How to Seamlessly Import SharePoint Data into IBM SPSS Modeler

IBM SPSS Modeler is a powerful data mining and predictive analytics platform that enables organizations to extract valuable insights from their data. By connecting SharePoint data data to SPSS Modeler via the CData ODBC Driver for SharePoint, you can leverage real-time access for advanced data mining, predictive modeling, and statistical analysis.

This guide takes you through the steps of connecting IBM SPSS Modeler to SharePoint data, enabling seamless data import, preparation, and analysis. With the CData ODBC Driver for SharePoint, you can unlock the full potential of your SharePoint data data within IBM SPSS Modeler for actionable insights.

Getting Started

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Overview

Here is an overview of the steps:

1. CONFIGURE THE ODBC DRIVER: Set up a connection to SharePoint data in the CData ODBC Driver for SharePoint by entering the required connection properties.
2. SET UP ODBC CONNECTION IN SPSS MODELER: Establish the ODBC connection within IBM SPSS Modeler by selecting the configured DSN.
3. IMPORT AND PROCESS DATA: Import the SharePoint data data into SPSS Modeler, then review, filter, transform, and prepare the data for predictive analytics and statistical modeling.

Configure the SharePoint DSN Using the CData ODBC Driver

To start, configure the DSN (Data Source Name) for SharePoint data in your system using the CData ODBC Driver. Download and install a 30-day free trial with all the features from here.

Once installed, launch the ODBC Data Source Administrator:

• On Windows: Search for ODBC Data Source Administrator in the Start menu and open the application.
• On Mac: Open Applications, go to Utilities, and select ODBC Manager.
• On Linux: Use the command line to launch ODBC Data Source Administrator or use unixODBC if installed.

Once launched, double-click on the CData SharePoint data Source and enter the required values to establish a connection:

Set the URL property to the base SharePoint site or to a sub-site. This allows you to query any lists and other SharePoint entities defined for the site or sub-site.

The User and Password properties, under the Authentication section, must be set to valid SharePoint user credentials when using SharePoint On-Premise.

If you are connecting to SharePoint Online, set the SharePointEdition to SHAREPOINTONLINE along with the User and Password connection string properties. For more details on connecting to SharePoint Online, see the "Getting Started" chapter of the help documentation

Setup an ODBC Connection in IBM SPSS Modeler

After configuring the DSN, it's time to connect to it in IBM SPSS Modeler:

• Launch IBM SPSS Modeler, log in, and create a new stream.
• From the Sources palette, locate the Database node, and drag it onto the canvas.
• Double-click the Database node to open the configuration dialog. Select , browse to select the configured DSN, then click OK.
• In the Database dialog, browse to select the table(s) you’d like to import, preview the data, and click OK to finalize.

You are now ready to process and analyze the SharePoint data data in IBM SPSS Modeler.

Process Data: Filter, Categories, and Model

Once the tables are imported, you can refine, filter, categorize, and model your SharePoint data data in SPSS Modeler:

• Filtering: Double-click your Database connection, go to the Filter section, and select/deselect fields to focus on relevant data. This improves processing speed and model accuracy.
• Set Data Types and Roles: Categorize your fields and assign roles to each data type by navigating to the Types section.
• Perform a Basic Analysis: Drag and drop the Analysis node next to your Database node, connect them, and click the Play button to run the stream and analyze the data.

You have now performed a simple analysis, enabling SPSS Modeler to process and display insights from your database.

Unlock the Potential of Your SharePoint Data with CData

With the CData ODBC Driver for SharePoint, connecting SharePoint data data to IBM SPSS Modeler is seamless. Start your free trial today and unlock the full potential of your real-time data for advanced analytics and decision-making.