Metadata is the contextual information that describes, identifies, or otherwise makes sense of data.
We increasingly hear about metadata and its importance for data analysis. Although metadata sounds like a fancy word, it refers to a dead-simple concept. We owe the word "meta" (amongst other things) to the ancient greek. Meta means "about the thing itself". A meta-joke is a joke about jokes, meta-thinking is thinking about thinking. In a logical continuation of things, meta-data is data about data. It is data whose only purpose is to define and describe the data object it is linked to. A web page could have metadata that tells us about the software language, tools used, and objects it contains. Before I talk more about the importance of metadata, let's look at some examples in the real and digital world.
Let's start with some metadata examples
The word "metadata" started in the 1990s to describe online resources and quickly became popular.
Metadata is used by libraries to categorize and organize their collections, whether in physical or digital form. Metadata helps to identify, locate, and classify books, DVDs, magazines, and other objects in the library's collection.
Digital libraries, which have become increasingly popular, rely heavily on metadata. These libraries include electronic print repositories and digital image libraries, among others. While they are based on library principles, their metadata provision is designed to be more user-friendly to non-librarians. This means that they often use custom-built metadata fields, such as taxonomic classification, location, keywords, or copyright statements, rather than traditional cataloging approaches.Overall, metadata is a crucial tool for librarians and users alike to organize and find the resources they need.
In the real world, metadata is everywhere. Each time you open an email, read a book or order something on Amazon, you encounter metadata.
Every book is enriched with metadata. Thanks to metadata, books can be classified in a neat manner, enabling potential users to find them in no time. Metadata about books include:
- the title
- the author-name
- publisher details
- table of contents
- date of publishing
When you take a photo with your iPhone, metadata is generated and saved just as the photo is created. This metadata includes:
- the time at which the photo was taken
- file name
- what camera was used to create the file
Every time you get or send an email, you deal with metadata. It helps you sort and find emails quickly using keywords. Common metadata for emails include:
- A message ID
- The date and the time at which the email was sent
- The e-mail addresses of both the sender and recipients
- The subject
The different types of metadata
For clarity purposes, different types of metadata have been put in specific categories. The different types of metadata are the following:
Descriptive Metadata: This is data that provides information about a resource or a file to aid in its discovery and identification. Descriptive metadata includes elements such as title, abstract, author, and keywords. For instance, when looking at a book in a library, the descriptive metadata would be details like the book's title, the author's name, the publication year, and a summary of the book's content. These details aid in identifying and locating the book within a large collection.
Structural Metadata: This is data that gives insights into how a data object or resource is organized. It helps users understand the relationships within and between different data elements. For example, a table of contents in a book is a form of structural metadata. It shows the order of the chapters, how many pages each chapter has, and how all these chapters relate to each other.
Administrative Metadata: This refers to technical information that helps manage a resource. It includes details such as file creation date, type, permissions, as well as information on usage rights and intellectual property. An example of this could be a digital photograph's metadata, which might include the date and time the photo was taken, the camera model used, the file size and format, and possibly even the GPS coordinates where the photo was taken.
For a more comprehensive look into the different types of Metadata and their applications, feel free to take a look at our recent article.
Dealing with metadata storage?
There are two ways to deal with digital metadata storage.
- Digital metadata can be stored internally in the same file as the data. This type of storage is called embedded metadata. It travels with the data and changes with it, creating consistency. However, this storage method makes it difficult to manage all your metadata in one place, causing redundancy and making normalization impossible.
- Metadata can also be stored externally to the original database, in a metadata repository, usually a data catalog. Centralizing your organization's metadata in a single place allows for more efficient searching and management, avoiding redundancy issues. On the flip side, this storage method increases the risk of misalignment between the digital metadata and the data object, as changes in one might not be reflected in the other. Some data cataloging solutions, such as CastorDoc, prevent this from happening.
Why should you invest in a metadata management strategy?
Organizations that manage information, such as libraries and archives, have a long history of signaling or encoding document contents. Before computers, they used standardized cardboard index cards in 1954.
These descriptions were later computerized in the form of bibliographic and standardized records. They facilitate the internal management of document resources and, on the user side, make it possible to optimize the search and location of documents.
