As the volume of data grows, so does the complexity of managing it. Many organizations are struggling with sprawling data sets, stored in on-premises systems, cloud services, and even IoT infrastructure. This growing amount of data complicates data management and makes it difficult to identify meaningful insights. The results are that organizations are drowning in data but starving for insights. This is a situation that makes the use of databases all the more critical.
The data stored in databases is often located in silos, which limits its discovery by users. In addition, some data analysts may need to create new datasets or rely on incomplete or inaccurate information. To overcome this problem, data catalogs provide a central repository of all data assets in one unified location. Using a search engine-like user interface, data catalogs retrieve a list of data assets matching specified filters. They also provide APIs to make it easier to access and create new datasets based on new data.
The right data catalog is vital to improving data quality. It should provide insights into datasets and flag incomplete or conflicting data sets. Furthermore, it should incorporate a community-based quality control mechanism so that users can rate and review data quality. The data catalog should also empower manual curators to enrich data. They should be able to add metadata, flag sensitive data, and remove unreliable datasets.
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