Who is Responsible for a Data Catalog? Demystifying Roles and Responsibilities
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The responsibility for a data catalog in an organization typically lies with several roles, often under the purview of the data and analytics team. It includes Chief Data Officers, data stewards, data owners, data analysts, data scientists, the data governance team, and more.
In this article, we will understand how the responsibility for a data catalog is shared between different roles in an organization. We will also learn how to choose a data catalog that meets everybody’s unique requirements.
Let’s dive in!
Table of contents
- Roles and responsibilities in building a reliable data catalog
- Who is responsible for a data catalog? Unveiling the key players in detail
- How to choose a data catalog that suits everybody?
- Bringing it all together
- Who is responsible for a data catalog? Related reads
The collaborative network: Roles and responsibilities in building a reliable data catalog
Here’s a general breakdown of the roles and responsibilities:
- Chief Data Officer (CDO) or Director of Data & Analytics
- Data stewards
- Data owners
- Data analysts and data scientists
- Data governance team
- Data catalog product manager or administrator
Now, let us look into each of these roles and personas in brief:
1. Chief Data Officer (CDO) or Director of Data & Analytics
- At the highest level, the responsibility for the data catalog often rests with the Chief Data Officer (CDO) or Director of Data & Analytics.
- They set the overall data strategy and ensure that the data catalog aligns with the organization’s data governance practice and business needs.
2. Data stewards
- Data stewards are usually responsible for the quality, integrity, and definition of the data within the catalog.
- They define terms, maintain the data catalog, and ensure data standards are met. They often work closely with data owners to ensure data accuracy and consistency.
3. Data owners
- These are individuals who are accountable for specific datasets.
- They are often responsible for ensuring the accuracy of the data within their domain and providing context about their data to other users.
- They might also be involved in setting permissions for access to the data they own.
4. Data analysts and data scientists
- While they may not manage the data catalog per se, data analysts and data scientists are heavy users of the data catalog.
- They often help validate the accuracy and usefulness of the data, and they may provide feedback on how to improve the catalog.
5. Data governance team
- The data governance team usually oversees the creation, management, and usage of the data catalog.
- They set data policies, standards, and procedures, and they work to ensure that data catalog practices align with data governance goals.
6. Data catalog product manager or administrator
- Some organizations may have a specific role dedicated to the management of the data catalog.
- This role would involve setting up the catalog, adding and maintaining datasets, and coordinating with other stakeholders.
These roles work together to ensure that the data catalog is a useful and trusted tool that supports data discovery, data understanding, and data governance across the organization. The collaboration between these roles is vital for maintaining a successful and effective data catalog.
Who is responsible for a data catalog? Unveiling the key players in detail
Now that we know the roles and responsibilities of all players who maintain a data catalog, let us look into each of them in detail:
1. Data stewards
- Data stewards are essentially the ‘custodians’ of the data catalog. They ensure the consistency, accuracy, and reliability of the data entries in the catalog.
- They also help establish and maintain data governance policies and standards.
- For instance, a data steward might regularly review and validate data assets to ensure they are accurate and up-to-date.
- They make sure the data catalog aligns with the organization’s data governance policy.
2. Data analysts/Data scientists
- Data analysts and data scientists heavily rely on the data catalog for their daily work.
- They search for, access, and use data assets for various tasks such as data analysis, machine learning model building, or business intelligence reporting.
- They can also provide valuable input into the cataloging process by documenting the insights they gather from certain datasets and contributing to the metadata.
- This could include flagging any data quality issues they encounter, or suggesting additional tags or classifications for specific datasets to improve discoverability.
3. Data governance teams
- The data governance team defines the rules and standards for data management within the organization. Thus, it plays a significant role in establishing the practices for maintaining the data catalog.
- This could include defining what metadata to include for each data asset, establishing standards for data quality, or setting policies for who can access certain datasets.
- The governance team might also be responsible for tracking data usage and ensuring compliance with data privacy regulations.
4. IT/Database administrators
- They often oversee the technical aspects of the data catalog, including its setup, maintenance, and integration with other data systems.
