Guide to Building a Data Catalog
With data powering modern enterprises, data catalogs are the key to leveraging the value of your data assets. With this guide to data cataloging, you can start building a data catalog for your business and make the most of your assets.
What Is a Data Catalog?
A data catalog is a detailed inventory of metadata with integrated data management tools to help users and analysts find what they need. Data catalogs have become the standard for managing metadata because most enterprises are dealing with high volumes of data in the modern world.
Data catalogs offer various functions that make them valuable to organizations. These organizational systems will rely on machine learning and AI to collect metadata and tag it for organizational purposes. With a robust data catalog in place, enterprises can experience features such as:
- Dataset searching through keywords, business terms, and relevance
- Dataset evaluation for previewing datasets and assessing data quality
- Data access through different technologies or directly
- Collaborative data management
- Data usage tracking
- Systems for data governance
The Benefits of a Data Catalog
With a robust data catalog in place, enterprises can experience the benefits of a reliable system for organizing and accessing data. Advantages include:
- Increased efficiency: Data catalogs eliminate the need for complex requests and data-wrangling procedures and create a seamless user experience. Users search the catalog and find what they need in an efficient self-service system.
- Improved data contextualization: A data catalog references business metadata and technical metadata to better contextualize information. It can also look to business glossaries to define terms within a dataset.
- Greater trust: Previewing data becomes simple with a data catalog, allowing users to identify incorrect values with ease. This fact streamlines the process of ensuring quality data and improves trust in existing data for making informed decisions.
- Enhanced protection and compliance: When an enterprise uses a data catalog in conjunction with a reliable data governance platform, navigating changing regulations becomes simpler and sensitive data remains protected.
How to Build a Data Catalog
To build a robust data catalog, enterprises need to apply the following steps.
1. Collect Data
In this initial stage, organizations identify what metadata needs to be captured and how to do so. During this process, consider data in tables or schemas. Ask the questions you most often want answers to and determine what data is involved in answering the question. For example, if you want to know the cost of acquisition for your customers, you need information on your leads and customers.
2. Assign Dataset Owners
Each dataset should have a point of contact so anyone involved with the data knows who they can contact with additional questions. These dataset owners will also be responsible for ensuring documentation is complete and correct.
3. Document With the Right System
Documentation is valuable to understanding your data assets, but when you first start your data catalog, there are likely to be gaps. You can still leverage value from your data assets, even with missing documentation, but you should make a reliable plan for documenting as you go to fill in those gaps.
You might conduct documentation tasks on a daily basis, or different teams can take on documentation responsibilities based on their expertise. Find a system that works for you.
4. Update and Optimize
Enterprises need a system in place to implement updates when necessary. Governance actions may be the key to updating documentation that is inaccurate as data assets change.
In addition to a system of updates, consider how to optimize your data catalog for your team. The optimization strategy should include creating documentation formats, identifying learning plans, and establishing systems for using the data catalog, so every team can leverage the value of your data assets.
Build Your Data Catalog With RightData
RightData offers two tools that empower enterprises to transform their data and gather valuable insights. With Dextrus, organizations can experience a self-service, no-code solution that wrangles datasets for analytics and drives powerful insights. With RDt, enterprises can maintain the quality of their data with data validation, dataset analytics, and much more.
