Describing what we do

In conversations about people involved in data analysis, one of my colleagues in an institute of sport observed that “the biggest challenge is how we develop and mentor these people”.

I see this as a critical issue as sport expands its data science portfolios. It has encouraged me to think about the verbs we use to describe our work in data.

When I first started in performance analysis in pre-digital days, we aspired to:

  • Observe
  • Record
  • Analyse
  • Model

Guillermo Martinez Arastey (2018), amongst others, has described how this role has changed in a digital era (link). It has meant for me that performance analysts are connected and I saw this at first hand when I met Darrell and Adam in Cardiff (link).

Darrell’s Vocational Performance Analysis post and Adam’s What has changed in Performance Analysis over the last 5 years? exemplified their reflection in and on action that define connected, sensitive educators.

I was thinking about these connections and changes when I came across IBM’s AI Ladder (link). This ladder used a fourfold taxonomy of verbs:

  • Collect accessible data
  • Organise a business-ready foundation
  • Analyse with trust and transparency at scale
  • Infuse throughout the business

I would add to this feedforward (link). With artificial intelligence I think it is vital to consider where we will be and involves us in mental time travel.

In a 2012 paper, Peter Dowrick observes:

The most rapid learning by humans can be achieved by mental simulations of future events, based on reconfigured preexisting component skills. These reconsiderations of learning from the future, emphasizing learning from oneself, have coincided with developments in neurocognitive theories of mirror neurons and mental time travel.

It is these “mental simulations of future events” that strike me as very important as we consider the verbs that guide us through a dynamic domain opening up before us.

Photo Credit

Markus Spiske on Unsplash


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