What is in a Data Catalog? Understanding and Evaluating it for Effective Data Governance and Discovery

Last Updated on: May 12th, 2023, Published on: May 12th, 2023

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A data catalog is a centralized and organized repository that serves as a single source of truth for your organization’s data. It enables users to easily discover, understand, and manage data from various sources, including databases, data warehouses, and data lakes.

A data catalog is especially useful when dealing with large-scale and complex data environments, and when aiming to make it accessible to both technical and non-technical users.

Now, let us look into the key components of a data catalog.

Table of contents

  1. Key components of a data catalog
  2. Understanding a data catalog’s integration capabilities and the impact on its features
  3. Evaluating data catalog software: Must-have and good-to-have features to get the most out of it
  4. Why Atlan with active metadata and AI is a strong data catalog option
  5. Bringing it all together
  6. What is in a data catalog? Related reads

Key components of a data catalog

A data catalog typically includes the following:

  1. Metadata
  2. Data profiling
  3. Data lineage and relationships
  4. Search and discovery
  5. Data access and security
  6. Collaboration and social features

Let us look into each of the above components in brief:

1. Metadata


This is the data about your data, which provides context and helps users understand the data’s origin, structure, and meaning. Metadata can include information like column names, data types, descriptions, and data lineage.

2. Data profiling


Data catalogs often include tools for profiling data, which helps users better understand the quality, distribution, and relationships among different data elements. This can include summary statistics, data distributions, and data uniqueness.

3. Data lineage and relationships


Data catalogs can track data lineage, which shows how data has been transformed and moved through different systems. This helps users understand data dependencies and the impact of changes on downstream processes. Relationship mapping can also identify connections between different data sets, making it easier to explore related data.

4. Search and discovery


A data catalog provides a search interface that enables users to quickly find relevant data sets based on keywords, tags, or other criteria. This simplifies data discovery and helps users quickly locate the information they need for analysis or reporting.

5. Data access and security


Data catalogs often include features to manage data access and enforce security policies. This ensures that sensitive data is only accessible to authorized users and that data usage complies with regulatory requirements.

6. Collaboration and social features


Some data catalogs include features that promote collaboration among users, such as the ability to add comments, annotations, or reviews to data assets. This can help foster a data-driven culture within your organization and encourage knowledge sharing.

By implementing a data catalog, your organization can make it easier for both technical and non-technical users to find, understand, and work with data in your entire data estate. This will empower your business users to leverage data for decision-making without needing deep technical expertise, while also helping to maintain data quality and security.

Understanding a data catalog’s integration capabilities and the impact on its features

A data catalog can integrate with various components of an organization’s data infrastructure, including databases, data warehouses, data lakes, ETL tools, and business intelligence (BI) tools, among others. The integrations impact the data catalog in several ways:

  1. Data sources
  2. ETL and data integration tools
  3. Data quality and governance tools
  4. Business intelligence (BI) and analytics tools
  5. Data science platforms and machine learning frameworks
  6. Data security and access control systems

Let us look into each of these integrations in brief:

1. Data sources


A data catalog integrates with databases, data warehouses, and data lakes, which serve as primary data storage systems. This allows the catalog to gather metadata and profile data from various sources and provide a comprehensive view of the data landscape.

2. ETL and data integration tools


ETL (Extract, Transform, Load) tools and other data integration solutions are used to move and transform data between systems. Integration with these tools enables the data catalog to track data lineage, understand data transformations, and maintain up-to-date metadata.

3. Data quality and governance tools


Integrating with data quality and governance tools allows the data catalog to enforce data quality rules, apply data governance policies, and track data quality metrics. This helps ensure that the catalog contains accurate, reliable, and compliant information.

4. Business intelligence (BI) and analytics tools


By integrating with BI and analytics tools, the data catalog can provide context for reports, dashboards, and analyses, making it easier for users to understand and trust the data they’re working with. Integration also allows users to access data catalog features (e.g., search, metadata) directly within their analytics tools, streamlining the data discovery and analysis process.

5. Data science platforms and machine learning frameworks


Integrating with data science platforms and machine learning frameworks allows data scientists to leverage the data catalog’s metadata, profiling, and lineage information in their workflows. This can improve the efficiency and accuracy of data preparation, feature engineering, and model evaluation.

6. Data security and access control systems


By integrating with security and access control systems, data catalogs can enforce data protection policies, manage user permissions, and ensure that sensitive data is only accessible to authorized users.

The integrations between a data catalog and other components of your data ecosystem have a significant impact on the catalog’s content and capabilities. Integrations help to ensure that the catalog provides a comprehensive, accurate, and up-to-date view of your organization’s data, while also facilitating collaboration, data quality, and governance.

