What is a data catalog?
A data catalog is a collection of an organization's datasets and data management tools. It helps data scientists and business users to find information quickly and easily. Data catalogs are standard for metadata management.
Data catalogs use metadata to create an inventory of all datasets in the organization. It gives users a single place to view all the available data.
Types of data catalogs
Depending on what metadata a data catalog handles, there are three different types, as mentioned below:
- Technical metadata data catalogs: This metadata tells users how data is organized and displayed to users by explaining the structure of data objects like tables, rows, and columns. A data catalog extracts, standardizes, and indexes metadata.
- Process metadata data catalogs: This metadata describes the circumstances of various operations in a data warehouse. Data catalogs enrich the metadata collected from different operations to make it useful for the users.
- Business metadata data catalogs: Business metadata or external metadata focuses on the business value of the metadata. The business metadata could include information such as data ownership, attributes classifying data sources, and more.
Benefits of data catalogs
A data catalog helps data citizens of any organization search and access data in an organization. It offers users the following benefits:
- Improved data context: Data catalogs help users access data through its descriptions and comments by other data citizens that help them better understand the context and the data.
- Reduced risk: Data catalogs ensure that data is only being used for intended purposes and aligns with company policies and data laws.
- Accurate and faster data analysis: Contextual data makes it more feasible for analysts to give more precise analyses and for data professionals to respond quickly to difficulties.
- Increased efficiency: Data catalogs help users help discover data faster, so there is more time to analyze the data.
- Reduced time to find data: Data catalogs help users instantly see the source and data sample to understand whether the data found solves the purpose.
Data cataloging best practices
A data catalog is a useful platform for data management. However, without a data cataloging methodology, the data cannot be used to the fullest. To make a data catalog work, users can follow these best practices:
- Include all data types: It is advisable to include all data types in the catalog because the ultimate goal of the data catalog is to help users understand and discover the data that they are often unfamiliar with.
- Make sensitive data a priority: It is essential to know the whereabouts of sensitive data. If sensitive data is found in multiple locations, it is helpful to identify redundant data. Understanding the location of sensitive data helps build strong governance and data protection policies.
- Use clear descriptions: A clear and verbose description helps in discovering data. An alternate name for the same objects could be an example of a description and help build data relations more comprehensively.
- Manage dataflows: Managing dataflows is advised for a better functioning data catalog. Data flow discovery helps in identifying flows between various data sources. That further helps in understanding the organization's dataflows that are unknown.
- Make it a data lake: It is advised to create zones in the data catalog once all kinds of datasets are put into it. Making zones will help keep the data catalog organized and make it easier for users to find the required data.
- Leverage machine learning techniques: Manual cataloging is complex due to the large amounts of data. Using machine learning, it is possible to control the pace and volume of data being entered.
Data catalog vs. metadata management
Data catalogs and metadata management are often interchangeably used. However, there is a difference in the way both function. Metadata management involves activities towards data governance, analytics, and overall discipline over data management. On the other hand, data catalogs form the central part of metadata management, providing a repository of data and the value that data offers.
Data catalogs are tools that help metadata management, whereas metadata management is the policies that help govern the storage and use of metadata. Metadata management is an approach to data management, whereas a data catalog is a tool that enables data management. Metadata forms a part of the data catalog.