Turn “Ad-Hoc Chaos” into Trusted Self-Service BI
Self-service BI works when the data foundation removes friction for business users. Our reusable dimensional framework gives you trusted definitions, “as-was” and “as-is” choices, foolproof joins, and upstream data quality checks, so teams can explore confidently without hand-coding SQL or exporting to Excel (or god help us all MS Access).
Why Self-Service Often Fails
The BI tool isn’t the problem, the data foundation is.- Every team rewrites logic for the same KPIs (potentially "enriching" the data to match the story they are selling.)
- Reports disagree because joins and definitions drift.
- “Ad-hoc” turns into days of wrangling, reconciliation and frustration for everyone involved.
What Business Users Actually Need
- Trusted definitions – conformed dimensions so “Customer,” “Product,” and “Calendar” mean the same thing everywhere.
- History & current state – pick as-was for historical truth or as-is for today’s snapshot without knowing SCD mechanics. Need as-of a specific date, yup we can do that also!
- Foolproof joins – facts and dimensions are modeled to align, preventing double-counts and dead-ends. Business users should not have to know or understand the difference between an Inner and Outer Join.
- Guardrails, not gates – validation and reconciliation run upstream so analysts don’t inherit silent data quality issues. Data Quality reports are built into the process.
The Enablement Pattern (Business View)
- Conformed dimensions unify subject areas so Marketing and Finance reconcile automatically.
- Automated versioning handles change over time; users simply choose as-was vs as-is.
- Utility-driven processing maintains dimensions consistently, no one-off pipelines.
- Deterministic fact loads resolve keys the same way, every time, preventing logic drift.
- Built-in reconciliation flags duplicates, lost rows, and null-key exceptions before BI sees the data.
What the BI Experience Looks Like
- Connect once to the curated Reporting schema (not the raw ODS).
- Drag-and-drop in Tableau/Power BI/Looker—joins and keys are already conformed.
- Need history? Use the as-was dimension. Need current state? Use the
_currtable.
Why This Scales on Greenplum
- Set-based, MPP-friendly utilities handle large dimensions without per-table custom code.
- Faster delivery: new attributes flow through the framework—no rewrites.
- Predictable performance with appropriate distribution, partitioning, and analyze routines.
Get Started Checklist
- Confirm conformed dimensions and the grain of your key facts.
- Choose the primary business date (drives partitioning/retention).
- Stand up the curated Reporting schema and publish one governed BI data source per domain.
- Enable the as-was and as-is dimension pattern; document it in your BI data dictionary.
- Turn on recon dashboards (dupes, lost rows, null key exceptions) for data ops, not end users.
For Engineers (Related Blogs)
Need a hand? We implement this framework in Greenplum/Postgres, help you wire it to your BI tool, and leave you with repeatable utilities. Contact Mugnano Data Consulting.
