Palisades Research: Consulting firm specializing in SAS data warehouse solutions, OLAP and internet-enabled

information delivery for decision support, statistical analysis for marketing and financial applications, and data  mining
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An enterprise can enhance its information delivery and analytical capabilities through three major development initiatives:

  • Data Warehouse
  • Data Mart
  • Data Mining

First: Organize the information currently scattered among different sources and store it in a data warehouse. Scattered and incompatible data from different sources is gathered together (extraction) and then cleansed and made consistent (transformation).

Second: Subset and process the relevant datasets in a data warehouse to create one or more specialized data warehouses or data marts. The data mart may be derived from a data warehouse containing transactional records. Each data mart contains the data needed for a related group of analyses. The data is summarized at appropriate levels. The first and second steps can be combined so that only the relevant data is transformed.

The summaries in the data warehouse or data mart are now available for ad hoc queries and for integration by SAS into multidimensional database (MDDB) cubes, accessed through the Internet or an intranet and analyzed, using the appropriate middleware – e.g., SAS IntrNet or ASP. SAS IntrNet also makes it easy to create a web-based user interface to enter parameters and run any SAS program, and to view HTML-formatted output on the desktop.

Users can query summarized datasets through their browsers via HTML forms or more active front ends employing cgi, ASP, Visual Basic, or Java. MDDB cubes can be viewed through the browser using products such as SAS OLAP Viewer or Microsoft Office Web Components, or as Microsoft Excel pivot tables.

Example: Multiple records for each customer in a large auto insurance data warehouse are summarized to create a data mart consisting of (1) one record for each customer with just that subset of fields needed for rating agency reporting and (2) more highly summarized data needed to create multidimensional database cubes for on-line analytical processing (OLAP).

We develop data warehouses and data marts efficiently by utilizing state-of-the-art middleware tools that allow us to reach diverse operational databases and integrate this enterprise data into a single end-to-end system. Since business users can interact with the data warehouse or data mart directly through their Web browsers – e.g., performing OLAP on SAS multidimensional database cubes - they gain immediate access to critical management decision information.

Third: Enhance decision support by adding data mining tools to access and analyze the contents of the datawarehouse.
Data mining is the process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns to achieve business advantage. This technology integrates different statistical and pattern recognition techniques such as neural networks, tree-based models, churn analysis, and traditional statistics, allowing the discovery of patterns, trends, exceptions, relationships, and anomalies that might otherwise stay hidden. We apply data mining methodology to a data warehouse or data mart to address following business problems:

- Demographic profiling of high value customers using decision tree models.
- Response modeling using a combination of regression methods, selecting those that produce the best gain chart.
- Determining of the hierarchy structure of the MDDB and defining the drill-down pass in OLAP applications. We exploit decision tree models using the dimensions of the data cube as independent variables and a high-value customer flag as a response variable.


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