Data Asset Analytics: Breaking Barriers to Maximizing Data Value with Data Cataloging
Earthrise, the dazzling Blue Marble photograph of Earth, has been a great inspiration for me. Apollo 8 astronauts on the first human-crewed mission to the moon took this awe-inspiring photograph. It is a powerful image representing the progress in travel exploration made possible through barrier-breaking inventions of the wheel to go farther on land, ships to sail across oceans, airplanes to fly in the air, and rocket engines to journey through space. We can trace similar progress in data management with innovations in punch cards, digital data storage, compute, network, analytics, and AI/ML to automate data processing, access information, enable data-driven decisions, and drive faster actions with greater precision.
I’m excited today to be part of a step forward in analytics and data cataloging: using enterprise metadata to automatically calculate enterprise data value. Organizations today consider their data to be an essential business asset. While it’s not Neil Armstrong stepping onto the moon—the ability to calculate data value is a major leap forward for enterprise data management. And it provides the foundation to value data assets and associated risks and liabilities just like you would any other enterprise asset on your balance sheet.
Data Value and Data Cataloging
I was thrilled with the ability to do data analytics using a spreadsheet on my first microcomputer. Today we have made significant advances with BI and advanced analytics, which can crunch a vast amount of data available in cloud data warehouses and data lakes. However, the most significant barrier in data analytics is finding the relevant datasets. Hence the need for an intelligent enterprise data catalog. An enterprise data catalog is a metadata warehouse, where information about data assets is stored, categorized, related, and enriched. The catalog enables search and discovery of data assets, not just for data analytics but any data initiatives, including data integration, data governance, data migration, and data protection.
Many organizations are modernizing their enterprise data platform to exploit the value of their data assets. An enterprise data catalog is a critical component for every enterprise data platform. With the help of an intelligent enterprise data catalog, I worked with customers who can now quickly inventory millions of data assets. However, the real challenge is understanding and maximizing the value of the data assets.
This challenge is mostly addressed with best practices in implementing the data catalog, which ensures alignment with your corporate vision and focuses on delivering business outcomes. With wide-ranging data asset types and diverse user groups using the data catalog for various use cases, customers need a way to easily measure, analyze, and communicate the value of the data assets to their business sponsors and stakeholders. While BI tools have enabled organizations to analyze data and make critical decisions, it is ironic that organizations often struggle to analyze their data assets and make strategic decisions. They need metrics on asset growth, user adoption rate, data asset usage, asset enrichment, user collaboration, and, most of all, data value.
Data Asset Analytics – New with Informatica Enterprise Data Catalog
If data is a valuable strategic asset, how can its value be estimated without insights into data usage? Imagine having a metadata analytics solution where you can automate critical metrics and spot trends, infer top influencers, catch misuse of privileges, identify areas that need attention, find answers to questions, and show data value in real time. This would be awesome!
All of this is now possible with the release of the industry’s first Data Asset Analytics solution as part of Enterprise Data Catalog version 10.4.1 (June 2020). Data Asset Analytics captures and organizes event and audit history information as metadata is being ingested, enriched, and accessed by users in the data catalog. As a result, you can easily measure, analyze, and maximize your data asset value. Data Asset Analytics supports:
- Storytelling for data assets with real-time metrics and trend charts
- Out-of-the-box analytic dashboards on
- User adoption
- Data asset enrichment
- Data asset inventory
- User collaboration
- Data value
- Extensive reporting on user logins, searches, asset changes, collaboration, and more
Data Asset Analytics makes data value analytics simple by automatically calculating enterprise data value. Based on the relative values you define for key metrics, Data Asset Analytics tracks metrics and computes data value in real time. The data value is the sum of data asset inventory, enrichment, usage, and collaboration values. The Data Value Dashboard reveals critical value metrics and trends as more data assets are ingested, enriched, used, and socialized. With clear visibility, organizations now have the levers to maximize the value of their enterprise data assets by identifying areas that need attention, such as user onboarding, asset curation, certification, asset reuse, and cultivating knowledge sharing across the business.
The recent successes of SpaceX has shown how broader commercial space flight participation is possible when the value is clearly demonstrated. With Data Asset Analytics, the value of data when clearly demonstrated can lead to higher data usage and unleash the potential of enterprise data assets. Data Asset Analytics is the secret to overcoming barriers to maximizing data value and opening doors to new possibilities for managing your data assets.
You can find more details in the Data Asset Analytics solution brief. And join us for the webinar, “Maximize Data Asset Value With the Industry’s First Data Asset Analytics Solution,” on July 28 (available on demand afterward).