A pattern keeps showing up in research and in the field, and it validates what I am hearing from customers of all industries, and in all regions worldwide. A recent report surveying data leaders across Australia and Singapore found that 97% of organizations deliver data products, yet only 25% do so through a structured, repeatable program.1 In conversations with data leaders across North America and Europe, I hear the same story: the gap between data ambition and deployment is wider than most boards realize.
The remaining 75% from the study operate ad hoc, in a fragmented mix, and for most, up to half their data team's capacity goes toward one-off work that will never be reused or shared, capacity that is unavailable for the AI initiatives that actually compound in value.
New data and analytic projects assume that if the data exists it’s aligned and accurate. Typically, the conversation focuses on features and dashboards, not the question that actually determines success: What does this solution require of my data before I can trust it? And once it is trusted, how do I manage to keep it aligned to that new deliverable?
These are the five requirements that separate organizations that scale AI from those still rebuilding the same dataset every quarter.
