Data foundations / 3 min read
Bad data cannot be the reason AI stalls
"AI is only as good as its data" is true, and it has become the excuse that stalls programs. Manufacturers that wait for clean data wait indefinitely. The answer is to fix data quality through focused delivery, not before it.
The operating reality
Bad data distorts forecasts, hides maintenance risk, inflates inventory and erodes confidence in the whole program. Manufacturing compounds this with miscalibrated sensors, incomplete mappings and limited source-system knowledge.
The flawed instinct is "clean everything." Enterprise-wide cleanup is broad, costly and disconnected from any near-term result. Start instead with one decision: failure prediction for a critical asset class, a forecast for a high-value product family, the root cause of a recurring defect, one trusted inventory view. Identify the data that outcome needs, assign owners, fix what matters. You get a visible return and a reusable standard.
Context is the harder half. An accurate sensor reading is useless if it cannot be tied to the asset, operating state, product, maintenance event and specification. That is why data engineers cannot solve this alone. Reliability, process and quality engineers, ERP experts and data stewards all carry part of the answer.
Make data quality a product responsibility, not a cleanup chore assigned after problems surface. Every data product should carry a business owner, a technical owner, agreed definitions, lineage, quality rules, service expectations and measured value. Clear ownership turns bad data into a managed operating issue.
The talent implication
you need people who connect data problems to operating consequences, spanning data leadership, architecture, ERP and MDM, engineering, manufacturing domain and governance. The strongest candidates do not promise perfect data. They improve it while delivering value.
The Sebastian Group view
Hiring for data quality is not one governance director. The right mix depends on the use case, systems, ownership and stage. We define where leadership, engineering and domain expertise must converge, and assess candidates on proof they have improved difficult data environments while moving the business forward.