10 Data Catalog Best Practices All Data Teams Should Know

Last Updated on: June 22nd, 2023, Published on: May 17th, 2023
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A data catalog is the backbone of modern data management, enabling organizations to find, understand, trust, and use their data effectively. Using a data catalog can be a transformative step for organizations aiming to enhance data governance and promote data literacy.

However, to maximize the benefits of a data catalog, it is essential to follow best practices that ensure its effectiveness and usability.

Here are some universal best practices to keep in mind when using a data catalog.

Table of contents

  1. What are the best practices for using a data catalog?
  2. Data catalog best practices: Taking a closer look
  3. Implementing data catalogs: Common challenges and how to overcome them
  4. Data catalog best practices: Books and online resources for more learning
  5. Data catalog best practices: Related reads

What are the best practices for using a data catalog?

Using a data catalog effectively requires following certain best practices to ensure its usefulness and maintain its accuracy. Here are some key best practices for using a data catalog:

  1. Data catalog selection
  2. Data governance
  3. Data classification and organization
  4. Data lineage
  5. Data catalog maintenance
  6. Promote data literacy
  7. Search functionality
  8. Collaboration and feedback
  9. Automate where possible
  10. Compliance

Now, let us look into each of the above data catalog best practices in brief:

1. Data catalog selection


When choosing a data catalog tool, consider one that supports metadata management, data lineage, and data discovery. The tool should also integrate well with your existing data storage and consumption layers.

It should support your current and future data architectures (relational, NoSQL, cloud storage, etc.). Security features are critical, especially for a healthcare organization handling sensitive patient data.

2. Data governance


Implementing a data catalog tool is closely tied to data governance. Establish clear guidelines for data ownership, data quality, and data privacy. Having a strong data governance framework in place ensures that your data catalog remains accurate, up-to-date, and trustworthy.

3. Data classification and organization


Classify and organize data properly in the catalog. Use descriptive, standardized naming conventions for data assets. Metadata, such as data descriptions, should be clear and easy to understand, facilitating data discovery for users.

4. Data lineage


Your data catalog should provide clear data lineage - the journey of data from its source through various transformations. This helps in understanding data provenance, improves trust, and aids in debugging data issues.

5. Data catalog maintenance


Regularly update and maintain your data catalog. It’s a living tool, and data assets will change over time. Regular maintenance ensures that the catalog remains a reliable resource.

6. Promote data literacy


Encourage a culture of data literacy within your organization. A data catalog is only as good as its usage - training and education can help staff understand how to use the catalog effectively.

7. Search functionality


A key feature of a data catalog is its search functionality. Ensure your data catalog has robust search capabilities that allow users to easily find the data they’re looking for.

8. Collaboration and feedback


Encourage users to provide feedback and collaborate through the data catalog. Some catalog tools provide features like ratings, reviews, and comments on data assets.

9. Automate where possible


Automation can help in keeping the data catalog up-to-date. For example, automatic metadata extraction and tagging can make the catalog more efficient and easier to maintain.

10. Compliance


For a healthcare organization, compliance with regulations like HIPAA or GDPR is essential. Ensure your data catalog supports compliance, for instance, by identifying and protecting sensitive data.

Implementing a data catalog can be a complex process, but with these best practices, you can make sure you’re using it in the most effective way possible.

Data catalog best practices: Taking a closer look

Now, let us look at each of the above points in greater detail:

1. Data catalog selection


The data catalog tool you select should align with your organization’s data strategy, technology stack, and business goals. The tool must support metadata management, data lineage, and data discovery, which are essential for understanding your data assets.

It should also be compatible with your existing data storage and BI tools, and support various types of data architectures, such as relational databases, NoSQL databases, cloud storage, etc. Given the sensitive nature of healthcare data, robust security features are a must, including user access controls and data protection measures.

2. Data governance


Data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. It involves setting guidelines for who can take what action, upon what data, in what situations.

This includes setting up data ownership (who is responsible for what data), data quality (ensuring the data in the catalog is accurate and up-to-date), and data privacy (ensuring sensitive data is properly protected). By having these guidelines, you ensure that your data catalog is reliable and trustworthy.

3. Data classification and organization


All data within the catalog should be properly classified and organized. This involves using consistent, standardized naming conventions for data assets, and clear and understandable metadata (data about data, such as when it was created and by whom). This helps users understand what each data asset is and how it should be used.

4. Data lineage


Data lineage refers to the life-cycle of a data element, from its origins to where it moves over time. Understanding data lineage helps enhance data quality, simplifies troubleshooting and debugging, and improves trust in data. The data catalog should clearly show the lineage of data assets.

5. Data catalog maintenance


A data catalog should be regularly updated and maintained. This involves keeping track of new data assets being created, changes to existing data assets, and removing obsolete data assets. Regular maintenance ensures the catalog remains useful and trustworthy to users.

6. Promote data literacy


Data literacy refers to the ability to read, understand, create, and communicate data as information. Staff should be trained on how to use the data catalog and how to interpret and use the data it contains. Promoting a culture of data literacy ensures that the data catalog is used effectively and to its fullest potential.

