Anticipatory analytics – why we can’t predict incidents

12 Nov 2020

Anticipatory analytics – why we can’t predict incidents

This presentation builds on the Master of WHS research project I completed in 2017, which focused on WHS-owned and driven data analytics use cases. This project gave me the opportunity to better understand the changes that our economy and world of work are experiencing at the hands of digital technology. Effectively, this is all about data.

This presentation will explain to participants how digital technology relies on data, and how data is industry 4.0’s oil. Data is shifting business models, disrupting industries, and changing our relationship with work. Digital technologies afford us so many benefits, but they can have a negative impact on our health, and subsequently on our work productivity and satisfaction.

As more immediate, larger volume, and more varied data sets become available, organisations have moved towards predictive analytics. I’ll explain what predictive analytics is, and what it isn’t, through examples we experience every day. I’ll then show participants how this fits into the management of WHS. We’ll discuss incident causation frameworks, and why we won’t be able to literally predict incidents for some time (hint: it’s because the most complex supercomputer known to humankind is often involved in incidents, the human brain). We’ll also talk about the pitfalls of counting, and why quantification is always, particularly in socio-technical systems with people, a representation of the world around us.
Finally, we’ll blast through some foundational concepts around innovation, and I’ll give attendees some tips and models to use to apply innovation in their workplace.

In a hyper-techno world, I describe many of these concepts as foundational. But they are fairly advanced for the WHS profession. And it is crucial we understand them in our role as ‘flexperts’, so that we can benefit from this industrial revolution, and not be swept aside by it.