- They may be involved in workflows like adding new data sources to the catalog, managing user permissions, or troubleshooting technical issues with the catalog software.
- They also work closely with the data governance team to ensure the catalog supports the established data governance policies.
5. Data consumers/ Business users
- While not typically responsible for maintaining the data catalog, data consumers (includes roles such as business analysts, marketing professionals, executives, etc.) are key users of it.
- They depend on the catalog to find and understand the data they need for decision-making.
- They can also contribute to the catalog by providing feedback on the relevance and utility of different data assets, or suggesting new data sources to include.
While the exact workflows and responsibilities may vary depending on the specific organization and the data catalog tool being used, these examples should give you a good sense of the typical roles involved in managing a data catalog.
How to choose a data catalog that suits everybody?
In the previous sections, we learned who is responsible for a data catalog. In this section, we will learn about how to choose a data catalog that matches everybody’s unique needs.
Choosing a data catalog that caters to the needs of all roles requires understanding the different needs of those roles. You also need to assess the features and capabilities of the catalog tools accordingly. Here are some factors to consider:
- User-friendly interface
- Rich metadata support
- Integration capabilities
- Data governance support
- Collaboration features
- Vendor support and training
Let us look into each of the above factors in detail:
1. User-friendly interface
- For data consumers, data analysts, and data scientists, ease of use is crucial.
- They should be able to search for and access the data they need without struggling with a complex interface.
- The tool should allow easy navigation, searching, and filtering of the catalog.
2. Rich metadata support
A good data catalog should support a wide range of metadata types, such as:
- Technical metadata (data types, data formats)
- Operational metadata (data freshness, data lineage), and
- Business metadata (data ownership, data description).
This is important for data stewards and data governance teams, as they work on cataloging and classifying data.
3. Integration capabilities
- For IT/database administrators, integration capabilities are key.
- The catalog should be able to connect to a variety of data sources (SQL databases, NoSQL databases, cloud storage, etc.) and also integrate well with other data systems like data warehouses or data lakes.
4. Data governance support
- The tool should support data governance activities such as setting data quality rules, managing data access permissions, and tracking data usage.
- It should also have features to help ensure compliance with data privacy and protection regulations, which is important for data governance teams.
5. Collaboration features
- For all roles, the ability to collaborate on data is important.
- The catalog should support features like sharing data assets, adding comments or notes to datasets, and providing feedback on data quality.
- As your organization grows, so too will the size and complexity of your data. Ensure that your chosen data catalog can scale to meet these growing needs.
7. Vendor support and training
- Good vendor support is crucial for all roles to help troubleshoot any issues that arise.
- Additionally, vendors should provide sufficient documentation and training resources to help users get the most out of the tool.
Remember, the best data catalog for your organization depends on your specific needs and context. It’s a good idea to start with a clear understanding of your requirements, and then evaluate different options against those requirements.
Also, we suggest you run a pilot with a shortlist of options to gather hands-on feedback from all user roles before making a final decision.
Bringing it all together
In conclusion, a data catalog is a collaborative effort involving multiple roles and personas within an organization. By understanding the responsibilities and needs of each role, organizations can select a data catalog that caters to their unique requirements, fostering effective collaboration and data-driven decision-making throughout the organization.
Who is responsible for a data catalog? Related reads
- What Is a Data Catalog? & Why Do You Need One in 2023?
- Essential Features of Data Catalogs
- Enterprise data catalog: Definition, Importance & benefits
- Data catalog benefits: 5 key reasons why you need one
- Open Source Data Catalog Software: 5 Popular Tools to Consider in 2023
- Data Catalog Platform: The Key To Future-Proofing Your Data Stack
- Top Data Catalog Use Cases Intrinsic to Data-Led Enterprises
- AI and Data Catalog: AI Data Catalog
- Benefits and Features of Snowflake: Snowflake Data Catalog
- dbt Data Catalog
- AWS Glue Data Catalog: Architecture, Components, and Crawlers
- Airbnb Data Catalog Democratizing Data With Dataportal
- Lexikon: Spotify’s Efficient Solution For Data Discovery And What You Can Learn From It
- Google Cloud Data Catalog Guide - Everything You Need to Know
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