Enterprise analytics governance is the backbone of trustworthy manufacturing data — defining who owns each metric, how it is calculated, who can access it, and how changes are controlled. Without governance, plants face conflicting KPI definitions, unaccountable data quality issues, and compliance gaps that undermine decision-making at every level. This checklist covers seven critical governance dimensions — from metric registry and data ownership assignment to change control workflows, role-based access matrices, policy reference cards, and a complete implementation checklist — enabling manufacturing enterprises to deploy analytics governance frameworks that keep data consistent, secure, and auditable across every plant and business unit.
Governance Health
Assess Your Analytics Governance Health with iFactory
iFactory's analytics governance module provides a complete health scoreboard — tracking metrics documented, data owners assigned, change control compliance, and governance maturity level across every plant. Governance leads get real-time visibility into framework adoption with trend tracking and automated gap identification that highlights which governance dimensions need attention before they cause data quality or compliance issues.
Analytics Governance Health Scoreboard: Enterprise Readiness Overview
The governance health scoreboard provides a four-metric snapshot of your enterprise analytics governance maturity — tracking the percentage of metrics formally documented, the share of data domains with assigned owners, change control compliance rate across the last quarter, and the current governance maturity level. These leading indicators reveal whether your governance framework is keeping pace with analytics adoption across plants.
Data Ownership & Stewardship Assignment Table: Role Mapping by Domain
Clear data ownership is the first principle of analytics governance. Every metric and data domain must have a named owner responsible for definition accuracy, a steward managing day-to-day quality, and a delegate covering absences. The table below assigns ownership across eight enterprise data domains with role contact and status indicators for each assignment.
| Data Domain | Data Owner | Owner Status | Data Steward | Steward Status | Delegate | Review Date |
|---|---|---|---|---|---|---|
| Production Output & OEE | VP Operations | Confirmed | Production Analytics Mgr | Active | Senior OEE Analyst | 2026-03-15 |
| Quality & FPY Metrics | VP Quality | Confirmed | Quality Systems Mgr | Active | Senior Quality Engineer | 2026-03-12 |
| Maintenance & Asset Health | VP Maintenance | Confirmed | CMMS Administrator | Pending | — | 2026-03-10 |
| Energy & Utility Consumption | Plant Engineer | Confirmed | Sustainability Analyst | Active | Energy Technician | 2026-02-28 |
| Supply Chain & Inventory | Supply Chain Director | Confirmed | Inventory Control Mgr | Active | Materials Planner | 2026-03-01 |
| Financial & Cost Metrics | CFO / Finance Director | Confirmed | Cost Accounting Mgr | Active | Financial Analyst | 2026-03-05 |
| Safety & Environmental | EHS Director | Unconfirmed | EHS Coordinator | Active | Safety Technician | 2026-01-20 |
| Workforce & Labour Metrics | HR Director | Unconfirmed | Workforce Analyst | Pending | — | 2026-01-15 |
Metric Definition Registry: Standardised KPI Documentation Table
A central metric registry is the single source of truth for every KPI used in manufacturing analytics. Each metric must have a unique identifier, clear definition, explicit formula, approved data source, refresh cadence, named owner, and current approval status. The registry below documents ten critical manufacturing metrics with their full governance metadata — enabling consistent interpretation across plants, dashboards, and business reviews.
