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8 Amazing Benefits of Metadata Management (+ Best Practices Included)

The digital-first business climate brings with it more data sources than ever before. While the astronomical increase of accessible data is a welcome one, it presents new data management challenges and implications. This is because the more data we accrue, the harder the data is to collect, standardize, interpet, and trust.

Metadata management plays a critical role in maximizing the value of data. It ensures data consistency, governance, and compliance while providing the contextual information needed to make educated business decisions.


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This article is going to explore the benefits of metadata management and some key best practices. But first, what exactly is metadata?

What is Metadata?

We can understand metadata by thinking of it as data that describes other data. If an Excel file is your primary asset, then any information about that asset - for example, its size, content tags, location, or user restrictions - is referred to as the asset's metadata.

We interact with metadata every single day. Think of the nutritional information on the back of a cereal box, or the page in your favorite novel that details the publication date, ISBN, copyright info, etc. This data is there to provide us with additional, contextual information about the product itself.

In fact, ‘meta' is a prefix that means "an underlying definition or description." Metadata is simply that, a summary of the data we've collected that makes organizing and interpreting the data much more efficient.

What is Metadata Management?

Metadata management, often referred to as enterprise metadata management (EMM) is the practice of organizing, classifying, and standardizing data across your entire business ecosystem.

Think, for example, of your machine learning pipeline. The only way to fully codify, automate, and deploy a successful machine learning model is to have end-to-end visibility of your raw data inputs, outputs, and data predictions. This data needs to be easily-accessible across your business and compatible with other systems.


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One of the most crucial ML pipeline best practices is to implement pipeline-related metadata management. This enables you to meticulously organize all of this data to improve its quality, interoperability, and reusability.

Types of Metadata

Metadata can be categorized into various types depending on its purpose. The three main types of metadata are administrative, structural, and descriptive.


This type of metadata presents the technical and administrative information about an asset. It's one of the most critical types of metadata to possess because of its value to data governance, compliance, and data management procedures.

Examples of administrative data include file size and location, access permissions, archiving requirements, and creation dates.


Structural metadata describes how an asset is organized and formatted. Beyond giving us contextual information about the asset's internal structure, structural metadata can be used to determine the interoperability of data and its relationship with other assets.

Examples of structural metadata include a table of contents, page numbers, indexes, and chapters.


Descriptive metadata is the most robust of all metadata types. It defines the descriptive information of an asset so that it is easier to discover and identify. Descriptive metadata is essential for cataloging assets like books and documents.

Examples of metadata for a book would include its title, author, genre, publication date, and keywords.


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Other types of metadata include:

  • Process metadata: information relating to the procedures used to manage statistical data, e.g. audit results and error logs.
  • Preservation metadata: concerned with information that enables long-term maintenance and preservation of an asset, e.g. rights management.
  • Provenance metadata : the information relating to the movement and modifications of an asset, e.g. duplications, change logs, ownerships
  • Usage metadata: defines how a data has been used, e.g. access patterns and comments

8 Benefits of Metadata Management

Let's discuss some of the benefits of metadata management and why it's become essential in the data-driven business climate.

1.    Enhanced Data Quality and Searchability

Not only do metadata management solutions provide detailed metadata and data lineage, but they leverage automation to classify, standardize, and corroborate data. This means that all data inconsistencies and issues are rectified in real-time, decreasing the potential of human error and improving the quality of data.

An added benefit of real-time automation is that employees can receive the most up-to-date data, thus improving data quality and the success of using said data to make pivotal, on-trend business decisions.

2.    Quicker Access to Insights

Just as autoML tools allow you to automate the application of machine learning to real-world problems, metadata management platforms accelerate the generation of actionable insights by providing you with an automatically structured and centralized storage system.

Businesses often have to navigate various complex systems just to collect and unify data. In fact, a huge 60% of data scientists' time is spent on data cleaning and organization.


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Metadata management solutions allow data handlers to spend less time cleansing, collecting, and organizing data, and more time applying it to real-world scenarios.

3.    Improved Data Consistency

In a managed metadata environment (MME), all data is standardized to eliminate the potential of error caused by conflicting terms. Formats, languages, and other attributes are labeled and transformed according to an internally compatible schema, eliminating data inconsistencies across integrated data sources.

Better data consistency makes data easier to retrieve and navigate, improving the time it takes to apply data to projects.

4.    Reduced Costs

Automated and repeatable metadata management solutions don't just save you time - they also save you money. As a data trend that promotes sustainability, metadata management solutions lower storage costs and facilitate better resource cost estimations, effectively reducing redundancy and excess expenditures.

5.    Optimized Marketing Strategies

Marketers are cleverly using metadata to monitor content usage. By tracking customers across data sources, they can gather metadata that gives context to these interactions, in turn enhancing their on-page marketing strategies and increasing sales. A common example of this is the ‘people who bought this item also bought' section that you see on plenty of ecommerce websites.

Metadata management can also enhance customer retention strategies.  Say, for example, you're using software to start referral program marketing. While you could simply approach each customer with the same referral template, you'll have much more success by personalizing your campaigns.

Most referral program software allows you to add custom attributes to your campaigns. Along with basic information like name, gender, and age, you might also collect keywords, buying frequency, acquisition channel, payment method, and more.

