Findability of data is important because business users like to search for information using familiar terms

By collecting data and applying the right business words, data classes, and quality assessments so it can be found, managed, and used successfully, metadata enrichment scales the onboarding of new data into a governed data environment. By guaranteeing data quality, usability, and safety for wider consumption, this feature dramatically boosts the productivity of data stewards who offer business context to data.

To fully benefit from metadata enrichments, you should publish data assets that have been enhanced with metadata to a catalog where users can easily identify and get the necessary data. The cornerstone of governance is making sure the proper business terms are applied to data since they specify the data's business nature and the laws and regulations that govern its management and protection. It's also essential for the findability of data because business users like to search for information using terms that they are familiar with. When data is poorly categorized, it affects the implementation of relevant policies and the accuracy and recall of search results.

But how can you make sure that when a commercial user searches for data, the search encompasses your end-to-end data environment and extends beyond data silos? An architectural strategy for streamlining data access in an organization to promote self-service data consumption is called a data fabric. A data fabric architecture, which integrates end-to-end data management abilities while being agnostic to data environments, processes, utility, and geography, can streamline data discovery, governance, and consumption, allowing businesses to manage data as a product. With a data fabric, businesses can increase the value of their data by delivering the appropriate data at the appropriate moment, wherever it may be. Users can implement appropriate governance and related regulations by combining infrastructural, technical, and business metadata utilizing standard procedures. Through metadata enrichment, this technology ultimately assists users in knowing their data, trusting their data, protecting their data, and consuming their data.

With just one click, users can now add data quality analysis, profiling, and automatic assessment to their data using Watson Knowledge Catalog's new metadata enrichment feature. This enables users to deliver high-quality data more quickly, jump-start governance, and seamlessly scale business understanding. Data's term assignment is driven by ML that isn't limited to a single global model and can change at the project level using regular Watson Knowledge Catalog connectivity that's fully integrated into IBM Cloud Pak for Data projects. This method enables businesses to scale business data understanding and, when necessary, manually change the results of automated operations. Teams can quickly test and utilize new models without affecting other teams.