Simplifying Metadata Management: What You Need to Know & Why

Data is the backbone of every system and process in your organization – including those that are manual. Think about it: every decision being made in your organization, from budgeting to product expansion, is (ideally) data driven. However, 76% of businesses find it difficult to understand their data, according to a recent survey published in Forbes. To gain better insight into your data and use it more effectively, you must understand the value of metadata and how to use it.

Any time there is data, there is also someone creating, cataloging, accessing, and evaluating it. All of that information creates metadata, or context about data, which can create greater transparency and trust so that you can more clearly understand the data. But how do you start using metadata? The solution lies in metadata management – a powerful process that holds the key to streamlining data workflows, ensuring compliance, improving data quality, and enhancing decision-making. 

In this guide, we’ll delve into the world of metadata management, exploring its diverse benefits, the challenges it poses, and its real-world applications. We’ll also take a deep dive into Manta’s automated data lineage, the leading platform that helps unlock your metadata, and explore its distinctive features that set it apart from other lineage vendors.

What is Metadata Management?

To begin, let's gain a clear understanding of metadata. In simple terms, metadata refers to descriptive information about data. It comes in various forms, such as data schemas, definitions, lineage, dictionaries, and more. Metadata provides essential context and insights into data's structure, origin, and flow within an organization's systems. This is a key component of enterprise metadata management.

"The challenging aspect of defining metadata is that the same data can be recognized as either data or metadata, depending on the context. For example, data models are metadata for business users. For data modelers, on the other hand, data models can be considered data that will in turn require other metadata to describe data models. Different sources contain different approaches to classifying metadata."


Irina Steenbeek, Ph.D.
Author of “Data Lineage as an Enabler of Metadata Management”

Metadata is essentially data about data – it provides descriptive information that helps understand the characteristics, structure, and context of the underlying data. Think of it as a set of labels that give meaning and relevance to data assets. Without metadata, data becomes a sea of numbers and letters, lacking the crucial context necessary for interpreting and utilizing it effectively. In some cases, metadata shows who modified data, when, and within which system the modification occurred. 

For example, if you work in a healthcare organization, patient data is likely stored in an Electronic Health Record (EHR) system. That system not only tracks patient data, but who input the data, when, and if it was modified at any point (which is the metadata). Unlocking the metadata in healthcare through data lineage can improve patient care options and help you gain a firmer grasp on the way data moves through your organization. 

Gartner defines metadata management as “a set of capabilities that enables continuous access and processing of metadata that support ongoing analysis over a different spectrum of maturity, use cases, and vendor solutions.” 

Metadata management involves capturing, storing, organizing, and maintaining metadata to ensure its accuracy, consistency, and accessibility. An effective metadata management strategy empowers organizations to fully harness their data assets, promoting data quality, fostering collaboration, and facilitating data governance.


3 Types of Metadata

Metadata is almost everything. One obvious question is how to map ALL metadata, and whether there is even a strong business case to do so. But, before you map your metadata, you’ll need to understand what type of metadata you want to track. You can break metadata down into three categories: 


Operational Metadata
Technical metadata provides information on the  characteristics of data, including an inventory of objects as tables or files, data structure and location, etc. Operational metadata helps you understand how the data is being used and the overall data lifecycle, as well as who can access it, when and where it was created, and when it should be deleted for compliance.  Business metadata shows the business use of the data object, including reason for collection and storage, agreements, policies, regulations, governance, and consent as defined in a business glossary.


Metadata can be created manually or automatically, depending on the software where it is first recorded. For example, an EHR system automatically records operational metadata and technical metadata. Software like Salesforce, however, allows you to input your own custom metadata, which can provide deeper insights into each element.

Metadata management ensures that all necessary metadata is captured, stored, and made accessible to relevant stakeholders. This process is vital for establishing consistency in data usage, enabling data consumers to understand the context and limitations of the data they interact with.


Passive Metadata vs. Active Metadata

Both active and passive metadata add value to your data pipeline. But active metadata provides insights that passive metadata alone cannot.

