Every organization wants to build a data catalog that organizes enterprise data into a simple, manageable format. A cutting-edge data catalog spans multiple data domains and clarifies data definitions, synonyms and essential business attributes. The catalog ensures all users understand and can efficiently leverage their data as a critical business asset.
A data catalog also identifies data owners, stewards and various subject matter experts. By identifying individual roles, business users know where to go when they have important questions about their data. As a result, data users from different departments can easily collaborate, saving significant time and energy.
Most importantly, a data catalog supplies technical and business lineage and knowledge of impacts to users, giving a precise understanding of their data—the flow and dependencies of data, from origination to completion and throughout the data supply chain.
The data catalog also identifies essential business processes, defines data access methods, and documents any usage restrictions. Companies must still incorporate high-quality data into their catalog to effectively leverage data for business insights.
While leading-edge technologies help, making a data catalog useful is challenging because there is so much complex data at stake. To build a useful data catalog, it must enable data stewards to improve the transactional data quality by surfacing the current state of the data.
Scoring is an important metric used to understand the quality of the data referenced in the catalog. Scoring can include complex calculations behind the scenes. However, it should also provide a simple result so data consumers can easily understand if they can trust the data.
Without high-quality data, organizations risk leveraging inaccurate information that, when used, can damage brand reputation, hurt customer adoption rates, and have a long, lasting negative impact across the enterprise.
Maintaining high-quality data throughout an enterprise and within a data catalog requires an integrated approach. One that combines the latest automated technologies with people, processes, and data to ensure the appropriate policies and procedures deliver trusted insights effectively. By including robust data quality controls on transactional data, organizations ensure only accurate, timely, and relevant information feeds in the catalog.
To build a high-quality data catalog with reliable information, organizations require a foundation of data governance combined with data quality and automated 3D data lineage technologies. Automated 3D lineage capabilities allow users to quickly uncover the impacts of data and where it fits into business processes. 3D lineage also details where data came from, where it’s going and its transformations. With traceability in the context of business processes and visualization of data, business users can quickly verify data sources, find and report data quality issues, and trust the information in their catalog.
Data is a valuable business commodity. However, it is only an asset if business users transform it into meaningful insights and improved outcomes. Ensuring that the catalog provides users with a clear understanding of how much they can trust the data is critical. This level of trust can be accomplished through a combination of governance and quality score indicators. The data catalog then streamlines data communication, giving users a one-stop-shop for all data needs. It also delivers enterprise-wide data understanding among business users to turn data assets into valuable intelligence.
Ultimately, organizations who build a data catalog that organizes high-quality business data enable consistent understanding among all data consumers to develop breakthrough intelligence.
Would you like additional information about building a high-quality data catalog? Listen to this webinar: https://www.infogix.com/resources/webinar-recording-how-newmont-gold-launched-a-data-catalog-in-just-weeks-and-scored-big-wins/.
For additional information about building a high-quality data catalog, read this article from Data Science Central: https://www.datasciencecentral.com/profiles/blogs/a-step-by-step-guide-to-build-a-data-catalog.
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