One name, one meaning; one thing, one code
Data standards, metadata and lineage, master data management
The same metric with different definitions in two departments, the same customer with three codes in three systems: untrustworthy data usually starts at the definition layer. We build the data standards system and dictionary, collect technical and business metadata with field-level lineage, identify master data domains and design golden records, so definitions, origins and identifiers are on record.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Standards start with the most disputed metrics
No wholesale standardisation. The metrics and master data domains with the most definition conflicts and the widest report impact go first; the method then extends domain by domain.
Metadata is collected automatically
Hand-registered metadata is stale within three months. Lineage and technical metadata are harvested from databases, schedulers and BI tools; business metadata is maintained by data owners inside the platform.
Master data gets an owner first
Each master data domain has a named owner and change process; golden-record matching and merge rules are confirmed by the owner before execution.
Deliverables
Data standards and dictionary
Foundational, metric and master data standards, coding rules, with adoption mapping and conformance check rules.
Metadata platform and lineage
Technical and business metadata collection, field-level lineage and data map, with impact analysis.
Master data model and operating mechanism
Domain definition, golden-record rules, distribution interfaces and change process.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Inventory
System inventory, metric definition conflicts, master data distribution.
Standards and model design
Standards system, master data model, metadata collection plan.
Platform rollout
Metadata collection live, master data cleansing, merging and distribution.
Adoption and operations
Conformance checks, change management, quarterly review.
Customer story
Reallysec Empowers Siemens Digital Industries, Leading the Intelligent Future
With visualisation and analytics in place, Siemens DI gained full-path visibility into product master data flows, plus anomaly self-healing.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
A data standard defines business meaning, value range and owner; the dictionary is the standard's technical implementation in a specific system, recording field names, types and mappings. Both are maintained by one process.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.