Once upon a time, project teams were thirsty for data to inform their decision-making. There was very little information readily available about supply chain status, sustainability factors, risk identification, project controls, and many other elements within the project management sphere. Data was pulled, parsed, and analyzed manually, and it was a laborious and time-consuming process.
Today, many project teams struggle to keep up with the enormous volumes of data flowing into the business. Innovative technologies deliver more information than ever, and we can easily share data across systems both internally and externally. The more modern problem is paring down all that data into something actionable, as project professionals try to pluck useful insights from a nonstop firehose of information.
If you feel you’re wasting time sifting through enormous volumes of project information—or worse, worried you’re missing clues you know are there but just can’t see—it may be time to implement steps to deal with data overload.
Begin by defining what you want to achieve. One key step in gaining control over your project data is understanding what you want the information to do for you. Are you interested in cutting out wasteful or uncontrolled spending? If so, you’ll want to zero in on cost and consumption data. Is compliance front and center on this project? You might opt to put quality and related datasets in the spotlight. This approach helps you quickly understand where data will be most useful in driving good business decisions and which pieces of information are likely to give you the actionable insights you need to reach your goals.
As you assess your project data needs, be sure to set parameters that make sense for your use case. For example, if you want to maintain visibility into current supply chain constraints, data that’s years old likely won’t be of much help. Similarly, cost information derived from large projects that benefitted from economies of scale may not be of much value when executing a small and highly focused initiative. Consider which parameters you can set that will narrow the window for useful data without excluding information that’s meaningful for your current effort. You may choose to exclude data based on its age, source, level of completeness, or other relevant factors.
Evaluate the range of information sources available to you and confirm the data quality suits your needs. This is where taking a “less is more” approach can really pay off. Multiple data sources that routinely contain duplicate, incomplete, or obsolete records are often more work than they’re worth. On the other hand, curating your feeds down to just a few sources for a particular type of information may be more efficient while still serving your analytics needs. A deliberate preference for quality will help reduce the amount of time you need to spend comparing, reformatting, deduplicating, and otherwise cleaning your incoming data before it can be used.
Don’t forget to review your data needs regularly. The right information for one project won’t necessarily be the right information for the next project. Data gathering and analysis aren’t set-it-and-forget-it, so take a fresh look at the types of information you plan to use at the outset of each project. Unless you’re working on an initiative that’s extremely short in duration, it’s also prudent to revisit your data strategy a few times throughout the project lifecycle. As the effort moves through different stages, you’ll likely find that some datasets age out to become less relevant, while others grow in value. Drive optimal results with a flexible approach to data that adjusts based on your needs in the moment.
PMAlliance, Inc uses a team of highly experienced and certified professionals to provide project management consulting, project management training and project portfolio management.
