SPSS Modeler Entity Analytics adds a completely new dimension to SPSS Modeler predictive analytics. Whereas predictive analytics attempts to predict future behavior from past data, entity analytics focuses on improving the coherence and consistency of current data by resolving identity conflicts within the records themselves. An identity can be that of an individual, an organization, an object, or any other entity for which ambiguity might exist. Identity resolution can be vital in a number of fields, including customer relationship management, fraud detection, anti-money laundering, and national and international security.

You suspect that you may have identity problems with your data. For example, individuals might appear more than once, or distinct individuals might appear to be merged or missing. How can IBM® SPSS® Modeler Entity Analytics help you address this? The following is a suggested procedure, though you may need to vary this to suit your particular requirements.

  •     Read the source data into IBM SPSS Modeler

  •     Create a repository ready to store the data

  •     Connect IBM SPSS Modeler to the repository

  •     Map the data fields to repository features

  •     Export the data into the repository and resolve the identities

  •     Analyze the resolved identities

  •     Resolve new cases against the repository

  •     Generate any necessary alerts (batch or real-time)

At this point, you need to know something of how IBM SPSS Modeler works. IBM SPSS Modeler is a very user-friendly tool, based on the graphical representation of a stream of data flowing through a number of nodes. Each node represents a particular stage of the workflow.

IBM SPSS Modeler itself provides a wide range of nodes, covering all the standard data mining functions. IBM SPSS Modeler Entity Analytics adds nodes for use specifically in entity analytics. These are EA export node, the Entity Analytics(EA) source node, and the Streaming EA process node.


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