
3.      Record all data as a simple a data point occurring at a specific date, time, event, etc.
4.      Ensure objectivity: Artificially skewed information defeats the purpose. If you cannot observe the data objectively, remove yourself from the process and assign the task to someone else.
5.      Note any strong correlation, which is a correlation that can be confirmed at a confidence level of at least 95%.
6.      Identify the primary driver of the correlation.
7.      Consider the data in relation to the organization’s business cycle and note any other possible drivers, causes, or effects for each variable and relationship.
8.      Note any changes in correlations over time.
9.      Complete an assessment of the correlation and their drivers, e.g. the cause, the effect, the reasons for shifts, and the nature of shifts.

But wait, we’re far from done. Metrics are often defined as “Parameters or measures of quantitative assessment used for measurement, comparison or to track performance or production.” This is true. Metrics provide a basis for comparing performance against prior performance, a target goal, or competition. However, this is an incomplete definition as quantitative data alone rarely gives the whole picture. Usually, you also need qualitative data to provide increased granularity and accuracy in the results. For example, if we can capture the variability in the time spent brushing my teeth, e.g. short, long, soft, hard, then my dentist can better assess the quality of the time that I spend brushing my teeth.

Next, you have to consider that perhaps there is more than one qualitative factor that affects a particular variable, and finally, perhaps one factor impacts the variable to a greater degree than the others. Thus, we capture the factor’s weight, e.g. 0.5x, 1x, 3x. 

Once the variable, quantitative and qualitative factors are validated, an equation can be developed to track and monitor performance. Unless you’re really into dynamic modeling that can capture the impacts of seasonality, economic data, sentiment, etc., a basic algebraic equation will suffice. So, it's not rocket science; however, it requires time, attention to detail, and a scientific approach. Otherwise, you may end up with meaningless data that doesn't help you to effectively measure and monitor the organization performance.

About the Author

Maisha Smart, MBA founded Finance and Marketing to help small businesses excel, by bridging the gap between finance and marketing processes. Some of her favorite activities include fine arts, a good debate, and social engagement.

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