Digital libraries have used the same devices to manage and locate electronic documents. The exchange of data items extracted from these records was quickly standardized within distributed applications.
People tend to prioritize digital data over metadata, but metadata is essential for unlocking the value of your data.
I learned about the significance of metadata when I misplaced my bag with my car keys and valuable items in a park. Fortunately, I had a device called "tile" that helps me locate my car keys using metadata. Tile provides me access to my keys' metadata and I can track their location through the app. This helped me find my keys quickly and resume my work without any delay.
Metadata is crucial. My car keys are precious, but if I don't know where they're located, they are of no use to me. If your organization collects any kind of data, you're in the same situation. You can have great datasets, if you can't locate them in your cloud data warehouse or other locations, they are utterly useless as well.
This also shows you the importance of investing in a metadata management tool. I'm happy I have the tile, automatically generating and updating metadata about my car keys. In fact, I'm busy and don't want to waste time thinking about the location of my various devices and objects, regardless of how important they are. In an ideal world: I wouldn't spend any time thinking about my car keys, but just always find them right when I need them. Your organization might face the same issue with data. Digital assets multiply, and people just have other priorities than keeping a neat record of the metadata. Of course, it's nice to be able to locate a table right at the time when you need it, but it's surely not worth dedicating 100% of your energy keeping track of every digital asset in your cloud data warehouse. That's when it becomes interesting to invest in metadata management tools that automatically collect metadata about your sets. if you're looking for such a tool, we've made a benchmark of all the data cataloging solutions on the market. if you're not ready for a tool yet, but still want to maintain a neatly organized metadata repository, feel free to use our handmade solution.
How will good metadata management practices change your life?
You've probably got the message at this point: metadata is key. Still, cultivating metadata about your data objects will impact your organization in ways you can't imagine. Metadata provides valuable information about your data, including its source, meaning, and relationships to other data. This helps your organization in several areas, including data discovery and trust, data governance, data quality, and cost management/data maintenance.
Metadata tagging in a modern data catalog allows for the automatic classification of sensitive material and stricter control over who has access to what assets. If you want to make sure that your data satisfies the requirements of regulations like the CCPA, HIPAA, PCI DSS, GDPR, and any other privacy law that may come to pass, compliance officers can work with your data team to keep a close eye on it.
Data cataloging means that any flaws or anomalies with private information can also be identified and fixed. If compliance officers discover that sensitive information is stored in an inappropriate location, for instance, they can rectify the situation by working with the data team to protect the information and reevaluate the company’s security.
Data Discovery & Trust
A good metadata management strategy benefits data discovery, allowing you to easily locate your data and see who has access to it. Organized metadata provides context for each table, such as its contents, importer, associated dashboard and KPI, and other relevant information. In essence, metadata makes your data discoverable. A metadata repository can answer the following questions:
- Where should I look to find the relevant data?
- Does this data matter?
- What does this dataset represent?
- How can I use this data?
These questions may seem basic, but the reality is that many data users waste time trying to answer them. Neglecting metadata is a common issue in organizations, causing data analysts to spend hours searching for digital assets across various locations.
The issue becomes more critical as companies gather data from an increasing number of cloud source applications. Poorly organized enterprise data resources lead to confusion without clear documentation standards. When data is transferred from cloud applications to the cloud data warehouse, metadata is automatically generated for the data resources. However, this can result in files with the same name but different definitions of terms, leading to confusion. For example, the term "users" might refer to completely different concepts in Salesforce and Marketo. This highlights the need to establish a clear metadata management standard for your system.
Once your business collects metadata in a standardized process, it becomes easier to find the data you need at the time you need it by leveraging the search features of data discovery tools. Remember how easy it is to find a photo on your iPhone when the only thing you remember is the location where the photo was taken?
I'm enjoying this feature way too much, as I have more than 40 000 photos stored in the cloud. When you've collected metadata, you can find digital assets in a blink of an eye by using keywords, just like a google search.
This saves data users considerable amounts of time compared to having to scan each data source in the system in the quest for the right data asset.
Metadata also enables objects that are similar or linked to be paired with like objects, to help optimize the use of digital assets. For example, metadata can help you pair a database with the dashboard that has been created from this database.
It also enables objects that are dissimilar to be identified and paired with like objects to help optimize the use of data assets.
