As you probably know by now, I LOVE metrics in safety. I was raised in industrial safety/process safety by some great leaders and mentors who taught me that everything in safety must be defined and quantified. They also taught me that anything and everything can and most likely will be manipulated at some point in time. This is why a functioning SMS is so critical, as all SMSs should have a QA/QC element that validates all the other elements of the SMS. And a huge part of that validation is validating the metrics gathered from each element. We have to recognize that even leading indicators can be manipulated, thus they must be validated. In Six Sigma we called this Gauge Repeatability and Reproducibility (Gauge R&R) and in my opinion, this was at the heart and soul of any SMS/Safety Process metrics.
Far too many organizations struggle to develop a means to collect AND validate their safety metrics and this will certainly lead to manipulation of the data, which leads to management being misled about the state of safety within the business. So I found this publication by the UK’s government very useful in how we, as safety practitioners, can evaluate how well we manage our data, both leading and lagging indicators. The framework titled, Data Maturity Assessment Framework, is structured across ten topics, intersecting with six themes. Each row of the assessment describes the features or behaviors associated with progression from low to high maturity for that topic and theme. This view of data maturity enhances a cross-cutting evaluation and encourages a balanced approach to
development and progress. Each of the topics, themes, and maturity levels are highlighted in the relevant sections.
The DMA has ten (10) topics that are important for data maturity in all organizations.
- Engaging with others
- Having the right data skills and knowledge
- Having the right systems
- Knowing the data you have
- Making decisions with data
- Managing and using data ethically
- Managing your data
- Protecting your data
- Setting your data direction
- Taking responsibility for data
