In today’s business world, organizations have access to large amounts of data. Before now, data owners spent a considerable amount of time finding and utilizing data. Today, data-intensive organizations rely on data analytics to gain a comparative advantage and derive meaningful insights for strategic decision-making.
Data catalogs allow for more efficient and effective use of data. In addition, data-centric organizations can find and utilize data from big data assets when they implement a data catalog. Data engineers in organizations that don’t own data catalogs experience difficulty handling data integration and data management complexities. One question that keeps coming up whenever the data catalog is mentioned anywhere is what is a data catalog? Thankfully, we’ve come up with a fact-based answer to this question.
Data Catalog Defined
A data catalog refers to an organized inventory of data assets that allows data consumers to discover, access, and assess data in one place. Data catalog uses metadata to enable data users to quickly search, understand, and utilize an organization’s data assets. Specifically, the metadata is integral to discovering data that was not initially included in the data user’s search. This invariably allows for a more fact-based data analysis. Users can derive meaningful insights from curating and managing data in a centralized platform.
Thanks to technological advancements, some high-end data catalogs offer a wide range of compatibility across applications, clouds, platforms, and databases. Data catalogs can exist as standalone entities or can be integrated with an analytics platform. As a standalone entity, it can sync with an API to provide a more cohesive consumer experience.
The end-users of a data catalog tool vary from applications to specified roles across an organization. By organizing inventory of available data sets into a searchable, centralized system, data consumers can find, understand, and utilize data more efficiently in less time.
Main Features of a Data Catalog
Today, many data catalogs support the automation of data cataloging. Regardless, there are some key capabilities you should watch for in a data catalog system:
- Governance and Access Control: In addition to performing its standard capabilities, a robust data catalog is built to provide governance and access control. This allows for self-service analytics and the breaking down of data silos. A data catalog system that syncs with a data governance solution will enable the data consumer to define policies and processes in a centralized platform. This will also mitigate data quality issues.
- Searchability: Searchability is undoubtedly one of the hallmark features of a data catalog. Additionally, a data catalog should promote data discovery. Users should be allowed to use tags and filters to narrow down their search.
- Metadata Gathering: Many data-intensive organizations are moving away from on-premises systems to multi-cloud solutions. Data catalogs enable connection and data curation from different data warehouses, databases, business intelligence (BI) tools, and ETL. This feature is integral to scaling data access in one place.
- Artificial Intelligence Integrations: These technologies automate the process of data tagging, organization, and classification. In addition, they can use data usage and result from queries to assign context to an organization’s data assets at scale.
- Collaborative Capabilities: An intuitive data catalog tool enables non-technical users to discover and utilize data. This feature promotes collaborative business-wide data use.
Organizing Data With a Catalog
Organizing data with a catalog may seem daunting and overwhelming. For the most part, the complexities and functionalities that data catalogs offer make it all the more challenging. Thankfully, we’ve provided a step-by-step guide to get you started with the organizational process:
- Evaluate the metadata throughout the organization’s data assets to identify data files, tables, and databases. Afterward, integrate the metadata into your data catalog.
- Create data profiles with the descriptions of data points. This will enable data consumers to locate and understand data faster.
- Setup linkages within the catalog using the relationships between data across the organization’s databases. Doing so allows for more robust query results.
- Assess data lineage to how the data originated and the transformations it has undergone to reach its present state. This is integral for troubleshooting analytical errors.
- Use techniques like tagging or sorting to organize data into the system.
- Incorporate data security measures to strengthen data security and mitigate data loss and data breaches.