Metadata Management will grow into 2021 and beyond. According to a DATAVERSITY® Trends in Data Management Report, 84 percent of business respondents had a Metadata Management initiative in place or had plans for one.
MarketWatch, a consulting firm, expects massive growth by 2026. How much success a company will have with Metadata Management will depend on implementing a useful Metadata Management framework.
Getting a handle on metadata makes sense for companies in complying with data regulations, improving data quality, exploring machine learning, and using data better. But Metadata Management goes beyond the technical, to the people and policies that support it.
Reid Hoffman, the co-founder and executive chairman of LinkedIn, says “data only exists within the framework of a vision you are building to, a hypothesis of where you’re moving to.” Getting to such fit-for-purpose data requires a Metadata Management framework that supports the business’s vision and activities.
What Is a Metadata Management Framework?
A Metadata Management framework (MMF) describes an organizational approach toward making data and metadata assets more accessible and usable to achieve business objectives. It mobilizes and expands existing resources that underly Metadata Management, in line with organizational needs.
A Metadata Management framework happens at the corporate infrastructure level and operational levels. At a higher level, an MMF, as Ian Rowlands, from the DAMA Chicago chapter, explains, requires executive support and a Data Strategy to enact a higher business vision. Also, the MMF instructs people and automated algorithms on capturing, integrating, managing, and publishing metadata at a more day-to-day level.
Given both strategic and tactical perspectives, the MMF must cover anyone interacting within or with an organization. As such, the MMF needs to support and be supported by a Data Governance program.
Data Governance informs the MMF about metadata’s availability, organizational priorities with metadata, and when and how to deliver metadata. In return, the MMF tells enterprise Data Governance about data compliance, data accessibility, and data quality. Think of the Metadata Management and Data Governance underpinnings as working and delivering data understanding in tandem.
Factors to Consider Before Choosing a Metadata Management Framework
Before creating any Metadata Management framework, consider the following:
- Get an MMF Baseline: Even if no formal MMF exists in an organization, an implicit one does. Technical documents mapping data architecture, the knowledgeable business analyst who others turn to understand reporting data, and data-entry procedures provide context around an organization’s data and pieces of its MMF.
Getting a baseline about what people, processes, and technology already exist and how they inform the organization’s Metadata Management framework just makes sense. Using a “qualified and knowledgeable data professional (and other skilled talents) to administer and interpret data readiness assessments” along with Data Maturity models like those put forth by Gartner, or the Capability Maturity Model of Integration (CMMI), gives a good MMF starting place. From there, a company can get concrete suggestions to improve its MMF.
- Be Clear About What an MMF Will Achieve: Be clear why an organization needs to manage metadata and implement a Metadata Management framework. Metadata Management helps reduce training costs, provides better data usage across data systems, and simplifies communication.But it does not solve all data problems.
If an organization prioritizes customer experience or developing products and services over that of data sharing and data discovery, then a MMF may not be the best solution compared to another option. Given that any useful Data Management framework tends to have a costly setup, an organization needs to critically look at why to implement a Metadata Management framework in the first place.
- Improve Collective Data Literacy: Working with metadata requires synchronized collaborations and repeatable actions among organization definers, producers, and users. Recognize that these kinds of people exist in different organizational departments and have a range of cross-functional skills. Some have very technical and analytical skills, and others have softer skills.
As a result, different company subgroups probably will think of and apply any MMF from a very different perspective. One person’s definition may not be the same as another’s.
Collective Data Literacy helps stakeholders make sense of an MMF by understanding how to interpret, communicate, and use metadata to grasp corporate data assets better. Plan on thoroughly understanding business needs with metadata, instructing people why and how to use any metadata to do their jobs or achieve their goals.
- Leverage the MMF Pieces Already in Place: Before spending money on tools and resources toward a new Metadata Management framework, use the MMF that already exists in an organization for a quick win and to show its value. Get the person who knows how to interpret what a customer report means to document the knowledge. Update the existing technical architecture diagrams to include more robust metadata, such as that in a data dictionary.
Also, consider existing standards and practices toward constructing or improving an MMF. For example, the International Statistical Classification of Diseases, endorsed by the World Health Organization (WHO), defines diseases and health problems for clinicians, researchers, and medical billers. If relevant to the business, take advantage of existing authoritative metadata sources.
Metadata Management Frameworks Use Cases
Useful Metadata Management frameworks address the organization’s vision, its Data Strategy, and available resources. Below, find MMF implementations that take all these into account.
- Department of the Interior (DOI) Metadata Implementation Guide: The Department of the Interior received a mandate to make its information more transparent and sharable among its 90,000 employees. The DOI had to consider the business, operational, and technical metadata in this initiative and various data cultures across ten bureaus and eight agency offices.
The DOI took a multistep approach in implementing and structuring its MMF. From a technical perspective, the DOI focused on using a metadata catalog with metadata tags. But not every DOI bureau and office immediately jumped into this new system.
Instead, the DOI took a “phased approach, designed to work with different data cultures.” The MMF rolled out in three phases: “getting started,” “implementation and management metadata,” and “improving Metadata Management.”
Each stage indicated how a DOI office or bureau would coordinate people, processes, and funding. The DOI also created a Data Governance committee across the different bureaus to guide and collaborate about Metadata Management practices.
- Enterprise Data Management Framework: Canberra in Australia wanted to empower Transport Canberra and City Services to be data-driven, improving data usability, trust, and compliance. The Enterprise Data Management (EDM) Framework provided data management practices, including Metadata Management practices and a rough MMF, to achieve these objectives.
Canberra recognized people as central to implementing the EDM and worked out ways to grow their data literacy. In addition to storing metadata in a machine-readable format, located in a centralized repository, Canberra supported the culture through initial implementation and ongoing maintenance.
Canberra developed a business glossary and data owner, steward, and custodian roles—the foundations needed for Metadata Management to get started. During the ongoing maintenance phase, the city would update its business glossary and metadata roles.
Canberra established its Data Governance program with its Metadata Management as a cornerstone. As part of its more detailed EDM Program Plan and first milestone, Canberra established a more formal Metadata Management Framework.
- OpenPDS/Safe Answers: In 2014, MIT responded to the behind-the-scenes collection of smartphone personal metadata by tech companies and government agencies. MIT created OpenPDS (Personal Data with Privacy) to give individuals the tools to collect, store, and manage their own metadata.
Safe-Answers describes the data architecture containing the personal data store (with the sensitive private data) and technologies to anonymize unique metadata into data summaries with just code for the third-party collecting information.
OpenPDS lets individuals decide what, where, and how personal Metadata should be stored. To test openPDS, the researchers constructed two field studies to get data about performance and user experience.
From these results, the openPDS framework advised organizations on “enhancing privacy and security of personal metadata.” While OpenPDS did acknowledge challenges around malicious attacks, it provided a direction for organizational respect of an individual’s privacy. With the passing of governmental data privacy regulations, like the GDPR, the OpenPDS MMF offered a way to comply with this legislation.
The MMF consists of more than a technical solution. It describes an approach and vision toward managing metadata, mobilizing people, technology, and practices. Also, Metadata Management frameworks closely intertwine with Data Governance. Doing good Metadata Management requires setting up a firm MMF foundation throughout the organization.
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