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You probably know all the differences between lagging and leading KPIs. Lagging is showing the past and is often difficult to influence, leading is showing the future and is easier to act on where actions will have a measurable impact.

Most of dashboards and management cockpit I have seen in companies were displaying lagging indicators, because they represent the strategic objectives of a company but maybe also because they are easy to measure. Conversely any action for improving a lagging indicator would be seen the earliest on the next kpi “period” and may have limited impact.

Well, not so easy to measure apparently sometimes…

As an example, the unemployment rate in France is definitely a strategic indicator but is published almost one month later. End of September, you have the figures for August. Very difficult to act on it. If the measure of a lagging indicator takes too long, you have very limited chance you can impact anything. Action you would implement now for improving the last month performance will not show impact before at least 2 months. And worst, the situation might have already changed resulting potentially in a nonsense action. This is something companies must absolutely avoid. Implementing Digital Governance with effective KPI frequency and why not, adding machine learning and big data are the way to avoid such situation.

Lagging indicators remain essential to business because they show what outcome has been achieved. However, they should be combined with leading indicators to show what is the direction, the trend and where to focus to be more effective.

Leading indicators are often hard to define and sometimes complex to measure. The objective is to define what would influence one or more lagging indicators, and being pragmatic by measuring what can be adjusted. On leading indicators, what is important is the trend, not the objective. More than that, setting an objective for a leading indicator will be misleading and generating wrong results.

There are multiple ways leading indicators might be defined. Best way is to find what “drives” the lagging indicators. Often intermediate processes that may affect the performance of the lagging indicators. For example, if you are in distribution, time to get organized for a delivery will have an impact on a lagging indicator of number of customers served in a month. No point in having an objective of “x” hours for the leading indicators but trend is very important.

Another technique (that I call “quick and dirty”) is to transform lagging indicators in leading indicators by increasing the frequency of the indicator and looking only at the trend at the lower level. This is an easy technique when you can still influence the indicator. Monthly sales volume is a lagging KPI and is measured against a monthly objective. Daily sales volume or cumulated sales volume might be leading ones if you look at the trend only and react / act as soon as the trend becomes negative.

Companies providing you with templates of lagging and leading indicators are probably no sense. Leading indicators are very personal to your organization and best way to introduce them is being agile with “try and run” approach.

After all, what will be important will be the capability of your digital governance solution to provide you with an overall vision of all the indicators, lagging and leading, at the right time and frequency and allowing you to check them the way they are supposed to be, trend or versus objectives.

Big data and machine learning will probably change the governance world by predicting the performance of the business and the impact of your actions on that performance. Meanwhile, it is very important to introduce digital governance giving your business the ability to mix and view differently lagging and leading indicators to drive your business.

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