7 Ways You’re Making Performance Measurement Too Hard

by Stacey Barr

Are you doing any of these unnecessary things with KPIs, and it’s making performance measurement harder than it needs to be?

Pushing cogs up hill. Credit: https://www.istockphoto.com/au/portfolio/1stunningart

Performance measurement is hard enough to do well in most organisations and businesses. People don’t know what process to follow. They’ve been burned in the past by bad KPI implementations. Data collection is boring and time-consuming and costly. More time is spent arguing over KPI results than using them to improve performance. Targets are scary and demanding.

So why would we willingly do things that make it even harder?

If you’re doing any of the following seven things, you’re definitely making performance measurement harder – and take longer – than it needs to.

#1 Measuring too much

There are several reasons we end up measuring too much:

  • we’ve always measured it
  • the data is there and it’s easy to measure it
  • ‘they’ want us to measure it
  • someone is a micro-manager

Measuring too much wastes time we don’t have. Instead, we need to practice how to be ruthless about what we select to measure.

#2 Making up the process as you go

It’s easy to fall into the trap of thinking your organisation is special, and needs a special approach to develop it’s KPIs. So you can end up wasting time and energy making up a bespoke approach, that ends up not working because it’s not been tested and proven.

Find a KPI methodology that is proven to work, and only tweak it after you’ve mastered it.

#3 Trying to get everyone engaged

We’ll never get everyone engaged in measuring performance. And some people won’t ever be engaged until they see how measuring performance can work well. We’ll use up all our energy trying everyone on board with our theoretical promises about better measurement.

It’s much easier if we just get together some volunteers into a Measures Team and measure something that matters. Then we can invite everyone else to see how it worked, and help us improve it, but using a simple and engaging Measure Gallery.

#4 Waiting for perfect data

The irony in waiting for better data before we start measuring is that we can only know what data we need after we start measuring. This means that waiting for perfect data means we’ll wait forever.

Besides that, imperfect data is still useful for tracking changes in performance over time, which is all we need to make decisions about improvement. We can even get buy for a while if we collect data manually, as a stepping stone to better data processes and systems.

#5 Spinning your wheels

Unanimous decisions take a lot of time to reach, if they can be reached, and they drain everyone’s enthusiasm. They often produce very lame and diluted compromises too.

To get the best measures in the easiest way, we use PuMP’s 80% rule. This helps us move quickly enough to maintain momentum, but also keeps us open to iterating backwards to make improvements, as we learn from going forwards.

#6 Taking on too much too soon

Don’t take on a new measurement approach with the whole organisation as your project. Like any change program, performance measurement needs to evolve as we learn, and engage more people as we progress.

No matter how big our organisation is, when we start small, where the energy is, we can learn and engage with each ripple our progress creates.

#7 Waiting for the reorganisation, new strategic plan, new BI system, etc…

Do you know how often the planets line up? Most likely never. Delaying our
implementation of better performance measurement until our reorganisation, strategic plan, business intelligence system, data collection processes, and so on, are all lined up will likely mean we wait forever.

After all, these things won’t dramatically change what really matters enough to measure (like the results that are fundamental to the organisation’s purpose and over-arching direction). In any case, these are all change initiatives. So, for the things they will change – like engagement or efficiency or data accuracy – we still need a performance baseline now so we can measure their impact.

How to make performance measurement easier…

To get out of our own way, and avoid making measurement harder than it has to be, we only need to do three things:

  1. Choose a proven process.
  2. Apply it, now, even if to just a single result that matters.
  3. Trust the process, and follow it.

How are you making performance measurement harder than it needs to be? [tweet this]

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  1. AP says:

    Stacey,

    I had been recently thinking about customer surveys and the data they produce. I have almost no experience with survey data, and was wondering if you have had much to do with them.

    Currently our teams will report how wonderful it is when a survey tool returns a new figure slightly higher than the last for a subject like ‘Customer Sentiment’ or ‘Customer Trust’. I roll my eyes and say that we need to see it on a control chart to understand what’s going on. A control chart would also be nice because you could overlay actions on the time line and see the outcomes. For example mark a vertical line labelled ‘customer education program commenced’ and see how the control chart varied after that point. We never seem to evaluate any of our previous actions in this way. Crazy!

    My background is scientific, so when I take a measurement there is an uncertainty built around it. Figures will often be expressed as X mg/L +/- 0.2 based on the instruments sensitivity. Figures will be shown as X with a standard deviation of +/- Y. Yet when I’m shown survey results it is always a hard decimal figure e.g. 6.3 with no uncertainty or variation.

    Would you be able to discuss surveys and the pitfalls of interpreting the figures in a future Measure Up e-mail?

    Regards, AP

    • Stacey Barr says:

      AP, thanks so much for the idea – yes it’s a great topic and I’ve added it to my Measure Up article queue. I am likely to discuss things like: survey estimates, relative errors, confidence intervals, and how the variability due to sample error impacts on using XmR charts to track sample-based measures.

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