Lost in the Library: Why Metadata Matters for Data Management

Imagine walking into a gigantic library that houses over one million books - biographies, fiction, adventure, gore, classic novels, encyclopedias, medical, scientific, and more. Yet, when you walk over to the card catalog, only 175,000 entries exist. It's like having a treasure trove of knowledge with no map to navigate it.

Let's pose some questions to illuminate how dire this situation would be:

1. How does one benefit from the information stored in the books that are not represented in the card catalog?

2. Where do I go to find books on my topic of interest?

3. What books in this library are too old and are ready to be transferred to another library?

4. What if there are rare, valuable books in the library that remain undiscovered due to lack of record keeping?

5. How can the library manage and maintain books that they don't even know exist?

6. What happens when a book is lost or stolen, but there's no record of it in the first place?

The bottom line is, you can only govern and manage what you have clear visibility to see. This simple analogy perfectly illustrates the criticality of having all your data assets represented in some kind of enterprise metadata registry.

In the context of data management, the "books" are your data assets - documents, email messages, big data, mainframe data, database, data in SaaS applications such as SAP, Teams, Jira, ServiceNow, Jira, Slack, data in your source code repositories like GitLib and GitHub, data in document management systems like documentum and docuSign, etc. The "card catalog" is your metadata registry. If you don't maintain a log of information "about" all your data, then you cannot hope to govern your data well at the enterprise level.

For example, if you have metadata describing all your documents in OneDrive, you can begin to apply data retention policies to that data for purposes of data minimization, ROT removal, and location-based data security. However, if you have zero visibility into the data you have stored on your local Windows file share, and elsewhere, then that data will remain largely ungoverned with respect to data lifecycle disciplines such as data privacy, data retention, data security, data remediation, data minimization, etc.

Customers need a data intelligence platform that not only maintains a statefull registry of metadata for data assets but one that is able to do that for ALL DATA across the enterprise, not just cloud-hosted data or just unstructured data, or just structured data. Solutions that limit or segment your visibility into your data are imposing silos for data lifecycle management. Would you consider buying 5 data retention solutions so that you could cover you data on the mainframe, on premises databases, cloud databases, cloud document stores? Of course not! This is what makes BigID's breadth of coverage one of the most important, if not the most important decision factor for our customers. Our breadth of coverage eliminates these IT solution silos and brings all your data under a single lens of data governance.

In conclusion, just as a library is only as good as its card catalog, an organization's data can only be effectively managed and governed if it's properly represented in a comprehensive metadata registry. Remember, you can't manage the lifecycle of anything that is virtually unknown. So, come and hear the BigID story today!

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Credit : The Dewey Decimal System, also known as the Dewey Decimal Classification (DDC), is a proprietary library classification system. It allows new books to be organized in libraries based on their subject matter. Developed by American librarian Melvil Dewey in 1876, the system divides all knowledge into ten main groups, each assigned a range of 100 numbers. Books are placed on library shelves according to these numbers, making it easier to find and return them to their proper location