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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: How the aggregate is built depends on what you are looking at:
  • Anything the app shows as Dataset - a dataset you rate yourself, or any element with a dataset assigned - averages the five dimension scores below, counting a dimension you leave blank as Poor (3), so an unrated element scores worse than a rated one.
  • Anything shown as Model derives its rating from its own inputs’ ratings, weighted by impact contribution, plus the completeness and reliability entered on it. With no inputs, it’s the average of those two.
Either way, fill the dimensions in wherever 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 two places: on products and materials, in the data quality panel beside the dimension selectors; and on models and sub-models, under Data quality in Settings → Product info. Like the ratings themselves, it’s read-only for externally synced elements. 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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In a CSV export

Both CSV exports - Export CSV on a product, and Export on any inventory list - carry every dimension as its own column, so you can read the ratings on every element without opening each one. On an element shown as Dataset those five columns are the ones the aggregate averages, so you can see which dimension dragged it down; on a Model the aggregate comes from the element’s inputs instead, so the dimension columns won’t explain it.
A blank cell means unrated, not Poor. The dimension columns report what was actually entered, so you can tell a deliberate Poor rating from one nobody has filled in yet. Data_Quality is the aggregate the app shows, built as described above - on a dataset that means a blank dimension has already been counted as 3.
Every column above reports the element’s own ratings, as entered on it - not those of the dataset it references. On the product export, a processing or use-stage row is the exception: it reports the ratings of the footprint dataset behind the step. One more column identifies what you are looking at. Both exports spell it the same way, so you can key a spreadsheet on LCA_Group across the two files: Either side lists all of them, separated by a semicolon and a space, when there is more than one. Each row also carries the version of the element it describes - Version_Number and Version_State on the inventory export, Source_Version_Number and Source_Version_State on the product export. See Versioning.

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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.