# Mapping the manufacturing data landscape: sources, systems, and silos
A single work order to machine 500 turbine brackets can generate over a dozen distinct data trails before the part ships: a programmable logic controller (PLC) logging spindle speed every 100 milliseconds, a historian archiving temperature curves, a quality system flagging a dimension out of tolerance, and a maintenance ticket opened three days later when the same machine vibrates oddly. Most of that data never talks to the rest. That is the silo problem, and it is the single biggest reason manufacturers struggle to turn "we have lots of data" into "we make better decisions."
This lesson maps where the data comes from, where it gets stuck, and how to measure whether your data foundation is actually usable.
Start with the order: build 500 brackets by Friday.
Seven systems, one physical batch of parts. Each system was bought at a different time, by a different department, often from a different vendor, with no shared data model. That is how silos are born, not through negligence but through years of reasonable, local decisions.
Silos cost money in three specific ways.
1. Root-cause analysis takes days instead of minutes.
If the vibrating machine caused the dimensional defect, someone has to manually cross-reference the CMMS ticket timestamp with the historian's vibration trend and the QMS nonconformance log. If these systems don't share a common asset ID or timestamp format, that correlation can take an engineer a full day of spreadsheet work. Multiply by hundreds of defects a year.
2. Machine data never reaches the people who could act on it.
A PLC might generate thousands of data points per minute, but if the historian samples it down to one reading per minute and nobody feeds it into a predictive maintenance model, the plant is paying for sensors it doesn't use. Industry surveys (McKinsey, Manufacturing analytics research) have long estimated that a majority of industrial data generated on factory floors is never analyzed. Treat that as a directional estimate, not a precise figure, but the direction is well established.
3. Duplicate and conflicting master data.
The same machine might be called "CNC-04" in the CMMS, "Line3-Mill2" in the MES, and an anonymous tag number in the historian. Without a shared asset hierarchy, joining these datasets requires manual mapping tables that break every time someone renames an asset.
Rather than trying to master every system, focus on five data categories that recur across the sector:
| Data category | Typical source | Update frequency | Primary use |
|---|---|---|---|
| Machine/process data | PLC, SCADA (Supervisory Control and Data Acquisition), historian | Sub-second to minute | Process control, predictive maintenance |
| Quality/inspection data | QMS, LIMS | Per batch/lot | Compliance, defect tracking |
| Maintenance data | CMMS | Per event | Asset reliability, spare parts planning |
| Production/order data | MES, ERP | Per shift/order | Throughput, costing, scheduling |
| Supply chain data | ERP, EDI (Electronic Data Interchange) feeds from suppliers | Daily/per shipment | Inventory, traceability |
Traceability across these five categories, being able to answer "which raw material lot ended up in which finished product, made on which machine, inspected by whom" is the single most valuable integration outcome in regulated manufacturing (aerospace, medical devices, automotive safety components).
Generic data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition → frameworks talk about accuracy, completeness, and timeliness. In manufacturing, translate these into specific, checkable metrics:
Suppose a plant has 200 registered assets in its CMMS. When cross-referenced against the historian's tag list, only 168 have an exact matching identifier.
Match rate = 168 / 200 = 84%
That 84 percent means roughly 1 in 6 assets cannot be automatically joined across systems, forcing manual lookups for any cross-system query involving those assets. Raising this above 95 percent is a realistic, common target cited by MES/historian integration vendors, and is a good first KPIKPIKey Performance Indicator, a measurable value that shows how effectively you're achieving a specific objective, tracked over time against a target.View full definition → (Key Performance IndicatorKey Performance IndicatorKey Performance Indicator, a measurable value that shows how effectively you're achieving a specific objective, tracked over time against a target.View full definition →) for any digital transformation initiative, well before investing in advanced analytics.
Knowledge check
1. A machine begins vibrating oddly three days after a quality system flagged an out-of-tolerance dimension, but no one connects the two events. This scenario best illustrates which core problem in manufacturing data?
2. Why does a historian archive PLC and sensor data separately from the MES tracking the work order, rather than one system doing both jobs?
3. A plant manager wants to know whether a specific batch of brackets that failed inspection was produced during a period when the machine showed early vibration signs. Which underlying capability determines whether this question can actually be answered?
4. Select ALL correct answers about why a single work order generates data in multiple, separate manufacturing systems rather than one unified system.
Select all the correct answers.
5. Select ALL correct answers about what makes manufacturing data 'usable' beyond simply existing somewhere in a system.
Select all the correct answers.
Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition → problems are usually organizational, not technical. A practical governance structure for manufacturing data includes:
Frameworks like the NIST Manufacturing Extension Partnership resources offer free, practical guidance for small and mid-sized manufacturers starting this governance work without hiring a large data team.
🎬 [VIDEO: "How Industrial IoT and Data Historians Work Together" - youtube.com - search for recent AVEVA or Siemens explainer videos on historian architecture and OT/IT data integration for a visual walkthrough of how sensor data flows from PLC to analytics dashboard]
Even without a full integration platform, analysts often start by writing a join query across exported CSVs using a shared timestamp and asset ID:
SELECT
h.asset_id,
h.timestamp,
h.vibration_reading,
q.defect_code
FROM historian_export h
JOIN quality_export q
ON h.asset_id = q.asset_id
AND h.timestamp BETWEEN q.inspection_time - INTERVAL '2 hours'
AND q.inspection_timeThis basic pattern, matching machine condition data to a quality event within a time window, is the manual precursor to what predictive maintenance platforms automate. Understanding it helps non-technical managers ask sharper questions of their data teams: "What's our asset ID match rate?" is often more useful than "Can we do AI?"