Metadata: A Framework

Introduction

Metadata can give you superpowers.. if you leverage it correctly. For this, you need to have a rock-solid metadata framework in place. It’s a complex endeavor: a good framework results from mixing the right ingredients in the right proportions.

Metadata is a scary word, yet it just refers to the best way to describe any content. Metadata is not a new term. It’s been around for ages - it's an essential part of any data management system. We define it simply as the data describing other data. It can be used to describe the content, format, structure, and meaning of data. Any time you organize or catalog your digital assets—for example, emails in your inbox; photos on Facebook; music files on iTunes; graphs in Excel—you're actually creating metadata! If someone asked you what was happening in those emails or photos or files or graphs, they'd probably ask you questions like: "Who sent them?" or "When were they created?" And if someone wanted to find something specific within one of these collections—a particular photo from last summer's vacation in Paris—metadata would be the element that will help him do that too!

In this article, we provide a metadata framework for thinking about your own management strategy for metadata in terms of three essential elements: people, processes, and tools. We also provide tips on how each element can be implemented effectively so that you can start taking advantage of the many benefits that come with having effective information management systems in place in your organization. To support your framework we recommend understanding the different types of metadata and their applications, but that's for another day and another blog.

Why is metadata management important?

Metadata management is important because it helps you learn what data you possess, where your data is, and how it’s organized. It also ensures that only the right people have access to the information they need, and equips you to develop a robust metadata framework for your digital transformation.

The advantages of metadata management include:

• A better understanding of your data assets (both internal and external).
• Greater ability to manage risks associated with human error or intentional tampering with data.
• Reduced likelihood of compliance breaches due to incorrect classification or inappropriate sharing.
• Accessibility of data to users who need it, when they need it.

A three-pillar metadata framework: People, Processes, and Tools

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The three following pillars are worth considering when defining your metadata management strategy:

• How you choose the people involved in metadata management: These are the actors in your organization who are involved in creating a metadata framework to manage, maintain, and update your metadata. This includes the data stewards, content creators, and curators.

• How you obtain and manage the catalog: This is the actual software used to house all of your data and make it accessible through an API or web interface. We’ve made a benchmark of all such tools if you happen to be in the process of choosing a data catalog.

• How you use the metadata: This is where things get interesting— here, we start diving into how different organizations use their data internally to drive decision-making around content creation, curation, distribution, etc.

The way you use your metadata will depend largely on your organization and the work that you do with it. If you’re a news publication, for example, then chances are high that you have a brigade of content creators who are working on stories all day long—so your catalog is likely to be smaller and easier to manage. If you’re an enterprise software company that has hundreds or thousands of employees creating a plethora of content on a regular basis, then you need a comprehensive metadata management framework and management system to accommodate this scale.

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1 - People

The people who are involved in your metadata management start with your data steward, and anyone else that is involved in managing data. This is the data team at large.

The best part about your data stewards is that it should have nothing to do with the day-to-day work that needs to be done in order to keep your company running smoothly. Data stewards will be just as responsible for ensuring that all of your records are properly organized as they would be if they were working with archival documents or historical artifacts in an archive—the difference being that this task involves digital information rather than physical ones.

When you reach level 2 of the metadata management game, you usually work without a data steward. How is that possible? It depends on your choice of tooling. If you have chosen an intelligent and automated data catalog like Castor, the catalog actually takes care of stewardship. It allows you to assign table owners, and identifies the most popular tables in your organization. The tool then nudges the owners to document the most popular tables, used by everyone else in the companies. In this case, the people taking care of the documentation are the dataset owners, who are regular members of the data team. An intelligent tool fosters collaboration and prioritizes documentation in such a way that you do not need a data steward anymore.

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2- Tools

Obtaining and managing your metadata can be done through a data catalog. A data catalog is an indexing tool that allows users to search for and manage data sets. It's usually used by companies that need to store and organize their data in a way that makes sense for them.

Build out a catalog that has both business and technical terms

It's important to remember that not everyone in your organization will know what metadata is, and even those who do will have different understandings of it. So you'll want to make sure that the catalog is easy for both types of people to understand.

To do this, make sure your catalog has clear definitions for business terms (like "metadata" itself) and technical terms (like "RDF"). You should also make sure it's easy for anyone in your organization to find—not just those who are experts on the subject matter.

Another way of ensuring consistency is by making sure that every time someone uses a term like “description” or “subject” they mean exactly what they say they mean when writing their descriptions and subjects.

Choose the right tool

Choosing a data catalog is like choosing the right pair of shoes.

You want to find something that fits your needs, but you don't want to spend too much time looking for it. You want something affordable, but you also don't want to sacrifice quality just to save some money. And you need something that will be comfortable and durable enough for everyday use.

So how do you choose?

There are some questions that can help guide your decision-making process:

• Who will be using this tool? What kind of user experience do they need? If your team has never used a data catalog before, it may be helpful if the tool has an easy learning curve and allows users to get up and running quickly. On the other hand, if everyone on your team has been using data catalogs for years, they may want something more powerful and customizable than an off-the-shelf product would offer them.

• How much time do you have available to spend setting up this new system? If time is short—if there's an urgent deadline looming or if other projects demand attention—then you might need a quick fix. (Good news: Castor takes less than 30 min to become functional)

• Is there regulatory compliance involved? Is so, make sure you pick a tool that will help you on this side, too.

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3 - Processes

Last but not least are all the processes you decide to put in place around your metadata. Efficient metadata management processes ensure data integrity, consistency, trustworthiness, and compliance. More importantly, it facilitates the interaction of data consumers with information, i.e., people know which data the company has, where it is (which is especially important in today’s distributed environments), and how to find it – to derive maximum business value from digital assets.

Key metadata management processes include the following.

• Metadata creation policy: It’s essential for you to decide when and in which manner metadata will be generated. Starting with the creation process is the best way to ensure control over your metadata.
• Metadata standard/schema selection allows you to bring uniformity to your metadata.
• Metadata discovery and capture refer to extracting metadata across your data assets.
• Metadata quality assurance is checking if metadata complies with quality requirements.
• Metadata storage usually implies developing a specialized repository.
• Metadata cataloging is organizing metadata into a searchable inventory.

Metadata management is more than just data governance

Metadata management is more than just data governance.

It has three components: the people who are involved, how you obtain and manage the catalog, and how you will use the metadata. For example, metadata management might be limited to a single department in your organization or it could be an enterprise-wide effort involving dozens of stakeholders across different departments with varying degrees of responsibility for managing records.

In an enterprise metadata governance is key to improving business intelligence and enhancing customer experience that focuses on strengthening its three vital components:

• Roles and responsibilities
• Policies and procedures
• Metrics

Implementing a metadata management solution is a big undertaking. In order to properly identify, assess and manage your business content you need a robust metadata framework that will help you understand what to do, who is doing it, and how. The first step in creating a successful metadata management framework is to understand why it’s necessary and what value it brings to your business. The second step is figuring out which tools to use while developing processes that will allow you to easily keep track of changes made over time by multiple users across multiple systems without breaking anything else along the way!

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About us

We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation.

Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog software to be easy to use, delightful and friendly.

Want to check it out? Reach out to us and we will show you a demo.

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