Session Outline
Multiple attempts to create value from data have been labelled “democratization:” business intelligence in the 1990s, self-service analytics in the 2000s, and (citizen) data science in the 2010s. Yet, progress on their common goal, putting more hands on data outside central teams, has been wildly uneven. Cynical observers even note the same problem being “solved” over and over without understanding why earlier attempts failed. The current generative AI revolution has led, once again, to the same pronouncements, so what would make things different this time? Can AI help us escape the paradox of so-called structured data remaining impenetrable to domain experts? And how can data leaders avoid being left behind as business teams embrace AI faster than central teams can govern it?
Key Takeaways:
- Why prior attempts to “democratise” data have generated uneven progress
- An alternative view on the problem from economics (instead of politics)
- How to use current hype to accelerate AI adoption
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