Shaun McGirr is a data & AI leader with 20 years of varied experience across official statistics, academia, consulting, automotive, and software. His mission in life is to help others do something useful with their precious data. AI has turned up the heat on everyone trying to create value from data, but many data professionals remain extremely sceptical about its impact on their own work. This is a wasted opportunity because the AI skills organisations are most lacking are (almost) the same skills data professionals uniquely bundle together. When not working, Shaun answers increasingly difficult questions from his two children and reminisces about an earlier life as a semi-professional musician.
Session Overview
Is AI the End of BI as We Knew It?
For decades, prediction was a promise tacked on the end of data maturity models, under different names: advanced analytics, data science, machine learning. This utopia of “value beyond reporting” launched countless data initiatives, and the slow grind up the maturity curve held a certain comfort for many data professionals, myself included. Then (Gen)AI crashed in to corporate consciousness in 2023 and has not stopped rocking the boat and exasperating data teams.
Why are we building chatbots, when we can’t even get the basics right? Haven’t they seen our maturity model? Are we losing control of the data agenda? Was our struggle for nothing? Garbage in, garbage out. But it is increasingly untenable to keep telling stakeholders to wait for the next data (re)platforming to finish before they cause chaos – even if we are right on average. Instead, we must refocus and realign, using the unique skillset only data professionals hold to better guide our organisations on what should have been the mission all along.
In this talk I update my own expired maturity models for an age when the most value-generating data do not hide in tables, and the means of prediction have become accessible to all. Our daily habits must change, not to waste this AI moment.
Three Tangible Learnings
Why BI-then-AI maturity models have held us back for decades.
How (Gen)AI is shifting power from data teams to business users and software developers.
What’s at stake if data teams do not reframe their unique expertise.
A Practical Guide to Seizing the Means of Prediction
Like any disruptive technology, we can ignore (Gen)AI through well-founded scepticism, or embrace it as a tool that improves our competitive edge. And as time goes on it will become tougher to sit on the fence, because the fence will be on fire.
Yet despite the relevance of our skill-set, I meet more “blissful ostriches” among data professionals than I do in business or software teams. In my first keynote I shared some reasons why, and now we will do something about it together.
By reflecting on work that took me far too long, or was frustrating to get right, or did not build enough on others’ shoulders (or all the above) I will arm you with a new mindset.
This mindset views data as a peculiar asset, which creates the most value not when it is made perfect, but when it changes behaviour. Viewed in this light our expectations of (Gen)AI become radically simpler, and its perceived bugs become features. I will show multiple examples of “oh sh*!” moments, when I’ve realised how many months earlier I could have delivered projects, with at least the same confidence.
As (Gen)AI lowers the cost of making predictions for everyone, those who know data best are uniquely placed to make the most of (Gen)AI with only minimal reskilling. But this window won’t last forever, so let’s act now!
Three Tangible Learnings
Why data professionals can find it so difficult to see the value in (Gen)AI
How to set useful expectations about (Gen)AI in our work as individuals & teams
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Shaun McGirr is a data & AI leader with 20 years of varied experience across official statistics, academia, consulting, automotive, and software. His mission in life is to help others do something useful with their precious data. AI has turned up the heat on everyone trying to create value from data, but many data professionals remain extremely sceptical about its impact on their own work. This is a wasted opportunity because the AI skills organisations are most lacking are (almost) the same skills data professionals uniquely bundle together. When not working, Shaun answers increasingly difficult questions from his two children and reminisces about an earlier life as a semi-professional musician.
