What Is Metadata Management?

Enterprise data is quickly expanding. According to Statistica, between 2020 and 2022, the worldwide enterprise data volume—or data shared within organizations—surged from roughly 1 petabyte to 2.02 petabytes, indicating a 42.2 percent average annual growth [1]. The steady uptick in enterprise data prompts the question:

• How do organizations manage vast volumes of data?

• More importantly, how do firms identify unique data instances?

The answer lies in metadata and its management. By offering insights into the foundational data, metadata helps organizations better manage and source their internal data. Furthermore, it ensures widespread accessibility and availability of accurate metadata.

Read on to delve deeper into the intricacies of metadata management, including its use cases, benefits, challenges, and best practices.

What is metadata?

Metadata is data that conveys information about other data. Much like the information on a product’s packaging, metadata offers details on the data's creator, privacy classification, storage location, format, storage method, and more. The extensive information allows a swift understanding of individual data records’ origin and associated characteristics.

Read more: What Is Metadata?

Types of metadata

Several types of metadata exist based on context and function. Below is an overview of descriptive, structural, and administrative types of metadata.

1. Descriptive metadata

The descriptive metadata provides information about a data resource’s contents, such as:

• Title

• Document size

• Image

• Keywords

• Author name,

These are all pieces of information that help locate information.

Specifically, descriptive meta tags assist search engines in categorizing content and enhancing search operations within a website. For example, on e-commerce sites, you may search for product listings based on a specific manufacturer or under a particular category. The search results are supported by “manufacturer” and “category” descriptive metadata. 

2. Structural metadata

Page numbers, table of contents, and section numbers are all examples of structural metadata. Enforced using markup languages like XML, structural metadata enhances the overall presentation of data by defining the hierarchical relationships among various data resources, including, but not limited to, paragraphs and headers.

3. Administrative metadata

The administrative metadata outlines access specifications and restrictions associated with a file, covering aspects such as copyright, rights management, and license agreements. Using data responsibly and ethically is the overarching goal of administrative metadata. 

Why is metadata management important?

The proliferation of different data sources continues to increase organizational data complexity. Many firms are adopting metadata management to navigate this intricate data landscape as a guiding framework for sorting and structuring data effectively.  Metadata management also helps businesses establish a transparent and auditable record of their data assets, ensuring compliance with data protection laws and regulations.

Read more: What Is Data Enrichment?

How does metadata management work?

Metadata management is typically facilitated by tools that autonomously capture and store metadata from firms’ applications, data integration tools, and data warehouses, among other sources.

Active metadata management tools, enriched with artificial intelligence and machine learning, go a step further by automating the profiling and tagging of metadata. These tools also assist in identifying inaccurate or missing data. Popular active metadata management tools include Oracle Enterprise Metadata Management and SAP PowerDesigner.

Benefits of metadata management

You can generate metadata manually or automatically through tools. Regardless of your approach, below are some notable advantages to practicing metadata management:

1. Data uniformity

Through metadata management, data attributes of all kinds conform to an internally acknowledged and accepted framework, ensuring consistency across integrated data sources. Standardized data also reduces the likelihood of errors in data interpretation. 

2. Reduced storage costs

Redundant metadata can inadvertently make way as data sets evolve and undergo changes. Automated metadata management tools prevent the accumulation of duplicate metadata, saving you from unnecessary data storage expenditures. 

3. Enhanced productivity 

By simplifying data retrieval, metadata management cuts down the time needed to perform

the arduous tasks of searching for and preparing data. The impact of this improvement is particularly pronounced in projects that demand extensive research efforts. 

Practical applications of metadata management

Metadata management is used in several industries, from finance to retail and IT. Here are a couple of real-world use cases.

Verifying non-fungible digital assets 

An NFT, or non-fungible token, encapsulates metadata about a digital asset, validating the NFT's legitimacy. This may include asset name, description, year of creation, and more stored securely on a blockchain network. Essentially, the managed metadata linked with NFTs safeguards against counterfeiting and fraud. 

Optimizing business performance reporting

Metadata management helps unearth historical data usage, streamlining data analytics for reporting. Take, for instance, a retail company aiming to analyze its sales performance over the past year. The firm tracks and documents its previous sales, evaluates the effectiveness of various reports generated, and ensures that relevant metadata accompanies the sales data. The comprehensive approach supports effective business performance reporting.

Challenges in metadata management 

Although metadata management has its perks, make sure you weigh the following factors before adopting the practice: 

• Tricky to standardize: Establishing consistent standards and guidelines for metadata management can prove challenging for big corporations. The difficulty stems from the array of data sources and the unique needs of different business units within firms.

• Data quality issues: If the underlying data is suboptimal, its limitations inevitably affect the corresponding metadata. The interdependence underscores the need for effective data preparation, a task that may not always be feasible.

Metadata management vs. data catalog

Metadata management is an approach or methodology pertinent to data handling. A data catalog, on the other hand, is a software tool that supports metadata management. As repositories for metadata, data catalogs compile information on the processes, platforms, individuals, and data inventories tied to source data.  

Best practices in metadata management

When planning to add metadata management to your data-handling routine, the following tips can help you lay the foundation: 

1. Outline your metadata strategy. 

Begin by identifying immediate and long-term use cases for metadata management. This involves pinpointing specific instances or projects where metadata can enhance data understanding, accessibility, or governance. Furthermore, determine what types of data formats will add to your metadata strategy. Your metadata strategy should coherently extend your business goals, contributing directly to organizational success.

2. Concur on data terminologies. 

Collaboratively agree with stakeholders within your firm on the classification and organization of metadata. This will ease the formulation of metadata management policies. Fostering a shared understanding across various business units is key to enhancing metadata interoperability. 

3. Invest in the right tools.

Consider factors such as ease of search when picking tools to implement your metadata management strategy. If your data is complex, opt for active metadata management tools that come with automatic data tagging controls. 

Learn more with Coursera.

Skills in data analysis enhance your ability to assess metadata quality. The Introduction to Data Analytics course on Coursera can assist you in developing these skills. Offered by IBM, this beginner-level course can help you discern different types of data structures, file formats, and data sources.