Usage context
Context
01
Narrative · 01
Context
A data definition and quality layer is set up when critical fields, quality rules and indicators need to be written and shared.
Data infrastructure & integration
Data Definition and Quality Layer
We install data definitions, ownership for critical fields, quality rules and quality indicators in software on the data you collect.
Solution flow
01
Context
A data definition and quality layer is set up when critical fields, quality rules and indicators need to be written and shared.
02
What we do
We install a technical data-quality layer on the data you collect: definitions and ownership for critical fields, quality rules and quality indicators live in software. The project definition approves the field set, rules and indicator cadence; the data dictionary and quality dashboard update in the same system.
03
What you get
Data dictionary, quality-indicator dashboard and critical-field ownership matrix.
Our approach
What we do
02
Narrative · 02
What we do
We install a technical data-quality layer on the data you collect: definitions and ownership for critical fields, quality rules and quality indicators live in software. The project definition approves the field set, rules and indicator cadence; the data dictionary and quality dashboard update in the same system.
Deliverables
What you get
03
Narrative · 03
What you get
Data dictionary, quality-indicator dashboard and critical-field ownership matrix.
04
How we verify
Acceptance requires the critical-field set to be defined and owned in the data dictionary and the quality rules to run on live data and update the indicator dashboard. A sample rule breach is verified by being flagged in the system.
05
What is required
Requires the list of critical fields to track and their assigned owners, read access to source systems, and approval of the quality rules and thresholds.
How it works
Where the same metric is computed differently in two departments, meetings turn into arguments about definitions. This layer holds the definition, ownership and quality rules of critical fields in software on the data you collect; acceptance is by the rules running on live data and updating the dashboard.
Undefined field, unreliable decision
When critical fields have no written definition or owner, every dashboard shows a different reality. Without quality rules and indicators, bad data enters decisions unnoticed.
Dictionary and ownership
The field set to track, the quality rules and the indicator cadence are approved in the project definition; every critical field is tied to an owner. The data dictionary and the quality dashboard update in the same system.
A working quality rule
The rules run on live data; a sample breach is verified by being flagged in the system. The quality indicator continuously shows whether the data is ready for decisions.
What comes next
Once definition and quality are stable, OEE, cost and reporting dashboards run consistently on the same dictionary.
Record chain
Record context
From obligation to measured record to produced output.
01
Obligation / requirement
Production, traceability or quality record obligation
02
Required record
Shop-floor production and process records
03
What AEON produces
Timestamped operational records from measurement
What's included
- Data dictionary
- Quality-indicator dashboard
- Critical-field ownership matrix
- Definitions, ownership, quality rules and indicators for critical fields
Industry context
Industry contexts where this solution is often relevant.
Related solutions
Solutions often considered together.
Contact
Let’s clarify the scope together
We clarify a suitable scope together based on your site, industry and goals.