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Variable scores the quality of every input and dataset using a Data Quality Rating (DQR) based on the PACT Methodology v2.0. The aggregate DQR gives you - and anyone consuming your data - a quick read on how confidently a footprint can be reported.
Lower is better. The best possible score is 1, the worst is 3.

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How the rating is shown

Each input row and each dataset surfaces a single aggregate DQR (e.g., 1.4, 2.0). The number is colored to make hotspots obvious: The aggregate is the average of the five dimension scores below. Dimensions left blank don’t count toward the average - but they also reduce confidence, so fill them in where you can.

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The five dimensions

Each dimension is rated Good (1), Fair (2), or Poor (3).

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Technological representativeness

How well the data represents the actual technology used.

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Temporal representativeness

How current the data is relative to your reporting period.

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Geographical representativeness

How well the data’s location matches yours.

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Completeness

How much of the underlying activity is actually covered by the data.

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Reliability

How the data was obtained.

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Primary vs secondary data

Alongside the DQR, Variable tracks the share of primary (specific, measured) versus secondary (averaged, modeled) data behind a footprint. You’ll see this as two percentages that add to 100%:
  • Primary data % - directly measured at your site or supplier
  • Secondary data % - database, sector, or estimated values
The split is informational - it doesn’t change the DQR - but it gives reviewers a sense of how much of the result rests on direct measurement.

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Document your ratings

Alongside the scores, you can record a free-text data quality description - why those ratings were chosen, which production years and background datasets the figures rest on, and any caveats a reviewer should know. EN 15804 expects this narrative “data quality discussion” next to the ratings. Edit it in the data quality panel beside the dimension selectors, on both products and materials. Like the ratings themselves, it’s read-only for externally synced materials. Over the public API and MCP it reads and writes as dataQualityIndicators.description. A Declaration has its own separate data quality discussion, edited alongside the other LCA methodology fields on the Declaration itself. That’s the one carried into a published EPD and sent to program operators - the product and material descriptions above stay in Variable. Filling in one doesn’t populate the other, so write the discussion on the Declaration you’re publishing. See Publishing EPDs.

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Where DQR shows up

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Improving your rating

Focus where it matters most:
  1. Find your hotspots. Sort inputs by impact contribution.
  2. Check their DQR. A high-impact input with DQR 2.5+ is your highest-leverage fix.
  3. Replace secondary with primary. Supplier-specific datasets (or a verified EPD) typically move all five dimensions toward 1.
  4. Re-run. The aggregate updates as soon as you re-assign the dataset.
Improving DQR on low-contribution inputs is rarely worth the effort. Always prioritize by share of total impact.