Abstract: A data profiling module receives user selection of spreadsheets, and the data from the selected spreadsheets is profiled. At least one matching column is identified among the spreadsheets selected. The data profiling module calculates a match metric for the at least one matching column, and unifies the spreadsheets into a single composite spreadsheet using the at least one identified matching column. A preview view of a composite spreadsheet is generated, visually indicating the at least one matching column, any non-matching columns between the spreadsheets, and the match metric for the matching columns. An action history module identifies spreadsheets for use in the procedure, and stores any action applied to the spreadsheets as a procedure template that can be applied to a plurality of other spreadsheets.

Abstract: A data profiling module receives user selection of spreadsheets, and the data from the selected spreadsheets is profiled. At least one matching column is identified among the spreadsheets selected. The data profiling module calculates a match metric for the at least one matching column, and unifies the spreadsheets into a single composite spreadsheet using the at least one identified matching column. A preview view of a composite spreadsheet is generated, visually indicating the at least one matching column, any non-matching columns between the spreadsheets, and the match metric for the matching columns. An action history module identifies spreadsheets for use in the procedure, and stores any action applied to the spreadsheets as a procedure template that can be applied to a plurality of other spreadsheets.

Abstract: Techniques are provided for determining which entities, in a population of entities, is most like a given entity. In the context of companies, the techniques involve constructing a peer network graph based on company-to-company relationship data. Once the graph is constructed, the weights of the edges are determined, and values for the paths are determined based on the edge weights. Peer connection scores are generated for a particular company based on the number and values of the paths between the node that represents the company and the nodes that represent other companies. Based on the peer connection scores between the particular company and other companies, a subset of the other companies are selected as members of a peer group for the company.

Abstract: Techniques are provided for determining which entities, in a population of entities, is most like a given entity. In the context of companies, the techniques involve constructing a peer network graph based on company-to-company relationship data. Once the graph is constructed, the weights of the edges are determined, and values for the paths are determined based on the edge weights. Peer connection scores are generated for a particular company based on the number and values of the paths between the node that represents the company and the nodes that represent other companies. Based on the peer connection scores between the particular company and other companies, a subset of the other companies are selected as members of a peer group for the company.