I wrote this for Forbes in March 2021, when organizations were sitting on mountains of data but struggling to extract real value from it. The conversation was still about volume, velocity, and variety. What I wanted to highlight was that big data deserved credit for fundamentally changing how businesses make decisions. Looking back, this piece was really about the preconditions for AI readiness, even though we did not call it that yet.
Key Takeaways
Data infrastructure is not glamorous, but it is the foundation everything else sits on. The organizations that invested in clean, accessible data pipelines in 2020 and 2021 are the same ones successfully deploying AI today.
The shift from descriptive analytics to predictive and prescriptive capabilities was already underway. Big data made it possible to move from 'what happened' to 'what will happen' and eventually 'what should we do about it.'
Cultural readiness matters as much as technical readiness. Teams that learned to trust data-driven insights during the big data era transitioned to AI adoption far more smoothly than those that never built that muscle.
What I Would Add Today
If I were writing this today, I would frame big data as Chapter One of the AI story. The compliments I gave to big data in 2021 are now the prerequisites for AI success. Organizations that skipped the data maturity work are now paying for it with failed AI implementations, hallucinating models, and teams that do not trust automated outputs. The lesson is clear: you cannot shortcut the foundation. The Intelligent Organization framework I have since developed places data infrastructure as a core pillar precisely because of what I observed during this era.
Go Deeper
These ideas are part of The Intelligent Organization, the framework I built for leaders navigating AI transformation.
Explore the Infrastructure & Data Pillar