Computer-aided software engineering (CASE) tools, introduced in the 1970s, were pivotal in providing metadata services for database and software application design, but integration among different tools remained a challenge due to vendor reluctance. The evolution of metadata management gained traction in the 1990s as businesses recognized the value of metadata repositories, leading to the development of standards and tools that expanded the scope of metadata to include both technical and business aspects
The Early History of Metadata
The concept of metadata has deep historical roots, stretching back to ancient times when librarians at the Great Library of Alexandria used tags to identify scrolls around 280 BC, which can be seen as an early form of descriptive metadata. The practice of cataloging evolved significantly over centuries, with the first library card catalog introduced in 1791 in France, laying the foundation for modern metadata systems. The term ‘metadata’ itself was coined by Philip Bagley in the late 1960s, marking a crucial point in how metadata describing and organizing information began to formalize in both library sciences and computer systems. The Library of Congress followed this development with its Cataloging in Publication (CIP) program initiated in 1971, which advanced the use of administrative metadata to systematically organize bibliographic data. By the early 1990s, the Information Interchange Model (IIM) was completed, establishing standards for how data elements and compound objects should be represented within file structures, helping to unify how digital data and database objects are managed across platforms.
Computer aided software engineering (CASE) tools, introduced in the 1970s, were among the first commercial tools to offer metadata services. CASE tools greatly aid the process of designing databases and software applications; they also store data about the data they manage (“metadata”).
It did not take long before users started asking their CASE tool vendors to build interfaces to link the metadata from various CASE tools together. These vendors were reluctant to build such interfaces because they believed that their own tool’s repository could provide all of the necessary functionality and, understandably, they did not want companies to be able to easily migrate from their tool to a competitor’s tool. Nevertheless, some interfaces were built, either using vendor tools or dedicated interface tools.
In 1987, the need for CASE tool integration triggered the Electronic Industries Alliance (EIA) to begin working on a CASE data interchange format (CDIF), which attempted to tackle the problem by defining meta models for specific CASE tool subject areas by means of an object-oriented entity relationship modeling technique. In many ways, the CDIF standards came too late for the CASE tool industry.
During the 1980s, several companies, including IBM, announced mainframe-based metadata repository tools. These efforts were the first metadata initiatives, but their scope was limited to technical metadata and almost completely ignored business metadata. Most of these early metadata repositories were just glamorized data dictionaries, intended, like the earlier data dictionaries, for use by DBAs and data modelers. In addition, the companies that created these repositories did little to educate their users about the benefits of these tools. As a result, few companies saw much value in these early repository applications.
It was not until the 1990s, as newer tools expanded the scope of metadata addressed to include business metadata, when some business managers finally began to recognize the value of metadata repositories. Some of the potential benefits of business metadata identified in the industry during this period included:
- Provide the semantic layer between a company’s systems (operational and business intelligence) and their business users
- Reduce training costs
- Make strategic information (e.g. data warehousing, CRM, SCM, etc.) much more valuable as it aids analysts in making more profitable decisions
- Create actionable information
- Limit incorrect decisions
The metadata repositories of the 1990s operated in a client-server environment rather than on the traditional mainframe platform that had previously been the norm. The introduction of decision support tools requiring access to metadata reawakened the slumbering repository market. Vendors such as Rochade, RELTECH Group, and BrownStone Solutions were quick to jump into the fray with new exciting repository products. Many older, established computing companies recognized the market potential and attempted, sometimes successfully, to buy their way in by acquiring these pioneer repository vendors. For example, Platinum Technologies purchased RELTECH, BrownStone, and LogicWorks, and was then swallowed by Computer Associates in 1999.
With the growing focus around the World Wide Web, data warehousing, and the pending year 2000 (Y2K) deadline looming, the mid to late 1990s saw metadata becoming more relevant to corporations that were struggling to understand their information resources. Efforts began to try to standardize metadata definition and exchange between applications in the enterprise. Examples include the CASE Definition Interchange Facility (CDIF) developed by the Electronics Industries Alliance (EIA) in 1995 and the Dublin Core Metadata Elements developed by the Dublin Core Metadata Initiative (DCMI) in 1995 in Dublin, Ohio.
The first parts of ISO 11179 standard for Specification and Standardization of Data Elements were published in 1994 through 1999. Microsoft and Oracle battled over development of a metadata standard throughout this period. The Object Management Group (OMG), supported by Oracle, developed the Common Warehouse Metadata Model (CWM) in 1998. While rival Microsoft supported the Metadata Coalition’s (MDC) Open Information Model in 1995, by 2000, the two standards were merged into CWM. Many of the metadata repositories adopted the CWM standard.
In the early years of the 21st century, existing metadata repositories were updated for deployment on the web, plus some level of support for CWM was introduced in the products. During this period, many data integration vendors began focusing on metadata as an additional product offering. Many of the products that existed in the early 2000’s became part of larger organizations’ suites (e.g., Superglue is part of Informatica, Ascential is part of IBM, etc.).
