Abstract: A data governance method comprises the following steps. One or more data elements associated with an enterprise are identified as one or more critical data elements. A data structure is maintained for the one or more critical data elements. For a given critical data element, the data structure stores information that reflects an impact that the given critical data element had on at least one event associated with the enterprise. The method predicts a risk associated with a subsequent impact that the given critical data element may have on at least one subsequent event associated with the enterprise, wherein the risk of the subsequent impact is predicted using at least a portion of the information stored in the data structure.

Abstract: An apparatus in one embodiment comprises at least one processing platform accessible to a plurality of user devices over at least one network. The processing platform implements a hierarchical value-based governance system for data assets of an enterprise. The hierarchical value-based governance system comprises a data asset governance controller, a technical metadata hub, a business metadata hub and a data asset catalog. The data asset catalog is coupled to the technical metadata hub and the business metadata hub and provides integrated technical metadata and business metadata for each of a plurality of the data assets of the enterprise. The data asset governance controller is configured to generate analytics actions for one or more analytics workspaces based at least in part on the integrated technical metadata and business metadata of the data asset catalog. The analytics actions may comprise visualizations providing actionable insights into the corresponding data assets.

Abstract: A data governance method comprises the following steps. Data elements from data assets associated with an enterprise are obtained. One or more of the data elements are identified as one or more critical data elements based on a level of criticality computed for each of the one or more data elements. In illustrative embodiments, the level of criticality is based on one or more of: a cardinality computed for the one or more data elements; a business relevance criterion computed for the one or more data elements; and an indirect cross-data lake correlation criterion computed for the one or more data elements.