Data Security, Privacy and Governance
If you're dealing with data, you need to be prepared to deal with security and compliance issues. These matters often feel like a mountain to deal with. Sensitive data and private information should not end up in the wrong hands, yet it feels almost impossible to control things when you're dealing with thousands, even millions of datasets. The key to ensuring security and compliance with laws such as GDPR is to have a solid data governance strategy.
Data governance is a set of policies regarding data usage and data security. These policies are created to determine the appropriate actions to be applied to a given dataset.
Again, here, metadata saves your life. It provides the means for identifying, defining, and classifying data within categories to ensure strong data governance. More particularly, it allows you to:
- Flag private information (PII), meaning you can then control which users can be given access to this info.
- Contextualize digital assets, providing clear definitions for how information can lawfully be used.
- Identify information that shouldn't be kept. For regulatory purposes, expiration dates are usually specified for user records. If you keep data past this date, you expose yourself to a hefty fine. Well-maintained metadata helps you keep track of when data was created, and when is needs to be disposed of.
- Finally, metadata establishes a digital audit trail for regulatory compliance. A well-maintained data repository helps you prove compliance with regulatory frameworks such as GDPR. That's valuable: if you can't prove compliance, you're automatically considered as non-compliant by the authorities. And that's something you want to avoid, because failure to comply with GDPR has ugly consequences.
Data Quality Monitoring
High-quality data is highly desirable, as it makes your organization's resources more reliable, increasing the business benefits gained by using them. Data quality is measured according to the following basic set of dimensions:
- Accuracy: this describes the "degree to which the data correctly describes the 'real world' objects being described"
- Completeness: Completeness refers to the degree to which required data are in the dataset. A dataset with a lot of missing values is incomplete.
- Consistency: if the datasets are replicated in multiple locations, their content has to be consistent across all instances.
- Timeliness: This refers to whether your datasets are sufficiently up to date.
Before investing in an expensive data quality solution, look at what your metadata has to say about data quality. If you've invested in a metadata management strategy, data users should have the following elements for each digital asset at disposition:
- Table name
- Table description
- Creation date
- Last refresh
- Table owner
That already brings you quite far off in your data quality assessment. First, you know a bit about the accuracy of your data. In fact, the table definition provides you with clear information about what the digital asset contains, and how the information was collected, and by whom. This allows you to quickly check whether the information reflects real-world facts and has been accurately measured. Having a centralized repository of metadata also supports data consistency. Finally, metadata tells you when your tables were last refreshed and thus whether your data is up to date.
Cost management and maintenance
Finally, metadata can be of good help when it comes to optimizing database management and especially data storage. Different storage solutions have different costs. For example, it's more expensive to store data in a cloud data warehouse than in a database. In general, the easiest it is to access digital assets in a database, the more expensive this storage solution will be. Based on metadata, you can identify the tables that are used the most as well as the poorly used tables in your business. This is extremely practical, as it allows you to move the unused tables in less costly, harder to query storage spaces. On the basis of your metadata, you can create rules according to which data that hasn't been used in the past 30 days is immediately moved to a less costly storage bucket. Metadata allows you to pinpoint exactly how much each dataset costs you according to storage cost and usage.
A good metadata management solution also helps you maintain your databases better. Metadata about digital assets includes data quality scores, number of issues with the data asset on a certain period, etc. You can thus know exactly which datasets you should focus your maintenance efforts on based on metadata. If a digital asset has been down 10 times in the past few days, you will ensure someone fixes it as soon as possible. More generally, this helps you prioritize the actions of your data team, ensuring it has the greatest possible impact and generated business value.
Thanks to their strong metadata management strategy, Vestiaire Collective managed to de-clutter their data warehouse and boost their data team's productivity by 20%.
Do you need a metadata management tool?
Metadata is important. You've probably understood it by now. One question remains, though: can you document all the files in your systems manually, or do you need to invest in a metadata management tool to support your documentation efforts? The most important dimension to look at when deciding whether to invest in a metadata management solution is whether it makes a key difference in how you document resources and how you collect metadata. In fact, you need to understand first whether or not you need assistance to document the content of your cloud data warehouse.