Passive metadata contains basic information about data such as data profiles (business qualification, quality score, etc.) or data operational characteristics (who accesses the data, how often, popular data sets, etc.). It provides a generic overview of the data landscape, but it is static, can’t be acted upon, and won’t be of much help with providing complete visibility into complex data pipelines, unlike properly activated metadata. 

Active metadata can tell you the story behind the static profile of your data. It shows how and where the data flows in a data pipeline, including all changes, data transformations, and calculations. Knowing this, you can find any blind spots in the data landscape and fix them before they become a problem for your organization.

But what makes metadata “active”? Gartner’s Market Guide for Active Metadata Management explains that active metadata is:
  • Continuously collected and processed to distill information 
  • Used to derive intelligence and insights in the form of recommendations, warnings, and notifications 
  • Delivered to people when and where they need it (rather than those people needing to seek out metadata insights themselves)

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Our focus in this guide is active metadata, a concept whose definition is still evolving. To help understand it, let's compare and contrast the concept with how we use data. Metadata, after all, is also just "data". Metadata management is a technology market that has existed for decades, going through various phases. The most recent phase started with the rise of data catalogs. There are more than 30 different tools out there (probably even more), and new data catalogs are created almost every month. Yet Gartner, in their Market Guide for Active Metadata, stated that "[t]raditional metadata practices are insufficient.” So what is wrong with metadata and how can activation help?

The ultimate challenge is that we are focused too much on metadata collection, which has resulted in silos of metadata. As each catalog has its specific strengths, it is not uncommon to see multiple tools implemented by one company in different business units, which then leads to a catalog of catalogs. This is funny… and useless. Like with data, just collecting it adds no value to the organization.

Using the data analogy, we typically use data in the following ways.

  • We query data to get answers to our questions. That is the very basic use case we usually think of first. It can be good old pre-built reports, ad-hoc queries, or smart AI/ML algorithms digging insights from the data we have. By performing queries, we turn data into information and eventually knowledge. 
  • We embed data into places where people or machines naturally need them. We do not force a sales representative to log into our reporting platform and write ad-hoc SQL queries or use pre-built reports. Rather, we prepare all the data they may need about a prospect or a customer, turn it into information, and deliver it to their workspace (as a dashboard in an application like the CRM they use daily). On top of that, we also enrich internal data with valuable external data to provide an even more complex view of the customer.
  • We leverage data to automate tasks and processes. Instead of waiting for the sales representative to open their workspace and search for customers who may be a good fit for a new product offering, we have an algorithm running in the background that scores existing customers and sends proactive notifications that suggest who to call and what (or even how) to offer. Or, for an even simpler example, we automatically send a reminder to a sales representative in case they take no action (even if they should). 

Obviously, there are more ways that we interact with data (like in data governance in healthcare); the above are the most traditional examples. The first "search" case represents a very "static" experience. Everything is sitting in a silo (e.g., a data warehouse or data lake), and we expect people to come, find what they need, and ask questions they need to ask. Do not get me wrong – it is awesome for some use cases and, when compared to a case with no data available, a huge jump forward.

However, we see that data is put to much better use in the other two examples, actively supporting users with limited data engineering skills and dramatically increasing their productivity. Compared to the first example, the latter are more "active" and thus more useful and accessible to a broader audience. And that is what we want to achieve with active metadata too.

How To Activate Metadata?

That leads us back to the very first question: what is active metadata? Gartner’s definition in their most recent Market Guide for Active Metadata Management is a bit vague but touches on several key aspects.

  • Continuous access - metadata is continuously collected. It is not something you do once per month or once per year, as we want to collect every change and every signal and respond to it.
  • Connecting dots - metadata is not just collected; it is constantly processed to distill information (and knowledge) from all the signals and noise. And with the right feedback loop, your system gets smarter over time, collecting and learning.
  • Actionable - all the intelligence and insights derived from metadata are not locked into a silo, but rather delivered in the form of recommendations, warnings, and notifications to humans and systems/applications that may need it.
  • Embedded - actionable information / knowledge is integrated into processes humans and machines perform, embedded into their workspace. People are not forced to go in and look for the insights. Instead, active metadata comes to them – when and where they need it.

