NexusGreenData measures your data against the NDMO data-quality dimensions on top of its own built-in test framework, turns failures into tracked issues with SLAs, and generates the scorecards and evidence the National Data Index asks for.
Data quality management measures whether your data is fit for use and keeps it that way. NexusGreenData runs every check through one built-in test framework and adds the NDMO dimension model, a rules catalogue, an issue register and scorecards on top.
Business rules expressed in plain terms are mapped to technical test cases, so the quality a steward cares about and the test an engineer runs stay in sync.
What it does
Score completeness, validity, uniqueness, consistency, timeliness and accuracy, combined into a configurable dq.v1 score.
A catalogue links business rules to technical test cases, kept in two-way sync so nothing drifts.
Mark the data that matters most; critical data elements are weighted more heavily in the score.
Failures raise issues with severity-based SLAs and a remediation workflow — assigned, tracked and closed.
Scorecards roll quality up by domain and asset over time, with Arabic and English exports.
Results map to the NDI data-quality maturity questions, so your scorecards double as readiness evidence.
Business rules are defined against catalogued data assets; NexusGreenData maps each to a technical test case and runs the checks on schedule. Results are scored against the NDMO dimensions, with critical data elements weighted more heavily.
Failures become issues with severity-based SLAs and a remediation workflow. Daily snapshots build scorecards over time, and evidence generators produce the data-quality readiness pack for the National Data Index.
| Foundation | Built-in test framework (one engine runs every check) |
|---|---|
| Dimensions | Completeness, validity, uniqueness, consistency, timeliness, accuracy |
| Scoring | Configurable dq.v1 model with critical-data-element weighting |
| Issues | Register with severity SLAs and remediation workflow |
| Outputs | Scorecards and NDI data-quality evidence, Arabic and English |
| Languages | Arabic and English, right-to-left |
Who it is for
Teams that need business rules, technical tests and issues in one place instead of disconnected scripts.
Owners of the NDI data-quality domain who must show measured, improving quality with evidence, not assertions.
Leaders whose dashboards are only as trustworthy as the data behind them, and who need that trust made visible.
FAQ
No. Existing technical tests are imported and kept, business rules are catalogued and kept in sync with them, and one engine runs every check, so nothing has to be rewritten.
The NDMO data-quality dimensions — completeness, validity, uniqueness, consistency, timeliness and accuracy — combined into a dq.v1 score, with critical data elements weighted more heavily.
Failures raise issues in a register with severity-based SLAs and a remediation workflow. Issues can be assigned, tracked and closed, and the history feeds scorecards.
Yes. Data-quality evidence generators map results to the NDI data-quality maturity questions, so scorecards double as readiness evidence.
See NexusGreenData score your data against the NDMO dimensions, raise and track issues, and generate NDI data-quality evidence.