What Is A Data Strategy?
Businesses are being ridiculed for going straight to AI by the people who told them to go straight to AI in 2017. The mantra of 'just hire data scientists' led businesses to hire data scientists. What did they think was going to happen?
Most skipped over data strategy and are now working to fill in the gap. When I started in AI strategy, I combined all three aspects (data, analytics, and AI) into a single technical strategy. I am a convert to breaking them out because the big picture includes more than just those three. Cloud, IoT, and many other technologies need their own strategies. It's easier to create enterprise-wide alignment with a top-level technology model that covers all the business's technologies.
That's why there are 3 distinct strategies now. Each is its own technology wave, but it's still valid to look at them as a cohesive unit supporting each other.
The concept of technology waves was first introduced to me in 2012. Rita Gunther McGrath gave an interview to promote her soon-to-be-released book, 'The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business.' She explained the implications of continuous change on business strategy and the competitive landscape.
I made connections to Data Science that turned into my approach to continuous transformation. Strategy is cyclical and recursive. The data strategy sets the table for analytics and AI. As the business matures, the AI strategy will force changes to the data strategy. What kind of changes?
The business will mature from data engineering to data curation. Ontologies require a new approach and new people to build them. That change supports more advanced models and, eventually, causal methods. Their data strategy must evolve for the business's AI capabilities to reach higher maturity levels.