Emc IP Holding Company LLC

Anand Singh

Stephen J Todd

Sudhir Vijendra

Barbara Latulippe

Qing Ye

Narayanan Krishnakumar

David M Dionisio

Stephen Gatchell

Perry Davis

At least one application data set stored in a data repository is obtained. The application data set is analyzed to generate at least one metadata node. The at least one metadata node is combined with at least one other related node to form a hierarchical data structure. One or more valuation algorithms are executed against the hierarchical data structure to calculate a value for the data set represented in the hierarchical data structure.

Metadata-based data valuation

Techniques are disclosed for creating high value data assets from undervalued data. In one example, a method identifies at least one data asset associated with an enterprise which has a lower non-economic valuation score as compared with one or more other data assets associated with the enterprise and is determined to be undervalued. The method then combines the at least one identified data asset with at least one of the one or more other data assets to form a composite data asset, wherein the composite data asset has a higher non-economic valuation score as compared to the at least one identified data asset.

Creation of high value data assets from undervalued data

One or more data assets associated with a data repository of a given enterprise is identified. Each of the one or more data assets is tagged with economic driver metadata that links each of the one or more data assets to at least one economic driver category from a plurality of economic driver categories associated with the given enterprise. At least one economic value is calculated for each of the one or more data assets based on the at least one economic driver category linked to each of the one or more data assets. Calculated economic values for the one or more data assets are stored in a valuation data structure.

Economic valuation of data assets

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.

Data governance with automated risk valuation via impact per event analytics

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.

Hierarchical value-based governance architecture for enterprise data assets

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.

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Westborough, MA, United States of America

Anand Singh