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Predicting Project Success with IBM SPSS

A technology company has literally participated in thousands of projects over the years. At some point the group decided it wants to determine what factors or characteristics may influence the (hopefully successful) competition of current and future implementation projects. Thankfully, they have maintained records in on every project that they were involved in and that data contains the following (among other) informational fields:

  • Project id – this is a unique internal project identifier
  • Client– this is a flag field indicating if the client was a new or established client (Y/N)
  • Contractors – this is a flag field indicating if non-employees were utilized on the project (compared to fulltime or salaried employees) (Y/N)
  • Internal Project Manager – this is a flag field indicating  if a fulltime internal project manager was assigned to the project (Y/N)
  • Project Status – this is a flag field indicating  if the project was completed successfully (on time, within budget)  (Target)
  • Technology Category -this is a flag field indicating if the core technology used for the project  was an  Established or Emerging technology
  • Team Size – this is a continuous field that provides the number of full time team members assigned to the project (1, 2, 3…)

Opportunity for SPSS Modeler

The first step was to perform a simple data extract to create a file that I can import into Modeler (in this case, an industry standard CSV). I limited the extract to the fields I am interested in, but I could’ve used Modeler to exclude or filter the data:









Once I had my data, I created a modeling stream starting with the Modeler VAR FILE source node (the source node makes it easy to find a file, set some defaults and import the data into SPSS).


The next step is to add the very important Type node. The Type node is where you review each field in the file and set a level of measurement (like “Continuous” for the team size field and “Flag” for fields such as client and contractors) for each of the fields in the file. It also is where I select my Target field (in this case the target is “project status”).












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Finally, we Model

After adding a SPSS Modeler node (CHAID) and connecting it to the Type node, I can run my stream.

When execution is finished, a “model nugget” is added to the Type node, and linked to the modeling node (sown as a dotted line). This link ensures that whenever the model is re-computed, the model nugget will be updated with the new results.








To see the model’s output, you can edit the model nugget (the output always depends on the models you run). First the summary tab confirms that the model target is the “project status” field and the inputs considered are the “client”, “contractors”, “technology category” and “team size” fields:








Next, the “Viewer” shows the model constructed tree showing the effects of the various inputs on the target. From this analysis, it would appear that the size of the team unquestionably affects the outcome of the project – and that to-date; the company is less effective as the size of the team increases.


















This example illustrates just a single example of leveraging predictive analytics to improve performance in everyday business solutions. SPSS makes it easy!


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Jim Miller

Mr. Miller is an IBM certified and accomplished Senior Project Leader and Application/System Architect-Developer with over 30 years of extensive applications and system design and development experience. His current role is National FPM Practice Leader. His experience includes BI, Web architecture & design, systems analysis, GUI design and testing, Database modeling and systems analysis, design, and development of Client/Server, Web and Mainframe applications and systems utilizing: Applix TM1 (including TM1 rules, TI, TM1Web and Planning Manager), dynaSight - ArcPlan, ASP, DHTML, XML, IIS, MS Visual Basic and VBA, Visual Studio, PERL, Websuite, MS SQL Server, ORACLE, SYBASE SQL Server, etc. His Responsibilities have included all aspects of Windows and SQL solution development and design including: analysis; GUI (and Web site) design; data modeling; table, screen/form and script development; SQL (and remote stored procedures and triggers) development and testing; test preparation and management and training of programming staff. Other experience includes development of ETL infrastructure such as data transfer automation between mainframe (DB2, Lawson, Great Plains, etc.) systems and client/server SQL server and Web based applications and integration of enterprise applications and data sources. In addition, Mr. Miller has acted as Internet Applications Development Manager responsible for the design, development, QA and delivery of multiple Web Sites including online trading applications, warehouse process control and scheduling systems and administrative and control applications. Mr. Miller also was responsible for the design, development and administration of a Web based financial reporting system for a 450 million dollar organization, reporting directly to the CFO and his executive team. Mr. Miller has also been responsible for managing and directing multiple resources in various management roles including project and team leader, lead developer and applications development director. Specialties Include: Cognos/TM1 Design and Development, Cognos Planning, IBM SPSS and Modeler, OLAP, Visual Basic, SQL Server, Forecasting and Planning; International Application Development, Business Intelligence, Project Development. IBM Certified Developer - Cognos TM1 (perfect score 100% on exam) IBM Certified Business Analyst - Cognos TM1

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