Traditionally, ETL (Extract, Transform, Load) has been a one-way journey. Data is extracted from operational systems, it’s transformed into a clean, structured format and then it’s loaded into a data store (typically a warehouse or lakehouse), rinse and repeat.

But what’s after that? Business users build reports and dashboards and make a decisions from their insights, but the data itself never flows back into the systems where the work actually happens. It requires input into the source system to see these changes.

That’s changing. Fast.

From a One-Way Street to a Full Circle

Modern data engineering is undergoing a transformation. Instead of a linear process, ETL is becoming a loop.

In this new world:

  1. ETL still ingests and transforms data into your central warehouse.
  2. Translytical flows (currently in preview in Power BI) connect analytics and transactions, allowing end users to change the data in the data store.
  3. Reverse ETL uses exports from the data store can be used to push trusted and curated data back into operational systems like Salesforce, HubSpot and other CRM or ERP tools.

The result? A continuous cycle where decisions and actions feed back into analytics, generating new data that restarts the loop.  

Here’s a real-world example of how this might work:

  • Your warehouse calculates Customer Lifetime Value (CLV).
  • A translytical writeback task in Power BI is used to update what constitutes a high value customer.
  • “Reverse ETL” syncs that value directly into the source CRM.
  • Sales reps see a “High Value” flag in their CRM without needing to run a single query.

This is powerful, but until recently, it still required a separate tool to bridge analytics and action.  

Enter Power BI’s Translytical Task Flows

Microsoft’s new Translytical Task Flows in Power BI (currently in preview) take this concept further. They blur the line between analytical and transactional systems, allowing you to:

  • Trigger workflows directly from a report without switching apps.
  • Write back to databases in real time.
  • Blend decision-making and action in the same interface.

Imagine this scenario:

  1. A sales manager spots a drop in conversion rates for a region on a Power BI dashboard.
  2. Right from the same report, they adjust campaign targeting parameters in the CRM.
  3. That change takes effect instantly, without a single handoff to IT or an analyst.

This isn’t just analytic, it’s operational analytics in action.  

Why This Matters

The shift from linear ETL to circular data flows changes the role of analytics:

  • Empowers end users: They become active participants, not passive consumers.
  • Shortens the feedback loop: Insights turn into measurable action faster than ever.
  • Improves data quality: Actions taken feed new, richer data back into the warehouse.

In short, users don’t just analyse data anymore, they shape it.  

The Future of ETL is Circular

The days of “data in, insights out” are fading. The modern data stack, powered by innovations like Power BI’s Translytical Task Flows, is making data flow in both directions.

This is a future where:

  • Analysts still model and govern data for trust and consistency.
  • Business teams act on it immediately within their daily tools.
  • Every action generates new data, improving the next round of analytics.

It’s time to stop thinking of ETL as a pipeline. ETL is a circle, and the organisations that embrace that will close the gap between insight and action for good.

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