Leading and Lagging Indicators: Navigating the Data Lake

Leading and lagging indicators

We live in a world of fast-changing, sometimes impenetrable, sometimes meaningless technology trends, achievements and terminology. Yesterday’s Deep Blue is today’s iPhone and the dizzying speed at which ‘data science becomes machine learning becomes AI’ can leave even the most committed of us struggling to know exactly what is what.

Tim Strongman of Arcadis, in his presentation at Cority’s London Roadshow, acknowledges this and offers a helping hand to help EHSQ professionals navigate the ‘data lake’ (in which it seems drowning is an entirely possible result).

Organisations have dutifully collected data for decades for the purposes of compliance monitoring, reporting and tracking. While these tasks are relevant this activity doesn’t go far enough. Technology improvements in capturing and processing this data now allows for lagging and leading indicators to inform change and avoid problems. But, what are lagging and leading indicators exactly? Fortunately Tim had a brilliant analogy!

“If your car runs out of petrol and breaks down then that’s a lagging indicator. If the fuel light comes on then that’s a leading indicator.” 

So, from an EHSQ professional’s point of view then it’s all very well keeping records but that only leads to lagging indicator information which is by its nature reactive and can be viewed negatively. Far better is to ensure you have access to leading indicator data which ensures proactive behaviour and can be communicated very positively.

Super Tim, thanks got it. What do you mean there’s more? Recently, data analytics tools have upped the ante and EHSQ professionals can now predict risk and design processes to optimise performance and drive business results.

Tim was good enough to assuage the feeling of inadequacy in the room by generously pointing out that very few organisations had mastered this art as yet but, in his experience, there were four key stages:

  1. Collecting good data
  2. Cleaning that data
  3. Identifying patterns in the data
  4. Making evidence-based predictions

So, while Tim cannot hold back the pace of change he can at least put it into context and break it down so it seems less intimidating and more achievable. Attending the Cority London Roadshow and working with their partners allows you to have an experienced guide to light the way as you navigate, what for you, is an unlit path.