Strategy
Large organisations often treat data quality as though it were synonymous with data value. Quality does not create value. It magnifies it. Consider contact records, company information, or organisational hierarchies. If a vendor can sell you a cleaner version next Tuesday, its value is bounded by the price of a
Innovation
Watch what happens in most meetings. The first idea is almost never the best idea. Yet it is often the most important. Not because it is correct. Quite the opposite. It is incomplete. It gives everyone else something to push against. This is one of the peculiar things about how
Technology
For most of history, knowledge was hard to find. It lived in libraries, universities, professions, and experts. Access was uneven. Those who knew more often had an advantage over those who knew less. Artificial intelligence changes that. For the first time, expertise is becoming widely accessible. A question that once
Leadership
Adding capable people ought to accelerate progress. Yet organisations frequently discover the reverse. The first few hires add momentum. The next few create leverage. Then, almost imperceptibly, a growing proportion of effort is devoted not to the work itself but to explaining, coordinating, negotiating, and administering it. Capability accumulates. Progress
Value
Data quality has an unusual characteristic: success removes the evidence of the problem. At first, the issues are obvious. Reports disagree. Fields are empty. Three labels describe the same thing. The defects announce themselves. Then a good team goes to work. Definitions become consistent. Pipelines become reliable. Errors disappear. Complaints
Innovation
Imagine two companies making the same mistake. The first discovers it after three weeks. The second discovers it after three years. The mistake is identical. Yet one survives and the other may not. Why? Because mistakes have a peculiar property. They grow while nobody is looking at them. A product
Strategy
Every generation believes its defining technology changes the rules of business. Usually, it changes the tools. Today it's AI. Before that it was social media, mobile, the internet, and the personal computer. Each expanded what organizations could do. None changed what organizations ultimately depend on. Every business is
Technology
We often talk about data as if it ages like food. Fresh data is valuable. Old data is stale. But that analogy breaks down surprisingly quickly. Nobody looks at fifty-year-old census records and calls them stale. Economists routinely analyse data collected decades ago. In many cases, its value
Value
Most data strategies fail for the same reason cities go bankrupt: they try to govern everything. Somewhere, a well-meaning data owner is responsible for 4,000 attributes. Each has a definition, lineage and quality score. The effort is impressive. The economics are less clear. Finance offers a useful clue.