In an increasingly technology-driven world, properly managed data has the potential to solve critical business challenges. But how can an organization properly manage data when so much time is spent discovering, understanding, and trusting it? A complete Data Catalog solution aims to address these concerns by making data discoverable, meaningful, accessible, and useful. It drives a shift from centralized, IT-driven data management to strategically-aligned business collaboration. Below I’ve listed 5 functions I believe are critical to any Data Catalog solution. In the comments section below, I’d love to hear the community’s additions to this list, or why you agree or disagree with what I’ve proposed.
This is a given, but the core of any catalog solution is collecting data about an organization’s data repository. This includes the technical metadata - such as field names, data types, paths, restrictions, etc., and business functional metadata - such as the people, policies, and processes related to the data. Having this information is crucial for the reuse and optimization of cataloged data.
Machine Learning & AI Capabilities
Machine learning algorithms can be leveraged to automate asset relationships, improve the quality of search results, and expose data conflicts or quality deficiencies. This type of automation will ultimately enable more time spent using cataloged data, rather than finding or remediating it.
Though much of the work around catalog metadata is automated, often the most valuable metadata is the knowledge and experiences of the data consumers. Crowdsourced solutions enable a company’s network of business users to collectively enrich the value of cataloged data, and consistently improve the output of machine learning algorithms.
A chosen solution should work seamlessly through data preparation and usage cycles, as well as integrate with existing security infrastructure and access controls. Overlooking this essential component can overly complicate and decrease returns on a catalog investment.
While the catalog may not calculate a dollar value for data assets, it should be able to provide contextual alignment to organizational goals and initiatives. This in turn can provide insights contributing to a data asset value estimation.
The Data Catalog is a core component of the Syniti Cloud. Though we pride ourselves in developing superior data management solutions, our product and engineering teams strive daily to improve the usefulness and functionality of our solutions. I’d love to hear the community’s additions, or counterpoints to the list above, along with any business-specific examples you may be able to share. As I touched on earlier - crowdsourced solutions often produce the most superior results.
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