However, relatively few companies actually purchased or developed metadata repositories, let alone achieved the ideal of implementing an effective enterprise wide Managed Metadata Environment as defined in “Universal Metadata Models”. There are a number of reasons for this, including:
- The scarcity of people with real world metadata skills
- The difficulty of the metadata development and integration effort
- The less-than-stellar success of some of the initial efforts at some companies that did not plan with enterprise data management best practices (no enterprise data governance, no enterprise data architecture, etc.)
- Relative stagnation of the tool market after the initial burst of interest in the late 90s
- The still less-than-universal understanding of the business benefits of managing metadata
- The too-heavy emphasis many in the industry placed on legacy applications and technical metadata at the expense of business metadata and business usage
Due to the attention given to analytics and the need for understanding the data used in decision-making, companies are beginning to focus more on the need for and importance of metadata. Emphasis is also expanding on how to incorporate metadata beyond the traditional structured sources and include unstructured sources.
Some of the factors driving this renewed interest in metadata management are:
- Entry into the market of a variety of new and established software vendors, some concentrating on metadata management, others adding metadata to their existing suites
- The challenges that many companies are facing in trying to address regulatory and privacy requirements with unsophisticated tools
- The emergence of enterprise wide initiatives like information governance, compliance, enterprise architecture and automated software reuse
- Improvements to the exiting metadata standards
- A recognition at the highest levels of the organization by some of the most sophisticated companies and organizations that information is an asset (for some companies the most critical asset), and must be actively and effectively managed
The history of metadata management and managed metadata environments continues to expand with each adoption of metadata standards by an organization, the recognition of the value of metadata to managing the information assets of the organization, and the need to optimize that value systematically and at an enterprise level.
As metadata continues to evolve, its critical role in modern data management is becoming increasingly clear. Metadata not only facilitates document creation, modification, and tracking but also ensures long-term accessibility and integrity of digital assets. Modern metadata systems use standardized languages like XML (Extensible Markup Language) and RDF (Resource Description Framework) to enhance interoperability between systems, allowing both humans and computers to understand and manage data more effectively. Additionally, embedded metadata within files like digital audio files or Exchangeable Image File Format (EXIF) for images helps in categorizing and retrieving content across different platforms. Furthermore, metadata plays a vital role in rights management by specifying access permissions to sensitive data, which is crucial in today’s privacy-conscious environment.
Key types of metadata, including administrative, descriptive, and structural metadata, form the backbone of systems that manage physical databases and digital content like web pages and electronic resources.
The future of metadata management will likely involve more sophisticated systems capable of handling complex data ecosystems while ensuring security and compliance with emerging standards.
Metadata plays a critical role in data management practices by providing essential information about data elements and ensuring efficient data discovery and access. There are several types of metadata that support various aspects of data governance programs:
- Descriptive Metadata: Provides key identifiers such as title, subject, and author, as well as other bibliographic information that helps users understand the data source and its content.
- Structural Metadata: Defines the file structure and physical characteristics of digital files, guiding how compound objects like digital images or documents are organized. It ensures that elements are ordered to form chapters or parts within the content management system.
- Administrative Metadata: Manages process metadata, offering details on the data’s origin, creator, and rights management metadata, including licensing and copyright information. This type of metadata also includes preservation metadata, which supports long-term accessibility and authenticity of digital resources.
- Preservation Metadata: Ensures the preservation of data by recording how digital files should be maintained over time, safeguarding resources for future use.
- Usage Metadata: Tracks the usage of datasets, capturing metrics such as the number of views or edits, helping organizations understand how data is utilized within a data warehouse or DW/BI system.
- Quality Metadata: Evaluates data on relevant criteria like accuracy, completeness, and reliability, playing a crucial role in maintaining the quality of data.
- Statistical Metadata: Provides insights into how statistical data is collected, stored, and processed. It also includes statistical metadata for methods and data accuracy, especially in automated information processing systems.
- Reference Metadata: Offers deeper understanding of data elements, clarifying the semantics and relationships between different datasets. This type of metadata also helps users gain insights into collaboration metadata, reflecting interactions and contributions around data.
- Legal Metadata: Manages legal information like licensing and usage rights, crucial for maintaining rights management over digital resources.
By incorporating these different types of metadata, data governance frameworks ensure a comprehensive approach to managing and preserving critical data, supporting both operational and legal needs while enhancing the accessibility and usability of data across the organization.
As metadata has evolved over time, its role has expanded beyond traditional cataloging systems and database management to become a crucial component of modern digital ecosystems. From managing statistical data to preserving digital resources like museum collections, metadata provides technical information that enhances both the storage and retrieval of digital assets.
In digital marketing and search engine optimization (SEO), metadata plays a pivotal role in enhancing content discoverability and driving website traffic. Web developers utilize HTML tags to embed metadata directly into web page code, providing search engines with key details such as descriptive metadata, keywords, and page descriptions.
Optimizing these metadata attributes, including meta tags and searchable keywords, can significantly boost a site’s visibility, ensuring it ranks higher in search results. This increased brand visibility is crucial for modern marketing strategies, as accurate, well-organized metadata helps search engines understand and prioritize content. By leveraging tools such as the Extensible Metadata Platform (XMP) and Resource Description Framework (RDF), businesses can improve how their digital identification and content are indexed, resulting in better online discoverability.
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