7. Search functionality


The data catalog should have robust search capabilities, enabling users to easily find the data they need. This includes not just keyword search, but also other methods like faceted search (filtering based on certain criteria), and natural language search (searching using everyday language).

8. Collaboration and feedback


Users should be encouraged to provide feedback on the data catalog and to collaborate with others through it. This can be facilitated by features like ratings, reviews, and comments on data assets. Collaboration and feedback help improve the quality of the data catalog and engage users with it.

9. Automate where possible


Automation can help maintain the data catalog by reducing manual effort and minimizing errors. For example, automatic metadata extraction can pull metadata directly from data assets, and automatic tagging can classify data assets without manual intervention.

10. Compliance


The data catalog should support compliance with relevant regulations. For a healthcare organization, this includes HIPAA, which sets standards for protecting sensitive patient data, and GDPR, which regulates data protection and privacy. The data catalog can support compliance by identifying sensitive data and ensuring it is properly protected.

Remember, the success of a data catalog depends not just on the tool itself, but on how it is implemented and used. These best practices should help guide your team in effectively implementing and using a data catalog.

Implementing data catalogs: Common challenges and how to overcome them

Implementing and using a data catalog can indeed present challenges. Here are some common ones, along with strategies for mitigating them, often using the best practices outlined earlier:

  1. Data quality issues
  2. User adoption
  3. Keeping the catalog up-to-date
  4. Finding the right data
  5. Ensuring compliance

Now, let us look into each of these challenges and how to address them.

1. Data quality issues


Poor data quality can render a data catalog ineffective. For example, if data is mislabeled or metadata is inaccurate, it can lead to confusion and misuse of data. To mitigate this, establish a strong data governance framework (best practice #2).

Make sure you have clear guidelines for data quality, and that data owners are responsible for the quality of their data. Regular maintenance and updates of the catalog (best practice #5) also help ensure data quality.

2. User adoption


Even the best data catalog will not be useful if people do not use it. Users might resist adopting the catalog, especially if they are not used to working with data or do not understand the benefits of the catalog.

To address this, promote a culture of data literacy within the organization (best practice #6). Provide training and support for using the catalog, and show users how it can help them in their work.

3. Keeping the data catalog up-to-date


As data assets change and new ones are created, the data catalog can quickly become outdated. An outdated catalog can lead to confusion and misuse of data. To mitigate this, automate where possible (best practice #9).

For example, set up automatic metadata extraction and tagging to keep the catalog updated with minimal manual effort. Regular maintenance and updates (best practice #5) are also key.

4. Finding the right data


With a vast amount of data in the catalog, users might struggle to find the data they need. To address this, ensure the catalog has robust search functionality (best practice #7), and that data is properly classified and organized (best practice #3). The metadata should be clear and easy to understand, helping users find and understand the data they need.

5. Ensuring compliance


Especially for a healthcare organization, ensuring compliance with regulations like HIPAA or GDPR can be challenging. Non-compliance can lead to legal issues and damage to the organization’s reputation. To mitigate this, make sure your data catalog supports compliance (best practice #10). The catalog should help identify sensitive data and ensure it is properly protected.

For example, a healthcare organization might have a large amount of patient data stored in various systems. Implementing a data catalog can be challenging because the data is in different formats and the quality is inconsistent.

To address this, the organization could establish a data governance framework, with clear guidelines for data quality and data ownership. They could also use automatic metadata extraction to pull metadata from different systems and bring it into the catalog. The catalog would then provide a unified view of the organization’s data, improving data discovery and usage.

Data catalog best practices: Books and online resources for more learning

To help you leverage a data catalog in your organization, here are some resources and books that can provide valuable insights, strategies, and practical guidance:

Books


Here are some books that you might find useful:

  1. “The Data Catalog: Sherlock Holmes’ Secret Weapon in the Big Data Battle” by Bonnie O’Neil and Lowell Fryman: This book provides a comprehensive overview of the importance of data catalogs and includes several real-life examples.
  2. ”Data Management at Scale: Best Practices for Enterprise Architecture” by Piethein Strengholt: This book covers various aspects of data management, including data catalogs, at an enterprise scale. It includes practical advice and case studies.
  3. ”The Enterprise Big Data Lake” by Alex Gorelik: This book provides insights into managing big data in organizations, including the role of a data catalog. It includes examples from several industries.

Online resources


Here are some online resources that you might find useful:

  1. Gartner’s research on data catalogs: Gartner, a leading research and advisory company, has published several papers on data catalogs, including case studies and best practices.
  2. Atlan’s case studies: Atlan, a provider of data catalog solutions, has published several case studies on how organizations have used their data catalog.
  3. Blogs and articles on Towards Data Science or Medium: These platforms often have articles written by data professionals sharing their real-life experiences with data catalogs.

Remember, each organization is unique, so what works for one may not work for another. Take the time to understand your organization’s specific needs and challenges when implementing a data catalog.

What’s next

Deploying a data catalog starts the seeding process of data democratization and data enablement in your organization. It says that your organization is serious about maximizing the value of data.

It also recognizes that we can extract much more from data when we create an even playing field for the diverse data users in an organization. A data catalog is a starting point for that inclusive initiative.

Are you looking for a data catalog for your organization — you might want to check out Atlan.

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