ML Journal

It isn’t about collecting more data—it’s about building trust, reliability, and governance to act with confidence. 

TAKEAWAYS:
● Manufacturers generate massive volumes of data across design, production, logistics, and service, but fragmented systems prevent its effective use.
● Reliable data requires a governance framework that prioritizes quality, availability, lineage, ownership, and integration health as non-negotiables.
● Achieving data mastery is a staged transformation—beginning with strategy and governance, piloted through early wins, and scaled with continuous monitoring.  

Manufacturing has never been short of data. Across the lifecycle, the streams are relentless. Design teams generate gigabytes to terabytes of CAD, simulations, and specifications. Planning adds bills of materials, schedules, and cost structures. At the production stage, the firehose opens: sensors, quality metrics, and inventory signals push into the terabyte-to-petabyte range. Logistics contributes tracking and order flows; service adds usage and maintenance histories of similar magnitude. In most factories, the production stage alone accounts for the majority of enterprise data.

Table 1: Data types and volumes across manufacturing stages

What most organizations lack is not information, but trustworthy information—data that is complete, contextualized, and ready to drive a decision now, not after a post-mortem. The short answer to why this is the case: Data production has outpaced absorption.

Most manufacturers have built systems over years, not as a unified fabric but as islands. Operations tech streams millisecond sensor data while enterprise apps still batch overnight. Vendors speak different dialects, IDs don’t match across product, process, and resource, and revisions in PLM rarely flow cleanly to MES or quality systems. The consequences are predictable: delays, re-keying, rising costs, and a persistent disconnect between teams. When data is fragmented, people optimize locally. The enterprise loses.

Reliability as an Operating Principle

Without a governance spine, more data doesn’t mean more truth, it means more delay. The first step is to make reliability an operating KPI, not an afterthought. That means treating five dimensions as non-negotiable:

1. Quality: Accuracy, completeness, and timeliness
2. Availability: The feed is up when operations need it
3. Lineage: We can trace where numbers come from and how they were transformed
4. Governance: Clear ownership, access, and compliance
5. Integration health: Connections sync with minimal failure

To ensure progress, set explicit targets: think 95%+ accuracy, 99.9% availability, full traceability, and sub-1% sync failures, and audit them on a regular cadence.

When organizations do this consistently, reliability scores rise quickly, and with them, decision speed and confidence.

Data Mastery is a Transformation

Data mastery is a staged change program. It starts with sponsorship and ownership—an executive mandate, accountable data stewards, and an honest inventory of today’s assets and pain points. The next step is to design the operating model—principles, decision rights, policies on quality, privacy, and security, and map the practical workflows for the data lifecycle, issue resolution, and change management.

Implementation should begin with focused pilots in high-value domains, coupled with data literacy training and enablement. Technology follows purpose: catalogs, observability, quality monitors, and automation to sustain new ways of working. Finally, it is run as an operation —dashboards, scorecards, compliance reporting, and a discipline of continuous improvement and periodic governance reviews.

Table 2: A model step by step approach to mastering manufacturing data

The Payoff: Trust in Action

The organizations that will win are not those that collect the most data, but those that can trust their data enough to act on it—confidently, repeatedly, and at scale. When reliability becomes habit, analytics stop arguing with operations. Engineers change a design and see the impact upstream and down. Planners simulate scenarios they believe. And most importantly, operations leaders move from firefighting to foresight.  M

About the author:

Buddhi Ratawal is Senior Manager, Strategic Business Development at DELMIA.