Ultimately, these integrations enhance the value of the data catalog, making it a more effective tool for empowering users and driving data-driven decision-making.

Evaluating data catalog software: Must-have and good-to-have features to get the most out of it

When evaluating data catalog software, it’s essential to consider both the must-have and good-to-have features. These features will help your organization meet its current needs while also ensuring it is future-ready. Here’s a breakdown of the must-have and good-to-have features:

Data catalog software: Must-have features


Here are the must-have features in data catalog software:

1. Metadata management


Robust metadata management is crucial for any data catalog. The software should be able to automatically extract, store, and update metadata from various data sources, including technical, business, and operational metadata.

2. Data profiling and quality


The data catalog should provide data profiling capabilities to assess data quality, identify data anomalies, and highlight data patterns. This helps users trust the data and make informed decisions.

3. Data Lineage and relationship mapping


The ability to track data lineage and map relationships between data sets is essential. This feature provides transparency into data’s origin, transformations, and dependencies, ensuring users understand the impact of changes on downstream processes.

4. Search and discovery


A powerful search and discovery functionality is a must for any data catalog. Users should be able to easily find relevant data sets based on keywords, tags, or other criteria.

5. Data access and security


The data catalog should integrate with your organization’s existing security infrastructure, enforce access controls, and maintain data compliance. This ensures that sensitive data is only accessible to authorized users.

6. Scalability and performance


As your organization’s data needs to grow, the data catalog should be able to scale and maintain high performance. Choose a solution that can handle increasing data volumes and sources without compromising performance.

Data catalog software: Good-to-have features


Here are the good-to-have features of a data catalog software:

1. Collaboration and social features


While not mandatory, collaboration features like commenting, annotations, and reviews can help foster a data-driven culture within your organization and encourage knowledge sharing among users.

2. Integration with BI, analytics, and data science tools


Integration with your existing BI, analytics, and data science tools allows users to leverage the data catalog’s features directly within their familiar tools, streamlining their workflows.

3. Machine learning and AI capabilities


AI and machine learning can enhance data catalog features by automating metadata generation, suggesting relationships, and improving search and discovery through natural language processing (NLP).

4. Customization and extensibility


A data catalog that offers customization and extensibility options allows your organization to tailor the solution to your specific needs and processes, ensuring a better fit for your data ecosystem.

5. Cloud-native architecture


A cloud-native data catalog provides flexibility, scalability, and cost-effectiveness. It can also simplify integration with cloud-based data storage solutions like Snowflake.

By prioritizing the must-have features and considering the good-to-have ones, you can choose a data catalog solution that best meets your organization’s current and future needs.

Why Atlan with active metadata and AI is a strong data catalog option

Atlan, with its active metadata platform and AI capabilities, could be a strong option for your data catalog needs. The introduction of Atlan AI as a co-pilot for data teams brings several advantages that can significantly improve your data management and discovery processes.

Let’s look at some key benefits that Atlan AI brings to your organization:

1. Chat-based data discovery and auto-generating SQL


Atlan AI simplifies data exploration by allowing users to search for data and get answers to their questions using natural language. This eliminates the need to open a query editor or write SQL, making it accessible for both technical and non-technical users.

2. Automated documentation


Atlan AI streamlines the documentation process by generating a first draft of descriptions and READMEs for your data assets based on their name, schema, and lineage context. This can save time and effort on manual documentation, especially for organizations with a large backlog of assets.

3. Exploratory capabilities


Atlan AI suggests questions to ask your data and shows you what questions your team is asking, promoting data exploration and insights generation for all team members, regardless of their technical expertise.

4. Integrations with modern data stack


Atlan’s platform integrates with popular tools like Slack, Snowflake, dbt Labs, Redshift, Looker, Sisense, and Tableau, ensuring seamless connectivity with your existing data infrastructure.

5. Industry recognition


Atlan has been recognized by Forrester, Gartner, and G2 as a leader in data catalogs, data governance, machine learning data catalogs, and data quality, indicating a strong and reliable solution for your organization.

Considering these features and benefits, Atlan and its AI-powered capabilities could be a great fit for your organization’s data catalog needs. Its focus on simplifying data discovery and management while promoting collaboration and accessibility for all team members aligns well with your objectives of empowering business users and ensuring your organization is future-ready.

Bringing it all together

Deploying a data catalog starts the seeding process of data democratization and data enablement in your organization. It helps users discover, understand, and trust the data while ensuring compliance and governance.

However, not all data catalogs are created equal. To get the most out of it, you need to know its key components, integration capabilities, and its features that help you meet your organization’s current and future needs.

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

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