Data with owners, rules and measurable quality.
III Governance and quality
When figures don't match across departments, the problem is rarely the tool. We define who owns each piece of data, which rules govern it and how its quality is measured.
Services
2 serviciosGovernance, Data Office and CoE
A formal governance structure: owner and steward roles, management policies and a center of excellence.
Deliverables
- Role model by domain
- Data management policies
- Governance committee and rituals
- Center of excellence
Data quality
Standards, automated rules, control dashboards and continuous remediation processes.
Deliverables
- Quality rule catalog
- Automated monitoring
- Quality dashboard by domain
- Remediation process
In code
Quality rules that run on their own.
Every rule is an automated test: if sales don't reconcile with the accounting close, the team knows before management does.
version: 2 models: - name: ventas_diarias description: Daily net sales by store and category columns: - name: id_venta_dia tests: [unique, not_null] - name: venta_neta tests: - dbt_utils.accepted_range: min_value: 0 - name: id_tienda tests: - relationships: to: ref('dim_tienda') field: id_tienda
YAML · 17 lines● main · reviewed
Questions we answer
Why don't the numbers match?
Because each department defines and calculates differently; we fix it with single definitions.
Who is accountable?
Every domain has a business owner and a technical steward.
How do we know it improved?
Quality is measured with rules and published on a dashboard.
Related
Do you have a data or AI challenge?