Every executive conversation about AI starts with the same optimism: we have tons of data. Every implementation that follows ends with the same quiet failure: the data was unusable.
The promise of a data-driven enterprise is often broken because organizations try to build AI refineries on a foundation of low-quality, non-integrated data. This failure stems from poor process design, where manual entry and system silos contaminate the "digital oil" needed for true AI success.
The Root Causes
The pattern is consistent across mining, energy, and megaproject operations:
Manual data entry remains prevalent. Equipment readings, work orders, inspection reports — a significant portion of industrial data is still typed by a human from a clipboard into a form. Every touchpoint introduces errors, omissions, and inconsistency.
Disconnected applications prevent integrated views. SAP PM, the CMMS, the historian, the LIMS, the spreadsheet on a supervisor's laptop — each holds a fragment of truth. "AI implementations in silos" fragment visibility and make it impossible to reason about the operation as a whole.
Unstructured data lacks governance. Up to 97% of a company's information lives in PDFs, emails, photos, scans, and shift notes. None of it is queryable. None of it is trusted. All of it is invisible to the analytics layer.
The Core Problem
A Kongsberg Digital survey found only 5% of industrial professionals feel data access enables "decisions very quickly." The rest operate in a world where the data exists somewhere, but getting to it, trusting it, and acting on it is slow, political, and often wrong.
Organizations possess abundant data but it's unreliable. The "digital oil" is contaminated — and no amount of ML modeling downstream can clean a contaminated feedstock.
The VSC Approach: Fix It at the Source
Rather than enterprise-wide data cleaning projects that consume years and deliver nothing, we advocate a targeted, iterative approach that fixes the contamination at the point where it enters the system.
1. Select a critical business process. Start where the pain is quantifiable — maintenance planning, shift handover, reliability reporting. One process, end to end.
2. Trace the complete information flow. From the source (operator, sensor, document) to the decision (work order, KPI, report). Identify every manual touchpoint and every system gap.
3. Implement improvements at source points. Automate capture where possible. Create the system connections that eliminate re-keying. Standardize procedures so two operators entering the same event produce the same record.
The emphasis is ensuring new data entering the system is high quality by design, rather than attempting retrospective cleanup of a decade of accumulated contamination.
The Question You Should Ask Today
Before your next AI investment, answer this: for the one process you most want to improve, how many hands does the data pass through before it reaches the model? How many of those hands can re-type, interpret, or omit?
Until that number is close to zero, the refinery you are building will process contaminated oil. And no model will fix it for you.
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30 minutes. Your specific case. Honest assessment.
Schedule a Meeting with VSCSources
- Kongsberg Digital: Industrial data access survey — 5% "decisions very quickly" statistic
- Industry analyst reports on unstructured data in the enterprise (up to 97% estimate)
- VSC field experience across OR and AMS engagements in heavy industry
