Measures and metrics can be confusing; what do terms such as Leading, Lagging, Qualitative, and Quantitative even mean? when should you use them and why? It’s very hard to be ‘Data-informed’ in your decision making, if you are not informed yourself, on what these terms mean and how to apply them. I got inspired to cut through some of this confusion after Kate Champ spoke to me about how she had used a quadrant to map out Qualitative, Quantitative, Leading and Lagging measures and metrics. I’ve built on this approach and have found it to be really useful when explaining what these terms mean and when to use them.
These are the definitions I use to explain these terms (I’ve been deliberate in choosing this vocabulary as I find it avoids confusion, at least for me!).
- Measure is an indication of the size, quantity, amount or extent of something
- Metric is a calculation between 2 measures
Metrics therefore come from measurements, e.g. a Measure would be ‘revenue’, whereas a Metric would be ‘revenue / month’. Measures and Metrics can be Quantitive, Qualitative, Leading or Lagging.
- Quantitative data is ‘Numerical’ and puts everything in terms of values or counts. These are expressed as numbers, e.g. how many, how much, or how often.
- Qualitative data is ‘Observational’, it looks to characterize and describe, and is non-numerical. It is messy, but rich in insights, especially when you look for common themes.
Quantitative data tends to give you the broader ‘what’, whereas qualitative starts to zoom into the ‘why’ behind it.
- Leading indicators measure ‘Actions’’, like sign posts or system inputs they point towards what activities are necessary to lead to your goals.
- Lagging Indicators measure ‘Results’, they look back at what system outputs occurred and what was achieved. They are therefore only known after the event.
Leading indicators are directly within your control to change, whereas lagging indicators are not, they are something that has happened. If you change your lead measure, like a system response your lag measures should follow. E.g. ‘Number of our people who can self-select onto teams and choose what they work on and who they work with’ (Leading) should impact ‘How likely are our people to recommend the organization as a place to work to their family and friends?’ (Lagging).
Mapping out a Quadrant
Creating a quadrant with these, for an area you want to be 'Data-informed' about, helps to act as a prompt for the specific measures and metrics you will need.
If, for example, one of the areas is a product, your quadrant could include measures like this:
The top row shows us ‘What’ our customers are doing, the bottom row starts to zoom into ‘Why’ they are doing it.
By explicitly mapping out an area you want to be informed about (e.g. Product, People, Customers) you can visually highlight any under or over represented sections. While that might not be a problem, it at least sparks a deliberate conversation. I find this a useful approach not only to explain measures and metrics, but to also ensure that there is a considered and balanced approach to collecting that information.
If you give this a try let me know how you get on, I'd love to hear about it.