Understanding how to effectively use a data catalog can be a game changer for organizations looking to maximize their data management and discovery.
Data catalogs are centralized repositories that organize and categorize all types of metadata, including technical and business metadata. A data catalog is used to allow an organization’s users to more easily search and discover data. The most important part of implementing a data catalog is choosing the right data catalog solution.
A data catalog should be 3 things:
- A way to discover & make your data more accessible
- An easy to use resource for both data and business stakeholders
- Future-proof and built to scale
Find more detail and an introduction to data catalogs in our guide here.
1. Select your data catalog
The first step to implementing a data catalog is investing in the right tool for your organization. We have written a whole guide on how to select the right data catalog for your organization - you can find it here.
When selecting a data catalog, prioritize a tool that not only allows you to discover your data but also actively contributes to making it more accessible. Look for robust search functionalities that help you find exactly what you are looking for, swiftly and precisely.
At Secoda, we’ve grown our data catalog to be able to connect data quality, observability, and discovery. A data catalog that provides visibility into the health of your entire stack, and prevents data asset sprawl, can work overtime so your team can focus on your main priorities.
Once you have selected your data catalog, you’re ready to start populating it.
2. Find and integrate your data sources
Take advantage of modern data catalogs that offer native integrations with the tools in your data pipeline, and are able to easily integrate with your data sources. Choosing a data catalog that is frequently creating new integrations and is receptive to customer feedback and requests will pay off if your stack ever changes, or if you need a more custom solution.
Some data catalogs offer options for customers to build their own custom integrations, so that no matter what kind of tool you’d like to integrate into your data catalog, you’ll be able to.
Integrating your data sources will make it quick and easy to find and understand the meaning of specific data, which can greatly enhance your data management and discovery efforts.
Follow the steps provided by your data catalog platform to integrate both cloud and on-premises data sources.
Benefits to connecting your data to Secoda
- Improved data lineage: By connecting your data to Secoda, you can automate the process of tracking and documenting data lineage. This can help you understand where your data comes from, how it has been transformed, and how it is used within your organization.
- Enhanced data documentation: Connecting your data to Secoda allows you to automatically generate documentation for your data assets, including descriptions, definitions, and metadata. This can help improve the transparency and understandability of your data, making it easier for others to use and trust.
- Improved data discovery: By connecting your data to Secoda, you can make it easier for others to discover and access your data assets. This can help improve collaboration and ensure that team members have the information they need to make informed decisions.
- Enhanced data governance: Connecting your data to Secoda allows you to establish clear roles and responsibilities for data management and ensure that all team members follow established data governance policies and procedures.
How does Secoda integrate into your data?
Secoda integrates with your databases, data warehouses and BI tools to fetch metadata, query history and activity from data sources to make it easier to share and document data knowledge with stakeholders.
As soon as the metadata and query history have been added to Secoda, they are compiled into a unified metadata store by way of a query parser. By parsing queries, we look at what data is there, where it is, and how it is used.
If queries are available, the tables, databases and dashboards will display a data column and table level lineage model, to easily see from one place what the data dependencies are, where the data came from, and where it's being used.
3. Tagging information in your data catalog
Tags are a static way to label your metadata and other resources with useful context that other users in your workspace need to know. Secoda has two built-in default tags, PII Identifier and Verified Identifier. To add additional tags to your workspace, you can create Custom Tags based on how you'd like to categorize data in your organization.
Create tag templates, tags, overviews, and data stewards to help organize and categorize your data. This will make it easier to find and use your data assets.
4. Search and View Data Assets
Not all search functionalities in data catalogs are built equally. Modern data catalogs should include advanced search capabilities that allow any user to explore and discover data insights. Advancements like natural language processing empower even nontechnical users to access the data they need when they need it. In short, enabling better data search allows for more efficient and effective data discovery, helping to boost productivity and reduce data request bottlenecks for the data team. Everything in your data catalog will be indexed via search, so it is critical you select a catalog with a fantastic search feature.
Search is very important to Secoda users and we are constantly improving the functionality to provide the best experience. You can search for any data resource using Secoda's search, including tables, columns, collections, dictionary terms, documents, metrics and questions. You can search for terms in plain language, or by the table name and data source if you already know what you're looking for.
Look for data catalogs with search features like:
- Semantic search
- LLM-powered search
- A modern UI that will be easy for all users to understand
6. Assign data owners
After populating your data catalog, identify the important people for each data asset and assign data users such as owners to your data assets. This will ensure that your data is managed effectively.
