When data from individual projects is rolled up to the portfolio level, any issues with the integrity of that information—its age, its completeness, or its accuracy—are magnified. Project data built on subjective metrics is also an area that can generate questionable portfolio insights. People who pad their budget forecasts because they’re worried about contentious financial negotiations or team members who are too generous with their progress estimates are all common sources of subjective project data.
Subjective data has long been a staple for many project teams, but when this kind of potentially biased or unsupported information is pulled from multiple initiatives and combined in the portfolio reporting process, executives may not be getting a complete and correct picture of the portfolio’s status.
If you’re still using subjective project data to generate portfolio-level reports, consider where you may be introducing risks into your leadership team’s data analysis and decision-making activities.
1 – Your data might be incomplete or inaccurate
Subjective data doesn’t provide a good basis for deeper analysis and you may never know if you’re working with all the facts or not. Details could be missing because somewhere along the way someone assumed those data points weren’t important enough to include. However, your executive group may view those same elements as necessary factors in making sound business decisions.
2 – It’s not always possible to reconcile subjective data
If a metric appears out of sync for any reason, you likely won’t be able to determine if (and why) it’s erroneous. Do those contingency figures seem unusually high for this particular type of project? Can your team really complete a key sequence of tasks in less time than ever before? The use of subjective data largely eliminates your ability to compare current and past information to ensure your portfolio data is accurate and realistic.
3 – “Adjusting” your data will eventually backfire
Some people are optimistic, others are more conservative. It’s not uncommon to tweak your analysis of subjective data if you know it contains only one perspective, such as a PM who consistently assumes the worst when developing budget forecasts. However, trying to normalize subjective data across multiple perspectives will result in portfolio-level data that’s even less credible than the individual data points used to generate it. You could end up with data rollups that are so far from reality that they’re useless, leading the executive team to lose confidence in any information they receive.
4 – Your data doesn’t enable you to identify trends over time
When your portfolio information includes subjective measurements, it’s difficult—and sometimes impossible—to know how things are changing. Is your on-time delivery metric improving? Has performance gone up or down? Are ROI figures getting better or remaining the same? Subjective data removes your ability to clearly identify key trends and to accurately track your portfolio’s results over time.
5 – Your data can’t deliver actionable insight for improvement efforts
Anything that influences your project data, from stakeholders trying to make themselves look efficient to forecasts that are based on hunches and instinct, could potentially cloud the picture when it comes to understanding where portfolio and financial performance could be improved. Gauging how well your improvement strategy is faring will be similarly muddled. You simply can’t know if changes to your processes or workflows are positively affecting performance or if improvements stemmed from some lucky confluence of other factors.
Impartial project data is a vital element in building and maintaining a healthy portfolio. If you haven’t already started replacing subjective sources of information with objective data, it’s something your organization should prioritize to ensure good portfolio performance.