APM ShortGuides How to Use Data to Make Better Decisions

Collecting accurate data is essential for guiding effective decision‑making throughout a project’s lifecycle, especially when projects reach critical points where uncertainty is high and clarity is essential. This guide supports project professionals in understanding how to identify, design and implement the right data systems to ensure clarity, confidence and impact. It covers: • What “data” means within the context of projects • Why high‑quality, unbiased information underpins confident decision‑making • Common challenges project managers face when gathering and validating data • How to organise and design data processes to maximise insight and value By applying these principles, project managers can build trustworthy data foundations that strengthen governance, enhance overall project performance and strengthen trust at key decision points and enable project boards to make informed, evidence‑based decisions. Author: James Lea and Wayne Chang Author bio: James Lea is a Fellow of the Association for Project Management and of the British Computer Society. As a consulting project and programme manager and board-level consultant, he specialises in the application of AI, project science and data to build a world where every project and programme succeeds. This short guide captures his hard-won experience in bringing data to life, and generating transformational impact and value. Wayne Chang is an experienced project manager with a strong track record in digital transformation, business development and strategic consulting. He has worked across multinational organisations, start-ups and public services, delivering technology-enabled and digitally focused initiatives. His work centres on turning strategy into practical delivery, particularly where data insights, digital capability and cross-sector collaboration are critical. In this short guide, he draws on his hands-on experience of using data in real project environments to help project managers build stronger data foundations through simple, practical improvements rather than complex or costly solutions.