Metadata Analytics from DataHub
Contemporary data stacks involve a myriad of specialized platforms and technologies, each with their differentiated use-cases and functions, making it cheaper and easier than ever to store, transform, and leverage data. The downside to this hyper-fragmented tooling is the complexity that arises when data practitioners attempt to govern data across this ever-growing set of resources:
- Who owns what?
- What does this data asset represent?
- How should this data be leveraged?
- How are core metrics and measures defined?
Data Landscape Summary
DataHub’s Analytics gives the organization a birds-eye view of the volume of data assets across Domain, Platform, and Terms, as well as a snapshot of metadata coverage by entity type. This is very useful to monitor and measure the impact of initiatives to increase data ownership, documentation, and more.
The Data Landscape Summary provides a quick breakdown of the number of Entities by Domain, making it easier than ever to track the progress of assigning Data Domains to you assets while rolling out Data Mesh practices.
Users can also view how data is organized and categorized throughout and organization’s data stack by viewing the breakdown of entities by Platform (i.e. Snowflake, Looker, dbt) or Glossary Term.
DataHub Usage Analytics
Another facet of DataHub’s Metadata Analytics is a summary view of how DataHub user are interacting with the tool. Easily understand how widely adopted DataHub is within your organization by looking at Weekly Active Users and Number of Searches performed over time.
Quickly gain insight into how DataHub users are interacting with the platform by seeing which functionality is most commonly adopted, and which actions users are taking. For example, we see in this graph that end-users are most commonly interacting with Dataset entities within DataHub, most commonly viewing the Schema, Documentation, and Lineage sections.
Looking at the actions taken by DataHub users, we can see some pretty interesting differences in activity based on Entity Type. For example, users are commonly updating Ownership, Description, and Terms for Datasets, but primarily interacting with External Links for Dashboards/Charts and Pipelines/Tasks.
Interested…? Understand Your Data Ecosystem using Analytics with DataHub!
DataHub’s mission is to empower how organizations understand and utilize their data through sophisticated metadata management. DataHub is building tools and features for governance, discovery, and observability for the modern data ecosystem. We’d love you to be a part of the DataHub Community! Come say hello in our Slack, check out our Github and view our latest Town Hall to learn about the latest in DataHub.