Why Organizations Need Metadata Management

There are several uses for metadata across your organization. These include using metadata for operationalizing data pipelines in DataOps and unlocking insights for improved business intelligence through metadata analysis by data lineage, among others. 

Use Case

How Metadata Helps

Business Intelligence (BI) In the realm of BI, metadata management plays a pivotal role in understanding the underlying data in reports and dashboards. Accurate metadata ensures that the right metrics and Key Performance Indicators (KPIs) are used, leading to more reliable insights and analyses.

Governance & Compliance Metadata shows auditors when information was last edited or accessed, who has access, and when it was created. 

Scalability & Growth Business leaders heavily rely on accurate and timely data to make critical decisions. Metadata management empowers them with the necessary context to trust data-driven insights and identify opportunities for growth and innovation.

Analytics & DataOps In the realm of analytics, metadata management helps data scientists and analysts understand the data they work with, leading to better models, predictions, and data-driven strategies.


In the case of activating your metadata through data lineage, there are a few use cases to explore: 


  1. Protect key business/regulatory metrics. Every company has a set of essential metrics they use to make decisions and manage their business. They are usually well-curated and carefully watched. Thanks to data lineage, we fully understand how each and every metric is calculated and where its data comes from. Activated data lineage evaluates every change in the environment to assess its impact on key metrics.

    For example, if there is a breaking change in an upstream data source or if a quality indicator for one of the sources dropped, warnings are immediately sent to notify those responsible for fixing the issue and those using those key metrics. This stops wrong business decisions from being made.
  2. Obtain contextual information when writing ETL code. We spend a lot of time moving data, transforming data, and running calculations and smart algorithms, just to get better insights. Data pipelines can be built partially in an automated way (this article is another great example of how to use metadata in an active way), but at least some parts are manually built by data engineers, or when using low-code/no-code platforms, by a variety of data users.

    When building a pipeline, you typically write SQL scripts and/or you drag-and-drop components in your ETL/ELT tool, link them together, and connect them to tables and columns. You have questions like – Where is this column sourced from? Is any PII data used to calculate it? What is the most recent data quality score of the associated data element? And many more. Now, imagine you have all that information as part of your workspace - all the critical context. You will certainly work much faster, and the same can be done for BI or AI/ML tools. 
  3. Prevent changes from breaking pipelines. Understanding the impact of changes is a very powerful capability of data lineage. We have used that power for decades as software engineers in our IDEs. Yet, in data, it was nearly impossible.

    In the ETL use case above, imagine that the developer implementing a change is warned by the ETL development studio that they have implemented a breaking change - a change that will break something downstream. In addition, we can also integrate data lineage into our CI/CD pipeline and make sure that when a developer tries to commit a piece of code, an automated impact analysis is triggered to determine if it is a breaking change and stop the commit or trigger a notification to the right people.
  4. Decommission unused objects. Our environment has many assets, such as tables and columns, reports, APIs, data exports, and more. But do we truly use all of them? If not, they only consume our expensive resources like space or money. They can even contain sensitive data! It is a very frequent issue, especially in the case of M&A projects or migrations. It is best to delete such assets. Unfortunately, measuring "usage" is quite difficult.

    For example, a column can be accessed by a human writing an SQL query or by a program reading something from it and processing it in some way. Activated data lineage constantly evaluates all data pipelines, and if a "lost object" is detected, the right people are notified.
  5. Clean and simplify our pipelines. Considering the assets decommissioning example above, it is still only a part of the problem. Because by deleting assets, the whole pipeline or its parts may become unnecessary.

    Think of complex SQL queries, dbt models, stored procedures, or ETL jobs, for example. How often do we actually clean and simplify them because some branches are no longer needed? Activated data lineage recommends which parts of the pipeline can be removed because they do not do anything truly useful.


Pros and Cons of Metadata Management

Using the examples above, it is clear there are benefits to unlocking and understanding metadata. However, it is just as important to acknowledge the challenges before beginning any metadata-related project so that you can address those issues early on. 




