The most common definition of Metadata, “data about data,” is misleadingly simple. The kind of information that can be classified as Metadata is wide-ranging. Metadata includes information about technical and business processes, data rules and constraints, and logical and physical data structures. It describes the data itself (e.g., databases, data elements, data models), the concepts the data represents (e.g., business processes, application systems, software code, technology infrastructure), and the connections (relationships) between the data and concepts. Metadata helps an organization understand its data, its systems, and its workflows. It enables Data Quality assessment and is integral to the management of databases and other applications. It contributes to the ability to process, maintain, integrate, secure, audit, and govern other data.

To understand Metadata’s vital role in data management, imagine a large library, with hundreds of thousands of books and magazines, but no card catalog. Without a card catalog, readers might not even know how to start looking for a specific book or even a specific topic. The card catalog not only provides the necessary information (which books and materials the library owns and where they are shelved) it also enables patrons to find materials using different starting points (subject area, author, or title). Without the catalog, finding a specific book would be difficult if not impossible. An organization without Metadata is like a library without a card catalog.

Metadata is essential to data management as well as data usage (see multiple references to Metadata throughout the DAMA-DMBOK). All large organizations produce and use a lot of data. Across an organization, different individuals will have different levels of data knowledge, but no individual will know everything about the data. This information must be documented or the organization risks losing valuable knowledge about itself. Metadata provides the primary means of capturing and managing organizational knowledge about data. However, Metadata management is not only a knowledge management challenge; it is also a risk management necessity. Metadata is necessary to ensure an organization can identify private or sensitive data and that it can manage the data lifecycle for its own benefit and in order to meet compliance requirements and minimize risk exposure.

Without reliable Metadata, an organization does not know what data it has, what the data represents, where it originates, how it moves through systems, who has access to it, or what it means for the data to be of high quality. Without Metadata, an organization cannot manage its data as an asset. Indeed, without Metadata, an organization may not be able to manage its data at all. As technology has evolved, the speed at which data is generated has also increased. Technical Metadata has become integral to the way in which data is moved and integrated. ISO’s Metadata Registry Standard, ISO/IEC 11179, is intended to enable Metadata-driven exchange of data in a heterogeneous environment, based on exact definitions of data. Metadata present in XML and other formats enables use of the data. Other types of Metadata tagging allow data to be exchanged while retaining signifiers of ownership, security requirements, etc. (See Chapter 8.)

Like other data, Metadata requires management. As the capacity of organizations to collect and store data increases, the role of Metadata in data management grows in importance. To be data-driven, an organization must be Metadata-driven.