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Data Quality Dashboard (Emissions Validations)

Data Quality is a new dashboard in Cozero that automatically checks your emissions log data for common quality issues, so you can catch and fix them before they surface during reporting. 

Instead of manually cross-referencing quarter-over-quarter figures in a spreadsheet or waiting on a data quality report from Cozero's team, you can now review flagged records directly in the platform, filter down to what needs attention, and act on them — dismiss, download, or delete — without leaving Cozero.

The dashboard runs two kinds of checks. Log entry-level validations flag individual records with issues like zero emissions, suspicious placeholder text, potential duplicates, or logs with no entries. Aggregated validations look across your data by location, category, subcategory, and time period to flag outliers and data gaps that only become visible when you zoom out.

Who is this for?

Data Quality is built for anyone responsible for signing off on emissions data quality before it goes into a report — especially organizations managing emissions data across many business units, locations, or subsidiaries, where manual review doesn't scale.

  • Sustainability managers preparing quarterly or annual emissions reports
  • Data owners overseeing emissions logs across multiple business units or locations
  • Teams that currently rely on manually built spreadsheets or on-demand data quality reports to catch missing entries, unexpected zeros, or suspicious spikes

How to access it

Navigate to Log > Data Quality in the left-hand sidebar of your Cozero workspace.

At the top of the page you'll see a row of filter chips indicating the categories of issues found in your data, for example:

  • Emissions are zero — entries where the result is 0 or missing
  • Suspicious text — entries with placeholder-style text (e.g. "test", "tbc", "demo")
  • Potential duplicate — identical entries that may be double-counted
  • Log without entry — logs with no entries recorded
  • Calculation error — entries with calculation issues
  • Outliers — months with unusually high or low emissions for a location/subcategory
  • Data gaps — months with no data at all for a location/subcategory

Each chip shows how many records are currently flagged. Click a chip to filter the table to just that category. The toolbar also includes options to filter (just like in the log entry overview) on the Log-entry level validations and an option to add or remove columns on you current view.

Step-by-step guide

Step 1: Open the Data Quality dashboard

  1. In the left sidebar, expand Log.
  2. Select Data Quality.
  3. The table loads with all validation categories shown as chips across the top, each with a count of currently flagged records.


Step 2: Review flagged log entries

  1. Click a chip (e.g. Potential Duplicate or Suspicious Text) to filter the table to that issue type.
  2. Use the table columns — Subcategory, Category, Location, Business Unit, and monthly period columns — to identify where the issue occurs.
  3. Filters can also be applied to narrow down issues by category, location, BU etc.



Step 3: Investigate and take action

  1. Select one or more flagged rows using the checkboxes.
  2. Open the Actions dropdown to:
    • Dismiss (for log entry level validations) the flag if the value is confirmed correct
    • Download the flagged list to share with the team responsible for fixing it
    • Delete (for log entry level validations) the underlying log entries, if appropriate
  3. Click into an individual log to open its detail page and edit it directly.

Step 4: Review aggregated validations (outliers and data gaps)

  1. Next we move to the aggregated validations to see Outliers and Data Gaps grouped by location, category, subcategory, and time period.
  2. Click a flagged row to open a filtered view of your Log Entry Overview (LEO) table with the corresponding logs, pre-filtered by location, category/subcategory, and period.
  3. Download the full aggregated table, including visible columns, if you need to share it.





Understanding log entry-level vs. aggregated validations

Data Quality checks your data in two different ways, and it's worth knowing the difference so you look in the right place for the right kind of issue.

Log entry-level validations look at individual records one at a time — is this specific entry zero, duplicated, missing text, or part of a log with no entries at all? These are quick to fix one by one or in bulk.

Aggregated validations look at your data in groups — by location, category, subcategory, and month — to catch problems that aren't obvious from a single entry, like an unusually high or low month of emissions, or a month where no data was logged at all for a given location and subcategory. These checks work best when you have a full year or more of historical data to compare against, since they rely on spotting deviations from your own reporting history.

Note that a value of zero isn't always a data quality issue — some activities, like renewable electricity with a zero emission factor, are genuinely expected to be zero. The Emissions are zero check is designed to exclude these valid cases and only flag zeros that look unintentional.

Frequently asked questions

What counts as a "potential duplicate"? Two or more log entries are flagged when their key input fields — location, date, activity data source, and rounded input values — are identical, suggesting the same activity may have been logged more than once.

Why isn't every zero-emission entry flagged? The check excludes cases where a zero result is expected, such as renewable electricity with a zero emission factor, so you only see zeros that are likely to be genuine data issues.

Will Cozero automatically fix flagged issues for me? No. Data Quality flags issues for your review, but Cozero does not automatically change your data or push corrections back to your source systems.

What happens if I dismiss a flag? Dismissed flags are remembered, so a record you've confirmed as correct won't keep reappearing on future scans unless the underlying data changes.

Does this replace the data quality reports I currently request from Cozero's team? Data Quality is designed to give you the same visibility directly in the platform, reducing the need for manual, on-demand reports. Reach out to your Cozero contact if you still need a report for a specific case.

Can I add my own custom validation rules? Not currently. Custom validations, AI-driven pattern detection, and data enrichment are being considered for future iterations.

Questions or feedback

If you run into any issues during the process or have questions about the process, contact us at support@cozero.io.