Your org chart is only as good as the data behind it. The Data Quality Dashboard gives you a single score from 0 to 100, shows exactly what is wrong, and tells you how to fix it.
The Data Quality Score is a composite number between 0 and 100 that represents how complete and accurate your Google Workspace directory data is. A score of 100 means every employee has a name, title, department, manager, email, and photo set correctly. A lower score means some of that data is missing or inconsistent.
The score matters because missing data creates problems downstream. An employee without a manager assigned will appear as a “floating” node at the top of the org chart. A department with no name shows up as “Unassigned.” These gaps confuse anyone looking at the chart and reduce trust in the tool.
The Data Quality Dashboard runs six automated checks against every employee in your directory. Each check is pass/fail and contributes to the overall score:
| Check | What It Catches | Why It Matters |
|---|---|---|
| Missing Titles | Employees with no job title set in Google Workspace. | Org chart cards display a blank where the title should be, making the chart hard to read. |
| Missing Departments | Employees with no department assigned. | They fall into an “Unassigned” bucket and are excluded from department-level analytics. |
| Missing Managers (Orphans) | Employees with no manager assigned who are not the CEO. | They appear as disconnected root nodes at the top of the tree, breaking the hierarchy. |
| Duplicates | Two or more entries that appear to be the same person (same name and email). | Inflates headcount numbers and creates confusing duplicate cards in the chart. |
| Invalid Emails | Email addresses that do not follow a valid format. | Clicking the email link from the chart would fail or send to the wrong address. |
| Missing Photos | Employees who have not uploaded a profile photo. | A chart with photos is more recognisable. Missing photos reduce usability, especially for new hires learning names. |
Each failed check reduces the score proportionally. For example, if 10% of employees have no title and 5% have no manager, the score reflects both gaps weighted by their impact.
On top of the rule-based checks, the AI layer adds four capabilities that go beyond simple pass/fail:
The AI identifies patterns across your data that individual checks miss. For example, it might notice that “40% of Engineering employees have no title set” while the rest of the organisation is fine. This tells you the problem is localised to one department, likely due to a batch of recent hires whose titles were not filled in.
Here is a practical workflow for bringing a low score up quickly:
Check the severity column
Start with Critical and High issues. These have the biggest impact on your score and on chart usability.
Look for patterns
If the AI says "40% of Engineering has no title", that is one conversation with one hiring manager — not 50 individual fixes.
Fix issues in Google Admin Console
ChartPull is read-only. To update titles, departments, or managers, make the changes in your Google Workspace Admin Console. ChartPull picks up the changes on the next sync.
Trigger a manual sync
After making changes in Google Admin, click "Sync Now" in ChartPull to pull the updates immediately instead of waiting for the next automatic sync.
Review your new score
The dashboard updates automatically after the sync completes. You should see your score jump. Repeat for Medium and Low issues as time permits.
Real-world scenario
NovaTech — 400 employees, data quality score 62/100
When NovaTech first connected ChartPull, their Data Quality Score was 62 out of 100. The dashboard showed three high-severity issues:
The AI recommendation was clear: “Focus on Engineering first — 52 of your 101 missing fields are concentrated in one department.” The IT admin reached out to the VP of Engineering, who batch-updated titles in Google Admin Console that afternoon.
Over the next two weeks, the team worked through the remaining issues. The duplicates were resolved by deactivating old Google accounts. The orphan employees were assigned managers after cross-referencing with HR records.
Result: Score went from 62 to 94 in two weeks.
The CEO commented that the org chart “finally looked right” and started using it in board presentations.