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Applies to BloodHound Enterprise and CE The Data Quality page helps you validate what data exists in your BloodHound database after collection and ingest. Use it to confirm that expected data sources appear, object counts change as expected, and collection coverage supports the analysis workflows you plan to use. It helps answer questions about the current shape and recent history of your database before you investigate findings, explore the graph, or troubleshoot collector output.

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What the Data Quality page is for

Use the Data Quality page after you upload or refresh data to:
  • Confirm that BloodHound ingested new or updated data.
  • Review object counts across supported data sources.
  • Compare current counts with historical collection trends.
  • Verify that expected object types appear for a directory, platform, or extension.
  • Check Active Directory data completeness for privileged collection coverage, including local groups and sessions.
Data quality does not replace collector logs or Cypher queries. If the page shows unexpected counts or coverage gaps, use it to identify where to investigate next.

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Before you begin

Before you review data quality, complete the collection or upload workflow for the data source you want to validate:
  • For Active Directory, collect and ingest SharpHound data.
  • For Azure and Entra ID, collect and ingest AzureHound data.
  • For OpenGraph data, verify or install the matching OpenGraph extension definition schema, then upload a conforming data payload. The Data Quality page does not support OpenGraph data.
    This feature is available under early access. Enable on the Administration > Early Access Features page to access it.
Wait for ingest and analysis processing to complete before you compare counts or trends.

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Review data quality

1

Open the Data Quality page

In BloodHound, go to Administration > Data Quality.The page opens with an aggregate view of included object data.
2

Review the total object count

Use the total object count at the top of the page to confirm the current amount of included object data in the database.The total helps you verify that a recent upload or collection run changed the database in the expected direction.
3

Review historical trends

Review the historical graph to understand how object counts changed over time.Use this trend to spot unexpected drops after a collector configuration change or large increases after onboarding a new data source.
4

Review source-specific details

Use the environment selector to focus on a specific data source, environment, or extension.Compare the displayed object types and counts with the data you expected the collector or payload to produce.
5

Review privileged collection coverage

For Active Directory privileged collection, review Local Group Completeness Over Time and Session Completeness Over Time.Use these charts to understand how much visibility BloodHound has into local group membership and session data across active computers.

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Data quality views

The available views depend on the data in your BloodHound database. The page can include aggregate object counts, source-specific breakdowns, environment-specific views, and completeness charts.

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Count behavior

Data quality counts show user-facing object data. They do not include internal labels or metadata objects that BloodHound uses to support platform features. For example, internal labels such as Base and AZBase can support calculations for Active Directory and Azure totals. They do not appear as separate object types in the breakdown because showing them separately can double count the same objects.

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Completeness charts

Completeness charts apply to Active Directory data collected with SharpHound privileged collection. They help you understand collection coverage for data types that depend on host availability and permissions. Completeness below 100% is common. Workstations and servers can be offline, unavailable, or inaccessible during a collection run. If completeness is lower than expected, review collector permissions, collector logs, and collection scheduling.

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Structured OpenGraph data

In BloodHound v9.5.1 and later, the Data Quality page includes structured OpenGraph object counts and historical trends. This enhancement helps you validate OpenGraph ingest without writing custom Cypher queries.
This feature is available under early access. Enable on the Administration > Early Access Features page to access it.
For structured OpenGraph data, BloodHound organizes counts by extension and registered source_kind. The source_kind identifies the OpenGraph data source and can come from payload metadata or from environments.source_kind in an extension definition schema. Select a specific extension or source kind to review object type counts and historical trends for that OpenGraph source.