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DataFlux Data Management Studio 2.7: User Guide

Overview of Data Explorations

Data explorations enable you and your organization to identify data redundancies and extract and organize metadata from multiple sources. Relationships between metadata can be identified and cataloged into types of data by specified business data types and processes.

A data exploration reads data from databases and categorizes the fields in the selected tables into categories. These categories have been predefined in the Quality Knowledge Base (QKB). Data explorations perform this categorization by matching column names. You also have the option of sampling the data in the table to determine whether the data is one of the specific types of categories in the QKB.

For example, your customer metadata might be grouped into one catalog and your address metadata might be grouped in another catalog. Once you have organized your metadata into manageable chunks, you can identify relationships between the metadata by table-level profiling. Creating a data exploration enables you to analyze tables within databases to locate potential matches and plan for the profiles that you need to run.

Once you have identified possible matches, you can plan the best way to handle your data and create a profile job for any database, table, or field. Thus, you can use a data exploration of your metadata to decide on the most efficient and profitable way to profile your physical data.

For more information about setting QKB options, see How can I specify Quality Knowledge Base options for profiles and data explorations?

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