The widgets on a dashboard present indicators that let you progress along the two inseparable axes of governance:
- Knowledge of data assets: what the organization holds and how well it is documented
- Data culture in the teams: how people work with the catalog day to day
These indicators fall into 4 categories.
What are legacy indicators?
This category mainly covers the enumeration of objects created in the platform in the 4 modules (Glossary, Dictionary, Uses, and Data Processing). It is the basis of all the other indicators, and so lets you understand the basic activity within the DataCatalog.
Examples of basic indicators:
- Enumeration of objects: enumerate the number of objects created through all the modules of the DataCatalog in order to carry out a basic inventory.
- Object History: track the evolution of the number of objects created across all modules in order to evaluate the progress of the DataCatalog enrichment.
- Attribute distribution: understand the development of the metamodel of the object records and be able to start measuring the enrichment of the object records compared to the basic model.
- Delays in statuses: understand the life cycle of objects and the time required to manage statuses.
What are activity monitoring indicators?
These indicators make it possible to trace who the key players working around the DataCatalog are, and to identify the users who connect the most and/or spend the most time on the platform.
They also make it possible to identify the time slots during which users tend to use DataGalaxy, to determine their licenses, and to count the different actions performed (CRUD).
Examples of activity monitoring indicators:
- Number of connections: understand who is connecting to the platform.
- Time spent: understand who the key players working around the DataCatalog are, and identify which users spend the most time and in which modules.
- Individual activity: understand the behavior of catalog users during their sessions.
What are enrichment indicators?
In order to understand the development and evolution of the metamodel, it is essential to first measure the enrichment of the object files (for example, the creation of custom attributes) in relation to the basic model. In a second step, we are interested in the quality of the attributes and their fillings.
Examples of enrichment indicators:
- Number of Attributes: understand the development of the metamodel of the object records and be able to start measuring the enrichment of the object records compared to the basic model.
- Fill rate: percentage of the number of attributes filled in on the number of attributes present in an object record. It lets you understand the completeness of all the attributes available in the DataCatalog.
- Completeness rate: percentage of the number of attributes filled in on the total number of attributes present in the form of an object whose recommended box is checked. The setting of recommended attributes is done by the customer administrators, following "Manage attributes" (not yet migrated to this Notion knowledge base).
What are crowdsourcing indicators?
This category is dedicated to monitoring collaboration between members of the same team or between different teams. We are therefore interested in analyzing user interactions on the same object, in studying the tasks assigned by a "user x" and performed by a "user y", and so on.
Examples of crowdsourcing indicators:
- Task count, comments, suggestions: count the number of tasks, comments, and suggestions created across all the modules of the DataCatalog in order to carry out a basic inventory.