  • Unlocks valuable insights into your data.
  • Improved Data Quality and Accuracy: Accurate metadata instills confidence in data, ensuring its trustworthiness and reliability for decision-making purposes
  • Compliance Requirements: Many industries are bound by strict regulatory compliance rules. Effective metadata management ensures that data adheres to these requirements.
  • Speeds up audit processes by providing insight into technical levels of metadata.
  • Enhanced Data Integration and Interoperability: Metadata facilitates the seamless integration of data from various sources and systems, enabling smooth data exchange.
  • Better Decision-Making Processes: Well-managed metadata provides valuable context and insights, empowering stakeholders to make informed and data-driven decisions.
  • Can be tedious and time consuming if inspected manually.
  • Keeping the central repository synchronized with the most current information in the source systems requires significant resources.
  • Data Quality Issues: Inaccurate or incomplete metadata can lead to data inconsistencies and errors, impacting the reliability of data-driven decisions.
  • Lack of universal standards: There is no uniform method of metadata exchange and no universal API that vendors can use to embed metadata make it difficult to achieve full integration.  
  • Security Concerns: Inadequate metadata management may compromise data security, leading to unauthorized access and potential data breaches.


At Manta, we are pioneers in the metadata space. We integrated actionable metadata years before the term active metadata was coined, and we are big proponents of an open ecosystem with standards. We are part of OpenLineage and Egeria, but those efforts are still evolving. It means that metadata vendors must negotiate and implement point integrations with every single data solution out there, which will clearly never scale.

Manta’s Leading Approach to Metadata Management

Among the plethora of metadata management solutions available, the Manta platform stands out with its unique approach and innovative capabilities. Manta offers automated metadata management and discovery, lineage mapping, and impact analysis through both run time and design time lineage, revolutionizing how organizations handle metadata.

The Manta Difference


Active Metadata

Manta's platform adopts an active metadata approach, continuously capturing and updating metadata from various data sources in real-time. This dynamic metadata collection ensures that organizations have access to the most up-to-date and relevant information at all times, making their lineage map highly accurate.


Automated Discovery

Powered by advanced algorithms and data flow analysis, Manta's platform automatically discovers metadata across diverse data systems and applications. This saves valuable time and effort, enabling organizations to focus on leveraging insights from metadata rather than being burdened by manual management.


Lineage and Impact Analysis

Manta's platform meticulously maps data lineage, providing a clear understanding of data origins, transformations, and destinations. With the aid of impact analysis, run time lineage, and design time lineage, organizations can fully comprehend the potential consequences of data changes, thus minimizing risks associated with data manipulation.


Flexibility and Customization

Manta's platform boasts high levels of customization, allowing organizations to tailor metadata management to meet their specific needs and requirements. This unparalleled adaptability makes Manta a suitable solution for businesses of all sizes and industries, empowering them to optimize their metadata management processes.

Evaluating a Metadata Management Tool

Not all metadata management and data lineage tools are the same. When considering a metadata management tool, you’ll need to make sure that your tool has some, if not all, of the following capabilities: 

Take the Next Step with Manta

In conclusion, metadata management is an indispensable component of successful data management strategies. By fully embracing and harnessing the power of metadata, organizations can navigate the complexities of their data assets with confidence. Manta's leading approach to metadata management, featuring automated discovery, lineage mapping, and impact analysis, offers a remarkable advantage for organizations striving to achieve data-driven excellence.

Effective metadata management is not merely a necessity; it is a strategic advantage that propels organizations towards sustainable growth and success. Embrace the transformative potential of metadata management and pave the way for a future where data is not merely an asset, but a catalyst for innovation and informed decision-making.

As the world embraces an increasingly data-centric approach, organizations that master metadata management will be at the forefront of innovation, setting new standards for data integrity, security, and insights. The journey to data excellence starts with metadata management – unlocking the true potential of your data and transforming your organization into a data-driven powerhouse.

To learn more about how Manta can help, get a demo. 

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