How does your organization make project decisions?
The optimal answer for most project professionals is: based on the data. But the reality might be a different matter. While 73% of project professionals say data is crucial to project delivery, only a quarter of organizations currently describe themselves as data-driven.
Without access to high-quality project data, you’ll be forced to fall back on experience or intuition to make decisions. And unfortunately, when it comes to project planning, these are often poor substitutes.
Psychologists have shown that humans have an innate tendency to underestimate the time and resources needed to complete a project — even if they have undertaken similar projects in the past. This cognitive bias is known as the planning fallacy.
To avoid the pitfalls of the planning fallacy, you’ll need to build a truly data-driven foundation for your project decisions. And the first step is to assess the quality of your project data.
Assessing Your Project Data Quality
You want high-quality project data to shape your decision-making — but what does this look like in practice? A range of factors determine the quality of your project data. Let’s look at each of them in turn:
Relevance
The amount of data organizations have access to is growing exponentially — but this isn’t an unequivocally positive thing. The first step to assessing your project data quality is recognizing that more data is not always better.
To enable effective decision-making, your project data needs to be relevant. If project leaders need to wade through large quantities of data to find the answers they need, they won’t be able to react quickly to critical issues. In fact, 72 percent of business leaders say that the sheer volume of data available has led to decision paralysis.
Of course, there’s no general rule for what data is or isn’t relevant — you’ll need to consider the specifics of your project. One major factor to be aware of is your industry's compliance requirements. What may be unnecessary detail in the manufacturing industry may be essential to meet regulatory demands in aerospace or pharmaceuticals.
Standardization
Virtually every project will involve multiple teams, each generating their own data. This can include everything from progress reports to supplier contracts, purchase records, and resource utilization statistics.
You'll need access to all of this data to get a clear picture of your project status. After all, a partial view can result in you missing key details. But if your data processes aren’t fully standardized, you may find that teams:
- Record data in different formats
- Use conflicting definitions for key terms
- Calculate important metrics inconsistently
- Store data in different applications
The result? You look at progress reports from your engineering, development, and marketing teams to ensure everyone is aligned — but there’s no easy comparison. You need to prepare the data before analyzing it or go back to each team for clarification.
Either way, your ability to identify and respond to emerging problems is limited.
Timeliness
Effective project management means responding at pace to changing circumstances. If a particular task begins to slip behind schedule or a particular team is on track to overspend, you need to make adjustments as soon as possible.
But without access to real-time data, you’ll struggle to identify issues as they occur.
Imagine your product team is falling behind at the testing stage due to unforeseen complications — but they only provide timeline updates every two weeks. Before you can even begin to understand what’s caused the delays — and how they impact the rest of your project — you’ve already wasted vital time waiting for the information you need.
Ultimately, the longer the delay before fresh data arrives on your desk, the harder it’ll be to catch issues in time.
Accuracy
When people think of data quality issues, human error usually comes to mind — copy-pasting errors, missing fields, duplicated entries.
No doubt, all of these issues are important. The commonly cited error rate for manual data entry is 1%. This may sound small, but if you’re working with a dataset containing 10,000 fields, that means 100 are incorrect. That’s a substantial risk if you’re using this data to shape high-value decisions.
But slips of the finger aren’t the only accuracy issues you may struggle with. If you lack clearly defined best practices for recording data, errors can be introduced at a more fundamental level.
Let’s say, for example, you are monitoring your resource utilization rate. Based on the data, you should have more than enough staff hours for the coming quarter to hit your targets. It’s only midway through month two that you realize you were calculating based on the total available hours without accounting for annual leave — and now you’re on track to overrun.
Accessibility
You may have read this far and been happy to discover that your project data is up to standard — it’s relevant, fully standardized, real-time, and well-validated.
But all this is of little help if key decision-makers can’t access it when they need to.
For major project decisions, you’ll likely need sign-off from a range of stakeholders, including senior leaders. And they’ll all likely want to do their due diligence before committing to anything — including looking at the data behind the decision.
But suppose this involves them having to track down relevant reports or requesting access to the data they need from multiple platforms. In that case, getting their approval will be a complicated process. As you spend time chasing them, the decision you’re pushing for gets delayed — and the consequences can be significant.
How a PMO Can Improve Project Data Quality
As you can see, project data quality covers a range of factors. That means improving it will require a comprehensive approach. Which raises the question, who should take responsibility?
While your data expertise may sit with your IT team, they are likely not in a position to understand the specific demands of project management. By putting project data quality in the hands of a project management office (PMO), you can take a coordinated approach based on a deep understanding of what drives successful projects.
A PMO can improve your project data quality by:
- Implementing a filterable project management dashboard using a project management tool. This will provide a single source of truth to ensure your project data is fully consistent and make it easy for teams to surface the most relevant data.
- Defining key metrics and ensuring appropriate collection methods. With clearly defined data processes, your teams will be better placed to collect relevant and accurate data based on shared definitions and best practices.
- Implementing standardized processes for monitoring and validating project data. Data quality issues can emerge from the smallest of errors, making them hard to spot. Consistent processes for data validation can help you identify problems sooner and fix them more easily.
- Tracking data quality to identify consistent issues. Improving your data quality is not a one-and-done task. A PMO can monitor data quality both within a single project and across concurrent projects to ensure continuous improvement of your data processes.