| Metric ID | Metric Name | Definition | Formula | Data Source | Refresh | Owner | Status |
|---|---|---|---|---|---|---|---|
| MTR-001 | Overall Equipment Effectiveness | Ratio of actual output to theoretical maximum output over planned production time | Availability × Performance × Quality | MES / Machine Data | Daily | VP Operations | Approved |
| MTR-002 | First-Pass Yield | Percentage of units passing inspection on first attempt without rework | (Total − Scrap − Rework) / Total × 100 | Quality Inspection System | Shift | VP Quality | Approved |
| MTR-003 | Mean Time Between Failures | Average operating time between unplanned equipment stops | Total Operating Hours / Number of Failures | CMMS / Maintenance Log | Weekly | VP Maintenance | Approved |
| MTR-004 | On-Time Delivery | Percentage of customer orders shipped on or before the requested delivery date | Orders Shipped On-Time / Total Orders × 100 | ERP / Order Management | Daily | Supply Chain Director | Approved |
| MTR-005 | Scrap Rate | Percentage of total production that is scrapped at any stage | Scrap Units / Total Units Produced × 100 | Quality System | Shift | VP Quality | Approved |
| MTR-006 | Energy Intensity | Energy consumed per unit of production output | Total kWh / Units Produced | Energy Management System | Hourly | Plant Engineer | Draft |
| MTR-007 | Overall Labour Effectiveness | Ratio of actual output per labour hour to standard output per labour hour | Actual Output / (Labour Hours × Standard Rate) | HR / Time Tracking | Weekly | HR Director | Draft |
| MTR-008 | Inventory Turnover | Number of times inventory is sold or used over a period | COGS / Average Inventory Value | ERP / Inventory | Monthly | Supply Chain Director | Approved |
| MTR-009 | Safety Incident Rate | Number of recordable safety incidents per 200,000 labour hours worked | Incidents × 200,000 / Total Hours Worked | EHS System | Monthly | EHS Director | Approved |
| MTR-010 | Cost Per Unit | Total production cost allocated to each unit produced | Total Production Cost / Total Units Produced | ERP / Costing Module | Monthly | CFO / Finance Director | Approved |
Change Control Workflow Cards: Metric Lifecycle Management Process
Every change to a metric definition, formula, or data source must follow a controlled workflow to prevent downstream dashboard breakage, KPI misalignment, and reporting discrepancies. The six-step change control process below ensures that all metric modifications are reviewed, approved, and validated before deployment — with clear ownership and expected duration at each stage.
Control Changes
Automated Change Control Workflows with iFactory
iFactory's governance module includes configurable change control workflows that route metric modification requests through review, impact assessment, approval, implementation, and validation stages — with automated notifications, version tracking, and full audit trail at every step to prevent unauthorised metric changes from reaching production dashboards.
Role-Based Access Control Matrix: System Function Permissions by Role
Controlling who can view, edit, or approve analytics content is essential for data integrity and compliance. The matrix below maps six functional roles against eight system capabilities — with filled dots indicating granted permissions and empty dots for restricted access — ensuring every user has precisely the access they need without over-provisioning sensitive governance functions.
| Role | View Dashboards | Create Reports | Edit Metric Definitions | Approve Changes | Manage Users | Data Source Config | Export Data | Audit Log Access |
|---|---|---|---|---|---|---|---|---|
| Plant Operator | ||||||||
| Plant Supervisor | ||||||||
| Analytics Team | ||||||||
| Data Owner | ||||||||
| Governance Lead | ||||||||
| System Admin |
Analytics Governance Policy & Standards Reference Cards
A robust governance framework is built on clearly documented policies and standards that every plant and analytics team must follow. The six policy cards below cover the essential governance domains — from naming conventions and data quality rules to retention policies, security classification, access control, and audit requirements — providing quick-reference guidance for governance implementation across the enterprise.
Analytics Governance Implementation Checklist
Use this checklist to implement enterprise analytics governance across your manufacturing organisation — from metric registry setup and data ownership assignment to change control workflows, role-based access control, policy documentation, and audit readiness. Each task includes a tick column for completion tracking, implementation category, responsible owner, estimated duration, and priority level.
