Three tips to help you use project data more effectively

Data is at the heart of successful project and programme management. Data informs decisions, highlights risks and keeps stakeholders aligned. Yet many projects experience challenges turning raw data into meaningful and trusted insights. APM’s upcoming short guide Improving Data Accuracy for Better Decision Making offers practical advice to help you get the most from your project data.

Here’s a preview of three tips from the guide – each of which is designed to improve accuracy, clarity and impact.

Tip 1: Address cognitive bias to build trust in your data

Even the most experienced professional can suffer from cognitive biases. Confirmation bias leads us to favour data that supports our existing beliefs. Optimism bias can lead us to underestimate tasks or overstate progress. Recency bias causes teams to overreact to the latest updates, ignoring long-term trends. The result? Decisions based on incomplete or biased information.

To counteract cognitive biases, try these simple but powerful strategies:

• Seek evidence that disproves your view: Actively look for data that challenges your assumptions. Ask: what would tell us this project isn’t on track?
• Benchmark: Compare current progress with past projects. If similar initiatives faced delays, factor that into your forecasts.
• Use structured reviews: Implement ‘pre-mortems’ – imagining what could go wrong before it happens – to identify areas for attention through a proactive approach to risk.

By acknowledging and addressing bias, you create a culture of objectivity. Stakeholders trust data that has been rigorously tested, not just cherry-picked to fit a narrative.

Tip 2: Standardise your data for consistency and clarity

Project data often lives in silos, such as spreadsheets, emails, messages, project management tools and more. Without standardisation, teams waste valuable time reconciling conflicting information, and errors slip through. The guide highlights common pain points when it comes to too many sources with too little integration:

• Inconsistent data formats (e.g. mixed date styles like DD/MM v MM/DD).
• undocumented processes, where only a few people know how data is collected or updated, reducing consistency.
• Manual input errors, which compound as data moves through pipelines.

The guide advocates for simple standardisation to improve reliability:

• Use templates and automated feeds: Replace ad hoc spreadsheets with shared templates and automated data feeds (e.g. APIs or integrations).
• Define data owners: Assign clear responsibility for each dataset. Owners ensure accuracy, update records and maintain version control.
• Adopt a data standard: Align with frameworks like the UK Government Project Delivery’s programme and project data standard to ensure consistency across projects.

Want a pro tip? Start small. Pick one critical dataset (e.g. risk logs or budgets) and standardise its collection and storage. Measure the impact, then expand to other areas.

Tip 3: Model data to tell a clear story

Are you drowning in data but starved of insights? The problem is data overload without insight. Many project managers collect reams of information but struggle to present it in a way that drives action. Dashboards cluttered with irrelevant metrics or overly complex charts cause confusion, rather than providing clarity.

To fix it, try purposeful modelling and visualisation:

• Focus on key metrics: Identify three to five metrics that truly matter to your project’s success (e.g. forecast completion date, budget variance, milestone adherence, risk exposure).
• Use entity relationship diagrams (ERDs): These powerful diagrams show how data connects, helping teams see the big picture. For example, an ERD might link project tasks to dependencies, resources and risks.
• Make it actionable: Every chart or table should answer the question: what should we do next? If it doesn’t, simplify or remove it.

Why do these tips matter now?

In an era of artificial intelligence and automation, data quality is more critical than ever. Poor data leads to:

• Wasted time fixing errors instead of solving problems.
• Eroded trust when stakeholders question the numbers.
• Missed opportunities to spot risks or optimise performance early.
  What’s next?

These three tips are just the beginning. The full guide explores:

• Data modelling techniques to create a ‘single source of truth’.
• Continuous improvement strategies to refine your data collection and analysis, and a Project Data Checklist to help you embed these techniques.
• Guidance on practical actions to put these ideas into practice.

James Lea and Wayne Chang are co-authors of the upcoming APM Short Guide Improving Data Accuracy for Better Decision Making 

 

You may also be interested in:

• What is project data analytics?
• Join the APM AI and Data Analytics Interest Network
• 5 ways to make the most of project data