3 Common Mistakes in Performance Measurement

Performance measurement is a very effective method for analyzing the way a part of your organization performs or even the whole organization itself. It has been used in many professional circles for quite a while, and it now has an established place in many industrial environments. The methodology has evolved quite a lot since its initial inception, and it’s important to keep some things in mind when applying it — otherwise you risk running into some common problems.

1. Incorrect data collection and analysis methods

Performance measurement relies heavily on good data, so it makes sense that making a mistake in this area can have a very severe negative impact on your results. Nowadays we have many tools at our disposal for the purpose of data collection, retention, and analysis, and it’s important to make good use of everything you have available.

It’s a good idea to have some systems in place to perform sanity checks on the collected data as well, as you never know when one of your collection systems might malfunction in some odd way, producing a set that has a few tiny, but important, details wrong.

Another point to keep in mind is to have a good system in place for pruning irrelevant old data. This can become quite problematic over time, especially if your systems keep changing in specifications every now and then. It’s okay to maintain old data sets that have been captured on a different version of the system, but you have to ensure that there is some process in place to align the different data sets.

2. Not applying the methodology consistently

Performance measurement is not something you should do once and then forget about it. It’s a continuous, ongoing process that must be integrated tightly into your operations if you want to see good results from its application. This means that you should instill this attitude in your employees on every level of the organization, from top to bottom. Make sure that everyone is on board with applying performance measurement, and that there are processes in place to control its use.

This applies especially strongly to the data collection facilities involved in your performance measurement system. Make sure that everyone knows what data points are important in your analysis, and how to organize the collected data to make it more accessible and presentable.

And of course, this also means that whenever your data collection practices change in some way, you must communicate this to everyone involved as clearly as possible. A common problem observed in organizations applying performance measurement is that they communicate changes like these too late or not clearly enough, leading to confusion in the way data is collected. This can subsequently lead to the kinds of problems we mentioned in the first point above, and it’s clearly a situation that should be avoided.

3. Having the wrong idea about good performance

It’s not always easy to define good performance, and many large organizations are still struggling with this on many levels of their work. This is somewhat relevant to the first point in this article, but it’s also a separate issue. If you don’t know what your performance targets are, you might end up moving in the completely wrong direction as a result of the analysis performed on the collected data.

What’s worse, sometimes this kind of mistake can compound over time, requiring you to go back over a significant period to undo the damage. It’s rarely easy to recover from a situation where you’ve been putting a lot of effort in the wrong area of your business, so it’s best to try avoiding this kind of situation altogether in the first place.

On the bright side, once you’ve defined your performance targets properly, and you also have good systems in place for measuring that performance, you can see a great improvement in your operations, and you’ll move towards your target in great strides. At the same time, it will be easy to undo any damage that’s been caused by wrong moves on your side.