| # | Task | Category | Owner | Duration | Priority | |
|---|---|---|---|---|---|---|
| 1 | Establish governance board with executive sponsor and cross-functional representation from operations, quality, maintenance, supply chain, finance, and IT | Organisation | VP Analytics | 2 weeks | Critical | |
| 2 | Create central metric registry with unique IDs, definitions, formulas, data sources, refresh cadence, and owner for every manufacturing KPI | Registry | Analytics Lead | 3 weeks | Critical | |
| 3 | Assign data owners and stewards for all eight data domains with confirmed acceptance and delegate coverage for absences | Ownership | Governance Board | 2 weeks | Critical | |
| 4 | Define and document governance policies: naming conventions, data quality rules, retention policy, security classification, access control, and audit requirements | Policy | Governance Lead | 3 weeks | High | |
| 5 | Configure role-based access control matrix in analytics platform with least-privilege permissions per role and quarterly review cycle | Access | IT / Analytics | 2 weeks | Critical | |
| 6 | Implement change control workflow with submit, review, impact assessment, approval, implementation, and validation stages | Process | Analytics Lead | 2 weeks | High | |
| 7 | Establish data quality monitoring with automated threshold checks and quality warning banners on dashboards for sub-threshold metrics | Quality | Data Stewards | 3 weeks | High | |
| 8 | Configure audit log capture for all governance actions — metric changes, access grants, data source modifications, and approval decisions | Audit | IT / Analytics | 1 week | High | |
| 9 | Conduct initial governance maturity assessment across all plants — score current state against target maturity for each governance dimension | Assessment | Governance Board | 4 weeks | Medium | |
| 10 | Schedule recurring governance review cadence — monthly operational reviews, quarterly board reviews, annual external audit for regulated plants | Sustain | Governance Lead | 1 week | Medium |
Frequently Asked Questions
What is the difference between a data owner and a data steward?
A data owner is a senior business leader accountable for a data domain — they approve metric definitions, set data quality standards, and authorise access requests. A data steward is a day-to-day operational role responsible for implementing those standards, monitoring data quality, and managing metadata. In manufacturing, the VP Operations might be the data owner for production metrics while a Production Analytics Manager serves as the data steward running the daily quality checks and registry updates.
How often should the metric registry be updated?
The metric registry should be updated whenever a metric definition, formula, data source, or owner changes — following the governance change control workflow. In addition, a full registry review should be conducted quarterly by the governance board to validate that all entries remain accurate, owners are still assigned, and retired metrics are formally archived. iFactory's governance module automates review reminders and maintains a complete version history of every registry change.
What is a good starting maturity level for analytics governance?
Most manufacturing organisations start at L1 (Initial) or L2 (Repeatable) on a five-level governance maturity model. L1 means governance is ad-hoc with no formal processes. L2 means some documentation exists but isn't consistently enforced. The target for most enterprises is L3 (Defined) — documented policies, assigned owners, regular review cadence, and automated tool support. L4 (Managed) adds quantitative governance metrics and proactive quality monitoring. L5 (Optimising) uses governance data to drive continuous improvement.
How does iFactory enforce governance policies?
iFactory embeds governance directly into the analytics platform. The metric registry enforces naming conventions and prevents duplicate metric creation. Change control workflows route modifications through mandatory approval stages before publishing to production dashboards. Data quality rules are evaluated on every data refresh — sub-threshold metrics trigger quality banners on dashboards. Security classification labels control dataset visibility. All actions are logged to the audit trail with immutable records. No governance action can be bypassed or overridden without leaving an audit trace.
What metrics should be included in the governance health scoreboard?
The four essential governance health metrics are: (1) Percentage of metrics formally documented in the registry — measuring completeness of KPI documentation. (2) Percentage of data domains with confirmed owners and stewards — measuring accountability coverage. (3) Change control compliance rate — the percentage of metric changes that followed the approved workflow in the last quarter. (4) Governance maturity level — an aggregate score based on the five-level maturity model covering people, process, technology, and compliance dimensions.
Ready to Govern
Start Your Analytics Governance Journey with iFactory Today
iFactory provides a complete analytics governance platform for manufacturing enterprises — from metric registry and data ownership to change control, access management, policy enforcement, data quality monitoring, and audit compliance. Our team can help you assess your current governance maturity, design a target-state framework, and deploy governance automation across your plant network in weeks, not months.
