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What is Netezza?

Netezza is a dedicated data warehouse appliance that uses a proprietary architecture called Asymmetric Massively Parallel Processing (AMPP) that combines open blade-based servers and disk storage with a proprietary data filtering process using field-programmable gate arrays (FPGAs). Netezza integrates a database, server, and storage, which are all interconnected by a powerful network fabric into a single, easy to manage system that requires minimal set-up and ongoing administration, leading to shorter deployment cycles and faster time to value for business analytics. Read more

On Febuary 22, 2012 Ann Winblad, a Silicon Valley titan indicated that “data is the new oil” and “predictive analytics is the next big thing”

Today, most organizations use business intelligence to understand what and why things happened in the past. Business imperatives are increasingly driving organizations to look forward and use their data to improve decision accuracy. This is the promise of predictive analytics, yet many organizations believe heavy statistical skill sets are required before taking on this next phase of business analytics. Guided wizards and other SPSS usability advances make predictive analytics available to a much broader group of BUSINESS USERS TODAY than most realize. Gartner’s CIO study for the past 3 out of 5 years rates business intelligence and analytics as the #1 priority. Said another way, those with predictive analytics skill sets will be in high demand for many years to come. Read more

Smarter business decisions and a large return on investment can be among the key benefits of a predictive analytics solution. But all solutions are not created equal.

That’s why it’s critical not to miss our complimentary IBM SPSS Data Mining Workshop hosted by the Ironside Group and IBM.  You will get hands-on experience with Modeler software to help you better understand the distinctive value of its easy-to-use, rapid-prototyping, predictive modeling capabilities. Read more

IBM SPSS offers a variety of integration options with other enterprise solutions, i.e., enterprise data warehouses, file systems, and business intelligence applications. In this article, we specifically focus on the integration with the IBM Cognos BI environment. As most of you are aware, IBM SPSS leverages the power of predictive analytics by allowing the user to examine the current state of their business, while at the same time providing a view of the future using advanced analytics techniques. With the use of the IBM Cognos BI integration point, the results are immediately available for IBM Cognos reporting, allowing for easy distribution to broad user communities.

In our previous article, we described the general workflow on how to import and export IBM Cognos packages from IBM SPSS Modeler. In this article, we will provide a simple case study using the standard IBM Cognos sample set to further illustrate the above mentioned integration.

Note:

-The integration is only available in IBM SPSS Modeler version 14.1 or higher.

-The supported IBM Cognos environments are version 8.4 and higher.

Providing Data to IBM SPSS from IBM Cognos

The IBM Cognos BI source node enables data miners to read data directly from IBM Cognos Framework Manager, including relational, dimensionally-modeled relational (DMR), and OLAP sourced packages.

In this case study, we select the Go Sales (query) package from IBM Cognos samples and use the following items from the query subject as our source data:

Go Sales (query)

  • Sales (query)

    • Sales

      • Revenue
    • Time

      • Year
      • Month Key
      • Month
      • Date

Before choosing a Framework Manager package to import the data, the user needs to establish a connection to the IBM Cognos server by providing the dispatcher URL and user credentials.

Conduct Data Mining Analysis in IBM SPSS Modeler

The source data we bring in from IBM Cognos contains revenue data for years 2004-2007. The objective of this study is to use advanced modeling techniques to discover trends behind the revenue data and predict future revenues. The following stream in IBM SPSS Modeler uses time series analysis to analyze the patterns in the monthly historical data and project those patterns to determine a range within which future values of the series are likely to fall.

 

 

 

In this example, we extend the forecast of the expected revenue to the next four time periods. The following output from IBM SPSS is a time series graph and shows the predicted (green line) vs. actual (blue line) revenue for each month between Jan, 2004 – Jul, 2007 and the projected revenue for Aug – Nov 2007.

 

 

 

 

Exporting IBM SPSS Predictive Results into IBM Cognos BI

The end results from an IBM SPSS Modeler stream can be exported back to the IBM Cognos BI environment, including both the transformed and scored data based on predictive modeling. In this case study, we will export the scored dataset of time series analysis into a database table called “Predicted_Sales” and generate an IBM Cognos package on the Cognos server reflecting the new database table.

Once the export is complete, we could use IBM Cognos Report Studio to create a visual report based on the exported data, including the predictions and confidence interval values for consumption by a broad user base.

 

 

 

 

 

 

 

 

Through this case study, we showcase how a user can combine the predictive analytic capabilities of IBM SPSS Modeler with business intelligence features of IBM Cognos BI. For any additional questions regarding the integration and its implementation, please contact us at the Ironside Group.

 

 

One of the features of IBM SPSS Modeler 14.1 is that it can now directly integrate with your IBM Cognos BI environment to leverage the power of predictive analytics. If you are new to IBM SPSS then feel free to reference our previous newsletter article showcasing all the components of IBM SPSS. This month’s tech tip will detail how IBM SPS Modeler integration is accomplished.

Before we get started with the tech tip, let’s review SPSS Modeler architecture from a high level.

1. Use an IBM Cognos BI source node to read data directly from a Cognos 8/10 Framework Manager package. This allows analysts to dive into the data mining process using friendly IBM Cognos package items rather than use underlying database tables that they may or may not be intimately familiar with.

2. Once the data mining process is completed, export the results back to a database table and create a Framework Manager package to be published on the IBM Cognos server for additional analysis and reporting.

Importing from Cognos Packages

1. Launch IBM SPSS Modeler and create a new stream.

2. From the Sources node tab, drag and drop the IBM Cognos BI node into the stream. Right click on the node and choose Edit.

3. Click on Edit within the Connection box to enter the Cognos connection information. Click OK to establish the connection.

4. Enter the following:

  •  Cognos server URL: Enter the dispatcher URL.
  •  Mode: Choose Set Credentials to log in with Cognos username and password. Choose Use Anonymous Connection to log in as anonymous user (only if anonymous login is enabled on the Cognos side).
  •  Namespace: Enter the namespace used to authenticate the Cognos user.
  • User name/Password: Cognos log in credential.

5. Click on Edit within the Package box to select a Cognos package.

6. Once a package is chosen, the content will show on the left hand side of the dialog. Choose one or multiple items to bring them to the right side. All chosen items will be imported as columns to SPSS.

Exporting Cognos Packages

1. From the Export node tab, drag and drop the IBM Cognos BI Export node into the stream.

2. Right click on the node and choose Edit.

3. Click on Edit within the Connection box to enter the Cognos connection information. This is similar to what you have entered in the Cognos BI source node.

4. Once the connection is established, click on the Refresh button next to the Data Source to choose a data source where the information will be written.

5. Click on Edit next to the Folder box to choose a location where the package will be stored.

6. Enter a package name and choose to publish it now or create an action script to publish it later.

7. On the left side, choose ODBC connection to enter the ODBC connection info to the database.

8. Click on the drop down arrow next to the data source box to select or create the database connection. This connection must point to the same data source that you selected in the Cognos connection tab. If the connection has not already been created, the user needs to create it in ODBC Data Source Administrator on the Windows machine. If you are connecting through an SPSS server, the data connection needs to be created on the SPSS server.

9. Enter the table name and select how you would like the information to be added to the table. Edit additional information as needed.

10. Click Run to publish the package in Cognos server.

For any additional questions regarding SPSS Modeler and its implementation, please Contact Us.