6.    Better Data Governance, Compliance and Security

Poorly organized data is vulnerable to compliance issues, cyberattacks, and data breaches, all of which can have serious financial, reputational, and even legal repercussions. According to IBM research, the average cost of a data breach exceeds $3m.


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Metadata management solutions automatically identify, catalog, define, and standardize sensitive data like personally identifiable information or financial details. This sensitive data is flagged, making it easier to secure and govern in compliance with regulations like GDPR, HIPAA, and CCPA.

Armed with automatic sensitive data classification, businesses can exert more control over sensitive data by creating security filters, user restrictions, and other measures. It also increases internal visibility of how sensitive data is being used across workflows.

7.    Improved Talent, Productivity, and Employee Satisfaction.

Metadata assists in our ability to source high-quality talent, achieve ultimate software diversity and inclusion, optimize internal workflows, and foster positive working environments. Employees that are more satisfied are more engaged and productive, resulting in the increased generation of innovative, actionable ideas and implementations.

8.    Drives Digital Transformation

Digital transformation is well and truly upon us, with 89% of companies adopting or planning to adopt a digital-first business strategy.


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Whether it's investing in a financial systems modernization solution to facilitate digital banking or simply creating a smarter mobile app experience, businesses are utilizing the likes of artificial intelligence, advanced analytics, and Internet of Things (IoT) to lead their digital transformation endeavors.

What does this have to do with metadata management? Well, to execute enterprise-level digital transformation projects, you need data - and lots of it.

A huge benefit of metadata management is that it provides you with a full-scale view of your data landscape. Automation enriches your digital operations by enabling the speedy retrieval of high-quality data, in turn speeding up data analysis and implementation processes.

Metadata management also drives digital transformation and innovation by evidencing how customers interact with your digital experiences. It assists the optimization and personalization of your customer journey, helping to build digital ecosystems that align with customer preferences.

Key Best Practices For Metadata Management

Now you know the benefits of metadata management, it's time to explore how to actually implement it. The planning and execution of metadata management varies across businesses and industries, but there are some key best practices that you should follow to promote sustainability.

Define Your Metadata Strategy

The long-term value of your metadata depends upon a clearly defined and unified metadata strategy. Your data management goals should align with your business' needs and objectives, and should also identify the foundational talent, tools, and processes that you need for successful execution.


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Metadata strategies provide a clear direction for your initiatives. Your answers to the questions below should help you form a foundational metadata strategy that you can build upon as you analyze the intricacies of your unique business goals.

  • What types of metadata do you require?
  • What types of metadata might you require in the future?
  • What current or future issues will be solved by investing in metadata management?
  • Where is the metadata located?
  • What obstacles might you arrive at when it comes to obtaining metadata?
  • What are your current and prospective use cases for metadata?

Identifying the use cases for metadata and which of them you should prioritize is of critical importance as it guides the overall trajectory of your initiative. While it's a best practice to leverage all types of metadata, consider whether you'll prioritize improving data quality, data orchestration, governance, or something else.

From there, you can specify exactly how you'll utilize metadata across integrations, departments, publications, and more.

Leverage The Right Tools

With so many metadata management tools to choose from, how do you know which ones are right for you?

The best metadata management tools combine machine learning and AI with data analytics integrations to provide you with intricate data context. Not only should the tool provide you with the different types of metadata (structural, administrative, descriptive, etc), but it should increase discoverability across all of your data sources, from your data lakes to your flat files.

Speaking of data lakes, ensure that you have an advanced data lake storage solution like Azure data lake. What is Azure data lake? It's a cloud-native platform that supplies a centralized repository combined with augmented security facilities, data authentications, and the ability to handle large-scale queries.


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Consider the integrability of your tools and data sources too, along with how efficient they are at capturing data. For example, docusign enterprise pricing solutions are more likely to have better integration and metadata extraction capabilities than free plans.

Adopt Metadata Standards

Metadata standards establish data uniformity, standardizing your vocabularies, formats, schemas, etc. to provide interoperability between your systems. Without a metadata standard, you risk system incompatibility and decrease your data's accessibility which, in turn, decreases data value and productivity.

The metadata standard you choose may depend on your specific industry or how straightforward/complex your goals are. The most common metadata standard for general use is the Dublin Core Metadata Element Set. Other industry-specific metadata standards include the Text Encoding Initiative Guidelines (TEI) and the ISO 19115 standard.


Metadata might exist behind-the-scenes, but it plays a vital role in how successfully we extract value from data and apply this value to real-world issues.

Metadata management supports businesses as they strive to continuously meet changing customer demands. For businesses who pride themselves on being the customer orientation definition, metadata management enables them to orient their products, marketing messages, and customer service strategies to align with what the customer wants.

Implementing a metadata management strategy cultivates a data-driven culture and drives collaboration, innovation, and the generation of increasingly intelligent business decisions.



Pohan Lin - Senior Web Marketing and Localizations Manager

Pohan Lin 

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in dynamic gaming solutions, web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as SME-News. Here is Pohan's LinkedIn.

Published Wednesday, June 22, 2022 7:30 AM by David Marshall
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