Select a Project Portfolio Management Tool That Uses Real World Data

What if you had the following conversation with an automobile salesperson?

Salesperson: "What color would you like?"

You: "Red"

Salesperson: "No, I need a number on a scale of 1 to 10."

You'd probably look at him as though he was a little crazy, yet many Project Portfolio Management applications do exactly this: they force you to modify your data to fit the application.

In some cases, this is impossible; in other cases, it will skew or bias the portfolio.

So you end up with project portfolio rankings that don't reflect the real-world and are difficult to understand. And you have unhappy stakeholders who are reluctant to provide data or trust the portfolio rankings.

And who can blame them?

Unfortunately, most Project Portfolio Management (PPM) tools are pretty inflexible in the types of data and data processing they use.

Meaningful Project Portfolio Management depends on obtaining realistic project data, usually from people in different departments such as finance, IT, R&D, marketing, etc.. Often this data is inexact or represents "best estimates."

Next, this data is used to rank projects in the portfolio in a way that reflects business strategy.

And finally, the project portfolio is presented to the stakeholders for evaluation and refinement.

For each one of these steps it is vital that the data be as accurate and "real world" as possible, otherwise the project ranking in the portfolio loses its significance.

If it is not, you end up with a Project Portfolio that is pseudo-quantitative, indefensible, and does not help you maximize your project portfolio.

To get the best results from a Project Portfolio Management tool, use an application that lets you put your data directly into its database.

Avoid applications that force you to fit your data into its model such as only allowing data on arbitrary straight-line scales such as "1 to 10." Find out how project data is collected and entered. Can you enter data in its raw form, including text data, or are you forced to modify it to fit the application?

Now imagine this conversation between a portfolio manager and a research scientist regarding the probability of success for a complex technical project:

Portfolio Manager: "What do you think is the probability of success for this project by the end of the year?"

Research Scientist: "Oh, I'd say somewhere between 50 and 75%."

Portfolio Manager: "No, I need an exact number."

Research Scientist: "I can't give you an exact number. It is impossible to estimate that with that kind of precision."

Portfolio Manager (exasperated): "Just give me a number!"

Research Scientist (frustrated): "Okay, uh, 68%."

Now they both walk away unsatisfied. The portfolio manager doesn't trust the number he was given and the research scientist doesn't trust that the portfolio is going to rank the projects based on realistic input. And they are both right.

There is always uncertainty associated with portfolio data. Look for applications that can process and display it.

For reliable, realistic, and comprehensible portfolio project rankings, choose an application with flexible data processing capabilities. For instance, find out how it handles data with inherent uncertainty, such as risk assessments.

Project Portfolio Management is about maximizing value even when your data has inherent uncertainty. Modern Project Portfolio Management is about managing probability, not single data points based on best guesses. Make sure that you select a tool that can do just that.

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