There is no secret sauce for data integrity.
It always starts the same way: “Data is the new [insert cliché here]!” “We must become data-driven!” “AI first!” At some point, someone will point out that this will require a lot of high-quality, trustworthy data on an ongoing basis. Nodding. Then the realization hits: This is a lot of effort…
That's when executives become desperate for the playbook. They want to know how others have done it, hoping another firm's map will lead them to the treasure without much effort and resources.
However, this search for a "secret sauce" is a delusion. It’s the equivalent of a crash diet for business: a promise of fast results, comfort in the moment, but no long-term benefits:
👉 Illusion #1: The case study. It promises validation and a replicable path to glory. But applying a fintech's data model to a 150-year-old insurer isn't strategy; it's a corporate séance, an attempt to summon a spirit that won't come. Find a case study from a close competitor instead? Look closer and see the magic disappear. They probably just started putting in the work three years ago.
👉 Illusion #2: The framework. It arrives as a set of elegant, interlocking boxes on a slide. The consultant presents it with confidence. The room nods appreciatively. Someone asks about implementation timelines. Sounds fantastic. But it’s absolutely powerless without the institutional will to fill it with commitment and resources.
The truth is there is no secret sauce, short cuts or hacks. Mastering your data is no different from mastery in sport or music: it demands time, effort, and consistency. Successful organizations have someone who wakes up every morning accountable for data quality—with budget, authority, and a seat at the table. The ones that fail? They try to do data quality “on the side” because it’s “everyone’s responsibility”.
Data integrity is not an engineering problem to be solved, but a human system to be cultivated within the organization. And culture follows incentives. If your organization rewards hitting immediate business targets above all else, why would anyone care about the data? If promotions go to managers regardless of their attention to data quality, your dashboards become expensive wallpaper, displaying fiction in high resolution.
The real work is unglamorous. Invest the resources. Do the work. Keep your eyes on the data prize. But while it may not win the prize for most innovative business idea, the outcomes very well could!
Yet most firms will continue their pilgrimage, collecting frameworks and case studies while their data remains a liability. The few who succeed? They won't have found a secret; they'll have built a discipline.