In Three Points
What it solves
Turn dispersed shop-floor records into operating evidence that management can use.
How it proceeds
Use business goals and metrics to align master data, definitions, data flows, and quality rules.
What the client receives
A metrics system, master-data rules, data-flow maps, application scenarios, and a governance roadmap.
Common Challenges
There is plenty of data, but operating reviews still cannot reach a shared conclusion
Data governance is not merely data cleaning. It makes the data captured through operating systems trustworthy, usable, and reviewable.
Customer, product, BOM, routing, supplier, and material master data is maintained by multiple parties.
Order, production, procurement, warehouse, quality, and finance data is scattered across systems, spreadsheets, and personal experience.
Sales, production, finance, and warehouse teams use different definitions for the same metric.
Data-governance Flow
Move from consistent definitions to practical data use
First let systems capture real business data, then create assets through definitions, accountability, and quality management, and finally apply them to analysis and alerts.
Metric Definitions
Break down critical metrics for delivery, capacity, efficiency, quality, inventory, cost, and collections.
Master Data
Organize product, customer, supplier, material, BOM, routing, equipment, warehouse, and people data.
Data Flows
Identify where data originates, which systems it crosses, who uses it, and where it becomes distorted.
Application Scenarios
Build reports, dashboards, alerts, and analysis for operating reviews and management decisions.
Deliverables
