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FREE WEBINAR 04.12 at 10:00

Data quality validation and alerting inside of PowerBI; road to cleaner DIMs with minimal ETL

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What You’ll Learn — and Why It Matters

During this webinar, you’ll see how to detect, separate and monitor low-quality data inside Power BI without building a heavy data-engineering stack. Everything we show is based on real project patterns you can immediately apply in your analytics or BI team.

01 Why data quality issues appear in the first place
02 The real causes of poor data quality
03 Why minimal ETL is better than no ETL
04 Splitting “good” and “bad” records — the foundation of alerting
05 Report Deep Dive: Structure, Logic & Alerting
01 Why data quality issues appear in the first place

Why data quality issues appear in the first place

You’ll learn why CRM systems and manual inputs are the most common source of invalid data—and why small flaws propagate quickly when no validation layer exists.

This is for you if:

  • You constantly discover errors after reports are published.
  • You suspect your CRM or operational systems contain duplicates, missing fields, or inconsistent values.
02 The real causes of poor data quality

The real causes of poor data quality

We’ll walk through typical patterns that generate “dirty” records: manual edits, merged sources, inconsistent type handling, and the absence of safe ETL processes.

This is for you if:

  • You rely heavily on manual exports, merges or ad-hoc transformations.
  • You want a more predictable and auditable data flow.
03 Why minimal ETL is better than no ETL

Why minimal ETL is better than no ETL

Power BI developers often remember the star schema — but forget the rest of Kimball.
You’ll see how even a very small ETL layer helps you detect anomalies, track Slowly Changing Dimensions (SCDs), and isolate invalid records before they reach reports.

This is for you if:

  • You want better-quality DIMs without building a full data warehouse.
  • You need a repeatable process to flag incorrect or incomplete rows.
04 Splitting “good” and “bad” records — the foundation of alerting

Splitting “good” and “bad” records — the foundation of alerting

We’ll show how creating parallel “valid/invalid” data paths makes quality measurable, transparent and easy to monitor. This small design decision enables alerting, scoring, and incremental improvement.

This is for you if:

  • You want to reduce time spent chasing data errors at the end of each month.
  • You need visibility into where your data gets corrupted.
05 Report Deep Dive: Structure, Logic & Alerting

Report Deep Dive: Structure, Logic & Alerting

We’ll show how creating parallel “valid/invalid” data paths makes quality measurable, transparent and easy to monitor. This small design decision enables alerting, scoring, and incremental improvement.

This is for you if:

  • Your team reacts to problems instead of preventing them.
  • You want a scalable way to notify data owners when issues appear.

Don’t miss this chance to see how to build transparent, automated data-quality validation directly inside Power BI.

Sign up for the webinar!

FAQ

Do I need to install anything?

No — the webinar runs completely in your browser.

Is the webinar free?

Yes, participation is entirely free.

Can I ask questions during the session?

Absolutely — we’ll have a dedicated Q&A segment at the end.

Will the recording be available?

Yes, all registered participants will receive access to the recording afterwards.