This article was written by Melanie Henry and originally appeared on the Informatica Blog here: https://www.informatica.com/blogs/5-key-takeaways-from-back-to-basics-data-asset-analytics.html
We’ve almost reached the end of Back to Basics: Data Catalog webinar series! In our penultimate episode , Data Asset Analytics, our experts (along with another special guest) shared an extensive amount of helpful information related to tracking vital data catalog-related metrics.
If you missed this episode, you’ll definitely want to catch it on demand. (Don’t forget: you can catch our other previous episodes on demand, too.)
In addition to a wealth of knowledge about Data Asset Analytics, this episode included a detailed walkthrough of some of the metrics and dashboards tracked by organizations that have been successful in deploying and utilizing their data catalogs. Keep reading for some of the key takeaways from Episode 4.
1. Data Asset Analytics Enables You to Optimize the Value of Your Data
Data Asset Analytics is a solution which captures and organizes event and audit history information in an intelligent data catalog as metadata is being ingested, enriched and accessed. This allows users to easily view, measure, analyze and optimize the value of their data assets.
Users can view a variety of reports and dashboards, including asset count, assets with lineage, user collaboration activity, and much more.
Read more about some of the report and dashboards available in the Data Asset Analytics Solution Brief.
2. A Major Benefit of Data Asset Analytics Includes Accelerating Data Catalog Adoption
With Data Asset Analytics, users can view critical data catalog metrics such as the number of logins, active user growth, the number of user searches, and how many times top assets have been viewed. Additionally, Data Asset Analytics offers the ability to view metrics related to asset inventory, enrichment, and collaboration. Access to this data helps build an understanding of who is utilizing the data catalog, as well as how it’s being used.
Being armed with advanced details around catalog utilization empowers teams with the information they need to identify areas that may need attention and encourage further adoption across the organization.
Check out our step-by-step workbook, 7 Best Practices to Drive Data Catalog Adoption, for more guidance on increasing adoption.
3. Data Asset Analytics Helps Provide Answers to an Assortment of Valuable Questions
- Who has used a specific data asset and when did they use it?
- Which data catalog modules are used the most?
- What is the percentage of objects with assigned owners, descriptions, business terms, lineage, etc.?
- Which user groups or datasets need more attention? Why?
Above are just a few examples of the many questions that are asked by organizations wanting to optimize the value of their data. Data Asset Analytics provides organizations with the knowledge they need to answer these questions.
In addition to answering existing questions, utilizing Data Asset Analytics can help uncover previously unknown insights and inform which questions you should be asking to increase the value of your data assets.
4. Organizations Can Utilize Data Asset Analytics to Justify Data Catalog Investments
After investing in an intelligent data catalog, executive management or other stakeholders may request information regarding the catalog’s adoption and resulting benefits. Fortunately, Data Asset Analytics provides several metrics, reports, and dashboards that can provide leaders with information that can support justifying the investment in a data catalog.
Data Asset Analytics can assist with easily reporting on data catalog adoption, inventory, collaboration and data value over time, which in itself is invaluable for organizations.
5. Data Asset Analytics Can Help Support Data Governance Initiatives
Continuing with the previous point, easily being able to report on certain catalog metrics is greatly invaluable for organizations, especially for supporting data governance initiatives. A large part of any successful data governance program is collaboration.
Having metrics related to how often users are rating data assets, asking questions or replying to questions within the data catalog, for instance, helps organizations understand how users are collaborating, allowing them to prioritize assets or pinpoint potential areas for improvement.
Read about other essential data catalog capabilities that support successful data governance programs in our eBook: Data Cataloging for Data Governance: 5 Essential Capabilities.