Benefits to assigning ownership
Assigning owners has many benefits. If your data has ownership defined, you can:
- Understand who will be affected by upstream or downstream changes to a dataset
- Understand who to talk to regarding information about the data
- Get notifications when the datasets change
- Identify abandoned data sets no one currently owns, that may need to be maintained or removed
How to assign owners to data in Secoda
After navigating to a Secoda resource, Editors and Admins can assign Owners by clicking the Owners field and selecting the relevant members from the workspace. The owners field is accessible through Catalog views, or in the side panel.
7. Update and maintain the data catalog
Regularly update and maintain your data catalog to keep track of new data assets, make changes to existing data assets, remove obsolete data assets, and ensure the catalog remains useful and trustworthy to users. Take the following steps to ensure your data stays complete and maintained.
Establish Data Quality Monitoring:
- Select a tool that can help you to monitor and report data quality issues automatically.
- Establish protocols for resolving and preventing data quality issues.
Enforce Data Security Measures:
- Select a tool that easily lets you implement access controls and permissions to ensure data privacy and security.
- Regularly audit and review user access rights.
Continuous Improvement:
- Solicit feedback from users and data stewards for continuous improvement.
Integrate with your Data Stack:
- Integrate the data catalog with other business intelligence and analytics tools for seamless data discovery and analysis.
Promote Data Culture:
- Foster a data-driven culture by promoting the importance of using the data catalog for decision-making.
- Encourage collaboration and knowledge sharing around data assets.
What are the key features of a data catalog?
Understand what makes a data catalog go from good to invaluable:
- AI: Act as an intelligent co-pilot for your organization
- Discovery: Democratize your data and make it accessible for all
- Governance: Ensure your data is used appropriately
- Scalability: Choose a catalog that is built to scale
- Ease of Adoption: Simplify adoption and encourage user engagement
- Security: Keep your data protected and secure
- Support & Customers: Get help when you need it by choosing a reliable partner
How has artificial intelligence impacted data catalog management?
Artificial intelligence has significantly revolutionized data catalog management. Modern data catalogs that leverage AI are not just tools; they are strategic assets in the data management strategy. AI transforms data catalogs into intelligent co-pilots for everyone at the organization, streamlining workflows, and ensuring that data becomes a powerful ally in driving business success. As organizations continue to struggle with increasing data complexities, embracing AI-driven data catalogs becomes not just an option but a necessity in staying ahead in the data-driven era.
Better insights
AI-powered data catalog platforms enable organizations to get better data-driven insights in several ways. Improving data quality certainly helps, but AI platforms can also allow nontechnical users to use natural language search to discover data faster and without help from the data team. This frees up time for the data team and empowers business users in your organization to make more data-driven decisions in a fraction of the time. It also makes collaboration easier.
Data discovery and management
AI data catalogs improve data discovery and make data management processes much more efficient. Using an AI-powered data catalog, you can automate tasks like data tagging, identification, organization and more. Teams can also get the relevant data they need in a fraction of the time.
Data quality
AI-powered data catalogs can help improve data quality by identifying inconsistencies, data errors and duplicates automatically. These processes can be completely automated, making it easier for your data teams to focus on other tasks.
Data context
AI-powered data catalogs can automate data lineage and documentation tasks to help organizations remain compliant with their industry data regulations. Automated data lineage makes it much easier to track and monitor the entire lifecycle of data, and time-consuming documentation tasks no longer have to eat up hours of the workweek.
AI and Secoda
AI-powered data catalogs serve as strategic assets in modern data management, making it easier than ever for any user to navigate data, streamline workflows,and enhance data-driven decision making.
Data catalogs powered with AI will assist your data team in writing documentation, descriptions, auto tagging PII and sensitive information, generating end to end lineage and more.
Non-technical users will have AI to guide them through the data catalog, with AI Assistants to help answer questions in natural language, generate SQL queries, and write docs based on all the information in the data catalog.
At Secoda, we believe the future of data discovery will be driven by large language models that allow anyone to ask any question to their data - read more about why we are doubling down on AI powered data catalogs.
Unlocking the Power of Data Catalogs
Understanding and effectively using a data catalog can transform the way your organization manages and utilizes its data. From discovering and collaborating on data, to understanding the meaning of specific data, a data catalog is a powerful tool that enhances data visibility, accessibility, and governance.
Selecting a data catalog with careful consideration is paramount for strategic alignment, operational efficiency, and long-term success.
A well-chosen data catalog optimizes workflows, enhances data quality and accuracy, and ensures compliance with security and privacy regulations. Its user-friendly interface encourages widespread adoption, contributing to better-informed decision-making.
Careful consideration also addresses scalability, adaptability to change, and maximizes the return on investment by aligning with the organization's growth trajectory.
Ultimately, the careful selection of a data catalog is an investment in the organization's ability to navigate the complexities of a data-driven landscape and leverage data assets for sustained success.
