What Comes First: Data or KPIs?

by Stacey Barr |

It’s not hard to spend millions on setting up new data management and business intelligence systems. And it can take years to implement, too. Plus there’s the tricky piece of getting people to buy in to collection and capturing all that data. But we need to do it, before we can start measuring what matters… Or do we?

chicken looking at an egg

It’s easy to argue that you can’t have a performance measure until you have the data for it.

But it’s equally logical to argue that you don’t know what data to collect until you know what you need measure.

Don’t you agree?

Sure, not all data is collected just so we can measure performance. We also need data for regulatory and governance purposes, operational decision-making, day to day transactions, research, and exploratory analysis.

These purposes for data are often accommodated in the requirements definition phase of a data management project. But the requirements for KPIs and performance measures are often overlooked.

A big reason for this is the lack of a deliberate process for performance measurement. It’s not often seen as a legitimate management process, like governance, regulation, and decision-making are. But it is.

If we delay our performance measurement process until we have data, we will have data that fails to fit the needs of our likely KPIs. Why? Because:

  • the data management project will make assumptions about the types, formats, granularity and linking that is useful for the data to have – assumptions that often prove wrong
  • the data management project will focus on the data that people have a known need for, and won’t capture data for not-yet-known needs (which performance measures and KPIs often point out)
  • new requirements for data, which performance measures and KPIs often point out, need to be pilot tested and tweaked before they become routine parts of the data management system

And you’ll be forced to fit your KPIs to the data you have. That’s tragic, because one of the most frequent – and powerful – impacts of a good performance measurement process, is that your mind is opened and you see more clearly what you need to know about performance. You see what you hadn’t seen before.

So don’t delay your performance measurement until after you have the data. You won’t have the data, you’ll be a year or two behind in your quest for great KPIs, and you’ll have a load of rework to do on your data management system.

No data should be no excuse to measure the right KPIs. Instead, commit to designing the performance measures you need. Then commit to pilot testing those measures with manual data collection and capture. Then tweak the data, tweak the measures, and use them both as part of the requirements specification for your data management system.


How have you linked your performance measurement process to your data management process? Which one drives the other?

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