Reverse Summarize in Power BI: Rebuilding Granular Data from Aggregates

Overview

Reverse summarization in Power BI is the practice of reconstructing synthetic row‑level data from already aggregated tables - such as totals by age group, region, or category - when the original granular records are unavailable. Instead of recovering the true underlying data, the technique expands each aggregate into multiple representative rows, creating a micro‑dataset that mirrors the structure implied by the summaries. This approach becomes valuable when organizations inherit pre‑summarized exports, face privacy restrictions, or need granular‑style insights for modelling, visualization, or simulation.

By rebuilding detail from aggregates, analysts can unlock visuals and analytical methods that require row context, run statistical or machine‑learning workflows, or blend summarized data with other granular sources. While the resulting dataset is synthetic and must be interpreted carefully, reverse summarization offers a practical bridge between limited source data and the richer analytical capabilities that Power BI enables.

Steps to Reverse Summarize in Power BI

In this example I am going to use the summarized data below to re-construct synthetic row-level data.

Load your data in Power BI.

• Navigate to Modelling tab in your Power BI Desktop ribbon.

• Select New Table.

Create the following DAX expression to reverse-summarize the above table.

(Note: the above expression expands the summarized table back into multiple rows by replicating each row according to a count column – in this case “Sample”)

Interpretation of DAX

• Data: Is the summarized table.

• GENERATESERIES(1, Data[Sample]): Creates a series of numbers from 1 → Sample for each row.

• GENERATE: Performs a row-by-row cross-join, meaning: If Sample = 3, it creates 3 rows. If Sample = 1, it creates 1 row.

See the Resulting De-Summarized Table

(Note: The De-summarized table contains 37 rows, which is a row-expanded version of the summarized table with 12 rows)

Conclusion

Reverse summarization in Power BI ultimately reminds us that analytics is not just about what data we receive, but what insights we can create. When organizations are limited to aggregated exports or privacy‑protected summaries, the ability to reconstruct meaningful, row‑level structures becomes a powerful enabler - unlocking richer visuals, deeper modelling, and more flexible decision‑support workflows. Although the resulting microdata is synthetic, the analytical opportunities it opens are very real.

As Power BI continues to evolve, techniques like reverse summarization will play an increasingly important role in bridging the gap between constrained data sources and modern analytical expectations. By thoughtfully rebuilding granular detail from aggregates, analysts can extend the life and value of imperfect datasets, support more advanced use cases, and deliver insights that would otherwise remain out of reach.

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