The Rise of the Data-Literate Project Manager
Building a data-driven team is now the norm with AI.
When project managers study in their field, they never expect to become “data-literate.”
But AI today is pushing the boundaries of what we need to learn as project managers.
This past week, I listened to a podcast with Marcus Glowasz, the author of “Leading Projects With Data.”
He stressed that project managers will be called on to be knowledgeable about the data they present and gather from their work.
We must become data-literate to use the data and translate what we see from charts and trends to stakeholders or project clients.
We must thread the needle and couple the digital (documents, charts, diagrams) and non-digital data (for example - emotional queues from team members, stakeholders, and clients).
Below are some suggestions that I took from the podcast on how we can become better stewards of data as project managers
Unlocking the power of your data treasure trove
You wouldn't leave something valuable like time, money (or even Bob in accounting) lying around without knowing exactly how you will use that resource in your project, correct?
Well, data is no different—it’s a wealth of hidden riches. Yet, many project managers either don’t know they’re sitting on a treasure or they don’t know how to mine it.
When you start viewing data as an asset, everything changes. It stops being something you have to manage and starts becoming something you need to leverage.
Data can help you predict project roadblocks, track performance, and improve team efficiency. Instead of relying on gut feelings or guesswork, you can make grounded decisions and have data to back them up. That’s the mind-shift of treating data like something valuable.
The more you regard your data as an asset, the more ROI you’ll get out of it. If you don’t take advantage of the data you already have at your fingertips, you’ll be stuck wondering why your projects feel like a game of chance.
Building trust through data transparency
Trust isn't given. It's earned.
When managing projects, trust can make or break your success. Luckily, you can use data to gain people's trust. When you consistently share data with your team and stakeholders, you show them that you’re not just winging it —you have the facts to back up your decisions. Numbers don’t lie (unless someone’s really bad at Excel).
You can use data to foster trust since it's like handing out key points of interest in your project’s journey. Instead of the team wondering if they are headed in the right direction, the data can show them the correct path. Transparency with your data means that everyone—from your team to the higher-ups—can track progress and see where things are going well and where they need adjustments. Having that data easily accessible avoids any sort of guesswork.
There's an upside to having reams of data - when people see the cold, hard facts, they’re much less likely to question your decisions. You’re no longer “just the project manager with some MS Project skills”—you’re the one with a solid database of research guiding the project.
From data deluge to actionable intelligence
Do you feel like you're drowning in data when working on an AI project (or any project nowadays)?
That’s because you probably are.
With any project, the problem isn’t having too little data. You need to determine what data actually will help the project move forward. It's like digging through a pile of junk mail and campaign ads to find the one bill that’s actually due.
However, not all data is created equal. As a project manager, your job is to sift through the noise and extract valuable insights. Disseminating the right data to the right people is crucial. If you give your stakeholders every piece of data imaginable, they’ll be overwhelmed. Give them only what’s relevant, and then everything will make sense since the data is on point.
The key is to focus on context. Before bombarding your team with numbers, ask yourself: “What decisions need to be made?” Tailor the data to those decisions. Be strategic with disseminating it—make sure it’s clear, concise, and actionable. That way, your team isn’t just looking at data; they’re using it to make meaningful progress.
Decisions backed by hard facts
Every project manager faces tough calls on a project.
Should we shift resources to meet a deadline?
Is this task really worth the time investment?
How can we get ahead of the schedule?
Instead of relying on intuition, data-driven decision-making puts facts front and center.
Data-driven decisions become less about personal preferences or subjective opinions and more about what the data indicates is best for the project. For example, data on resource allocation can show where bottlenecks are happening or which team members are overloaded. You aren't relying on a casual conversation over lunch with your team. You’ll know the exact reasons why things need to change—and you can course-correct very quickly.
In the end, using data-driven decision-making helps you stay objective. Explaining a tough decision to your stakeholders is easier when you have evidence backing it up. Everyone is more apt to agree when you say, "The numbers show this is the most efficient path," rather than, "I’m doing this since I just heard about this crazy idea over lunch with Lisa."
Nurturing a culture of data literacy
You can utilize data all day long, but if your team and stakeholders don’t see the value, it’s a loss.
You, as the project manager, need to reinforce a data-driven culture. Everyone on the team needs to understand how data is the lifeblood of your project—a tool for everyone.
Making data part of your everyday conversations is a way to build a methodology for working with the team. Encourage your team to bring data to meetings, use it to justify decisions, and even point out when something seems off in the data presented.
Think of it as creating a unique language that everyone speaks fluently. Once your team sees the power of data, they’ll start compiling and actively looking for trends. They won’t be waiting for you to bring the data to them to review.
When your team is data-driven, it will start self-correcting when it gets off course without you having to micromanage it. This shifts the mindset from “What do I think?” to “What does the data tell me?”
That’s when the real magic happens, and projects start to run like a well-oiled machine—with data guiding every decision.
Closing thoughts
Becoming data literate isn’t an option in a world where we will spearhead AI projects.
AI is built on data, and we need to aggressively hone our skills to help our teams advance in a rapidly changing world. Knowing how to read the data and making our teams data-driven will be a high priority for project managers in the future.
Those who can peer into the data and read the trends before anyone else will be valuable resources to any company.
All signs point to it—and you don’t need to see the data to confirm those trends.
AI-Driven Tools for PMs
GuideJar - Creates step-by-step guides using AI.
Bliro - is an AI assistant for meeting notes.
NotebookLM is a tool for understanding. When you upload your sources, it instantly becomes an expert, grounding its responses in your material with citations and relevant quotes.
AI News PMs Can Use
Google's list of 185 real-world gen AI use cases from leading organizations.
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Cool ChatGPT Prompt for PMs
Periodically Assess the Team’s AI Readiness
Can you help me design an AI skills audit survey that will assess the current AI literacy and skill levels of my team members? I need the survey to include the following parameters:
Basic AI Knowledge: Questions that gauge understanding of AI concepts and terminology.Technical Proficiency: Specific questions to assess proficiency with AI tools and platforms currently in use or planned for future projects.Application of AI in Daily Tasks: Questions determining how well team members can apply AI solutions to streamline their workflow and enhance decision-making.Problem-Solving with AI: Scenarios to evaluate the ability of team members to use AI in solving complex project-related problems.Ethical Considerations: Questions that check awareness and understanding of ethical issues and responsibilities in using AI.Learning and Development Needs: Questions identifying areas where team members feel they need more training or resources to effectively use AI.Feedback on AI Integration: Open-ended questions for team members to provide feedback on their experiences with AI in the project, including any obstacles they face.
Please also include guidelines on how often this survey should be administered.
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@chris, I've heard my entire professional life that "accounting is the language of business." Your post made me realize, "project leadership is the language of progress and growth."