How to Actually Assess Your Manufacturing Data Maturity

The standard data maturity model — usually a five-level scale from "ad hoc" to "optimising" — has two problems. First, it measures capability in the abstract, not against specific business outcomes. A plant that scores "Level 3 — defined" on the data maturity scale may be producing daily production reports that the operations team ignores because they do not trust the numbers. A plant that scores "Level 1 — initial" may be generating the one piece of data that drives 80% of its operational decisions accurately. The number means nothing without the business context.

Second, most maturity frameworks produce a current-state score and a target-state score without a credible path between them. The assessment tells you that you are at level 2.3 and should be at level 4. It does not tell you which specific investments, in which sequence, will move you from 2.3 to 4 — or what 4 actually looks like for a mid-market manufacturer with SAP B1, a 200-person headcount, and a 10-person IT team.

Data foundation quality: can you trust the numbers in your ERP? This is the most fundamental question in manufacturing analytics, and the answer is often partially. Production orders are complete and accurate. Inventory valuations have a small number of persistent discrepancies that finance has learned to adjust for. Quality data is captured in the quality module but the rejection codes are inconsistently applied across shifts. Assessing foundation quality means looking at specific data — running queries, checking reconciliation, identifying the specific fields and records where the data is incomplete or wrong.

Integration coverage: which operational data sources are connected to each other, and which are siloed? A plant with SAP PP running production orders and a separate MES capturing downtime has two data sources that should inform OEE. If they are not connected, OEE is calculated manually from both systems. If they are connected but the integration was built on a nightly flat-file export, the data is 24 hours stale. Integration coverage assessment is a map: which systems exist, how they are connected, and how the connection quality (latency, reliability, completeness) compares to what the business decisions require.

Analytical capability: what questions can the current data environment answer, and how long does it take to answer them? This is not a technology question — it is a workflow question. If the question "what is our current inventory value by plant and product category?" takes two days to answer because the inventory report has to be exported, cleaned, and manually reconciled against the balance sheet, that is a maturity problem regardless of which BI tool is in place. Analytical capability assessment means tracing specific business questions through the current data workflow and measuring the time, effort, and manual steps involved.

Operational adoption: are the people who make operational decisions actually using the data? The best analytics platform in the market delivers no value if the shift manager is running production from memory and a whiteboard, and the plant manager is looking at a dashboard that the operations team has quietly stopped trusting. Adoption assessment is qualitative: interviews with the people who use the data, observations of actual decision-making behaviour, and identification of where the data is used versus where it is ignored.

A manufacturing data maturity assessment that produces useful results takes 10-15 working days for a single-site operation and 20-30 days for a multi-site group. The assessment has three components: document review (existing reports, data dictionaries, integration architecture diagrams, if any exist), interviews (with operations, finance, IT, and the data users themselves), and data sampling (running queries against live systems to validate what the documentation says).

The data sampling component is where most assessments stop short. Consultants who conduct assessments as document and interview exercises produce assessments that reflect what people believe about the data rather than what is actually there. The inventory report says accuracy is 98.5%. A query against the material document history and the balance sheet comparison reveals a systematic discrepancy in the accounts for stock-in-transit that the accounting team has been manually adjusting for two years. The query finds this. The interview does not.

The output of a useful manufacturing data maturity assessment is not a score. It is a list of specific data problems, each with a business impact (the cost or risk of the problem), a root cause (the system or process failure driving it), and a recommended fix (the specific data engineering or governance change that addresses it). These are sorted by impact-to-effort ratio to produce a prioritised action plan.

The action plan from a manufacturing data maturity assessment should have three horizons: quick wins (fixable in 30 days with minimal investment), medium-term investments (6-12 months, moderate budget), and platform investments (12-24 months, significant budget). Most assessments identify 4-6 quick wins, 3-5 medium-term investments, and 1-2 platform investments.

Quick wins are typically data quality fixes: correcting a persistent reconciliation error, adding a validation rule that prevents a data entry mistake, or automating a manual report that the finance team produces weekly. These are high-credibility early actions that demonstrate analytical leadership without requiring budget approval.

Medium-term investments are typically integration projects: connecting the MES to SAP, building a proper OEE calculation that the operations team trusts, or connecting the field force app to the ERP for secondary sales visibility. These have clear business cases and measurable outcomes that can be defined before the investment is approved.