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Data Driven PMOs

PMOs cannot be focused on technology alone.  Just picking a software does not constitute a good PMO.

A PMO must include People, Processes, and Tools to operate as an integrated sustainable system.

However today, the three elements of people, process, and tools, are not enough.  A fourth element is coming into play which is Data Analytics.

Data analytics can help in the most fundamental aspects of project management.  These include lessons learned and Key Performance Indicators (KPIs).  Analytics will help in both.  Here are a four examples of how Data Analytics can help

1] Projects Lessons Learned: The Project Management Institute (PMI) identifies lessons learned from projects as a key input to future projects. Most companies are unable to make best use of lessons learned from previous projects for many reasons:

  • Data is not archived properly
  • Data is not accessible
  • No efforts are made to find patterns from the data using data analytics

2] Projects Success Criteria: It is possible today to identify “features” of a successful project, and ensure they are designed in every new project.  These features might vary by company, so it cannot be one size fits all.  Features can be certain project managers, certain planning steps, certain processes, certain clients or types of clients, and much more.

3] Predicting Project Risks: Companies can predict upcoming problems also using data analytics.  They can manage risk better, and deal with issues better by looking at data from the project itself and adding indicators based on predictive algorithms used on the project.  This does not have to be complicated and is doable with online available tools.

4] Feature Engineering: Another use of data engineering can be in determining what information must be gathered on projects to make best use of lessons learned and to be able to predict project performance for current and future projects.

This is new worldwide.  But already companies are doing this.  But as expected this kind of activity will be held very close to the chest.  Companies who figure their success factors will never share them with others.  So I am expecting and seeing many companies using these techniques but clients are asking their consultants not to advertise or bring this up in the open.  Other clients are insisting their consultants are only working with them alone to avoid any leak of findings to competitors.

There is a lot more that can be done with data engineering on projects.  I plan to write more as I see more applications with clients and worldwide.

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