Most data failures aren’t technical. They’re relational.
Poor data quality rarely destroys a company in one dramatic moment. It erodes trust slowly — in dashboards, forecasts, customer metrics, even in the data team itself.
I’ve seen revenue decisions made on flawed attribution, pricing models built on inconsistent definitions, and boards lose confidence because numbers change every month.
The hidden cost of low data quality isn’t just rework. It is credibility.
Once stakeholders start second-guessing the data, decisions stall. Investment slows. Politics creep in.
The soft skill many data pros ignore? Communication under pressure. The ability to say, clearly and early: “This metric isn’t reliable yet.” To align on definitions before building models. To explain tradeoffs in plain language.
One practical safeguard is to institute a quarterly “metric audit” with finance and GTM leaders. Review definitions, data sources, and known gaps. Document them. Make ambiguity visible.
The Takeaway.
If data quality is a business risk, are you managing it like one — or just fixing tickets?
Thanks,
Tom Myers
P.S. Also, please connect with DIH on LinkedIn.