Now you might be wondering: what kind of assistance does a data catalog provide? Fundamentally, the value of a metadata management tool resides in the fact that it automates the data documentation process. What does it mean? Say, you document a specific file in your system, enriching the columns with descriptive context and definitions. An intelligent data catalog will propagate the original definition you gave to a specific column to all the other columns that bear the same name in your cloud system. This means that each minute you spend on documentation has a much greater impact that when you document your data resources using a manual process. If you have thousands of datasets containing one column with the same name, writing a definition for one column is equivalent to writing definitions for thousands of columns. This is incredibly time and cost saving.
Now, whether you find automating this process interesting or not depends on whether your organization is an enterprise or a small business.
If you're a small business, you won't be dealing with too much data resources. In this case, putting someone (or a few people) in charge of documenting each file in your company is possible. Manually maintaining a data catalog to keep your system organized is a feasible option. If that's what you need at the moment, we've got a free to use template available, and we explain how to use it effectively.
In the enterprise case, your business might just be too large to document data content and files manually. It would take a disproportionate amount of time and human resources to document thousands of files. Especially given the fact that data is not static. Your company keeps collecting data throughout time. This means you would need to hire a full time data documentation team that continuously updates the metadata around your files. In terms of cost and time, it is generally more efficient to invest in a solution that automates the documentation process, bringing visibility to your system. If this is the most suited option for your business model, make sure you choose a tool that suits your company's needs. To compare enterprise level data catalogs, take a look at our comparison articles.
How does metadata impact data privacy and security in more detail?
Metadata plays a crucial role in enhancing data privacy and security by providing detailed information about data access, usage, and transfer. It can be used to implement robust access control mechanisms by defining who can access certain data based on the metadata attributes, such as the classification of data (e.g., public, confidential, sensitive). Metadata can also track data lineage, showing the flow of data through systems, which is vital for detecting unauthorized access or data breaches. Furthermore, metadata supports compliance with data protection regulations by documenting data handling practices and ensuring sensitive information is handled according to legal requirements. To mitigate risks, organizations should regularly audit their metadata to ensure it accurately reflects current data privacy and security policies.
What are the challenges and solutions in metadata standardization across different systems and organizations?
One of the main challenges in metadata standardization is the diversity of data formats, definitions, and structures across different systems and organizations. This diversity can lead to inconsistencies, making it difficult to manage and integrate data effectively. To address these challenges, organizations can adopt common metadata standards and frameworks, such as Dublin Core, ISO/IEC 11179, or Data Catalog Vocabulary (DCAT), to ensure consistency in metadata creation, storage, and exchange. Implementing a centralized metadata management system or repository can also help by providing a unified view and control over metadata across different systems. Additionally, collaboration and communication between organizations and departments are key to agreeing on standardized metadata practices and ensuring they are consistently applied.
Can metadata be misleading or incorrect, and how can such issues be addressed?
Yes, metadata can be misleading or incorrect due to human error, system glitches, or outdated information. Misleading or incorrect metadata can lead to misinterpretation of data, inefficient data management, and decision-making based on inaccurate information. To address these issues, organizations should implement processes for regular metadata review and validation. This can include automated checks for consistency and accuracy, as well as manual audits by data stewards or managers. Establishing clear guidelines for metadata creation and updates can help minimize errors. Additionally, leveraging metadata versioning and change tracking mechanisms ensures that changes are documented, and historical metadata can be reviewed to understand and correct discrepancies. Encouraging a culture of data quality and responsibility among all data users is also essential for maintaining accurate and reliable metadata.
Big data is incredibly valuable, but metadata is the key allowing organizations to access this value. A good metadata management strategy will help your organization around four dimensions: Data discovery, data governance, data quality and data maintenance. Maintaining a centralized repository of metadata manually is tiresome, and can quickly become unsustainable when the number of datasets you own start growing exponentially. Thankfully, there are plenty of tools out there for you to choose.
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 to be easy to use, delightful and friendly.
Want to check it out? Get a free 14 day demo with CastorDoc and try it for yourself!
Subscribe to the Castor Blog
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. 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.
You might also like
Explore the power of metadata and how CastorDoc's framework can improve your organization's data management and understanding.
Learn how a data glossary can help your organization ensure consistent and accurate communication about your data.
“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.” - Michal P., Head of Data