“Data-driven” and “data-informed” are both contenders in the great game of buzzword bingo. You’ll hear them tossed around in strategy sessions, tool demos, and board meetings alike. But behind the jargon is a real and important distinction that affects how your organization makes decisions, solves problems, and plans for the future.
So what do these terms actually mean - and which one should you be doing?
Spoiler: probably both.
What Do the Terms Mean?
Data-driven decision-making means letting the data take the wheel. You’re putting numbers in charge: if the data says option A is better than option B, you pick A.
Data-informed is more nuanced. It means data is a critical input, but not the only input. Decisions take data into account, but also incorporate institutional context, stakeholder values, lived experience and other sources of knowledge.
Both approaches rely on data. The difference is how much room you leave for judgment, interpretation, and other kinds of wisdom.
Strengths and Tradeoffs
Being data-driven has its upsides. It:
Helps reduce bias by anchoring choices in evidence.
Is ideal for high-volume, repetitive processes - and can enable automation for those.
Makes outcomes easier to measure and justify.
But it can also be blind. It ignores things the data doesn’t capture and can skew outcomes if the data are incomplete. It can downplay outliers, context, or longer-term impacts that are harder to quantify.
Being data-informed brings flexibility. It:
Recognizes that numbers don’t always tell the whole story.
Allows for decisions that reflect mission, ethics, or equity.
Can provide direction when the “right” answer isn’t obvious.
There are risks. Data might be cherry-picked to support the outcome you already want. Or you may flounder in a sea of ambiguity when there is no clear data signal, or no one agrees on what the data mean.
Which Approach Fits When?
Think of it this way: being data-driven is a great fit when precision matters and the stakes are narrow. You’re optimizing, not philosophizing.
Data-driven makes sense for:
A/B testing subject lines for your newsletter. Choose the version that leads to the highest open or click-through rate.
Optimizing staff schedules to align with customer demand. Adjust staffing levels or shift timing to match when clients’ needs peak.
Reordering supplies based on usage patterns. Set reorder points based on historical consumption to prevent shortages or excess.
You’re choosing the best option quickly, reliably, and with minimal guesswork.
Data-informed shines when:
Weighing the community impact of a new program. Consider quantitative outcomes alongside qualitative feedback from stakeholders and local context.
Choosing between expanding products or services, or investing in infrastructure. Balance financial data with operational realities, staff capacity, and future needs.
Prioritizing your to-do list for the week. Data from a project management tool might suggest what is overdue or pending, but your knowledge of other upcoming deadlines, relationships or team morale helps direct your attention.
These decisions are multi-dimensional. They affect people, culture, and future plans. They may not have a clear “winner” based on numbers alone.
Why This Matters More Than Ever: The Role of AI
As AI becomes a more common tool with applications across all facets of an organization including decision-making, the tension between data-driven and data-informed thinking is only growing.
Right now, most AI systems are fundamentally data-driven: they surface patterns from enormous datasets and optimize, predict or automate based on what’s been seen before.
But they don’t know your community. They don’t understand your mission. And they do not consider nuances, ethics, or historical context, at least not without help.
Human judgment must come in. Just because an algorithm recommends something doesn’t mean it’s the right choice. AI can be a powerful assistant, but you need to be the boss. Consider AI one more form of data input; if all decisions are driven by outputs from an AI tool, that’s still data-driven. If AI outcomes are one input to your decision-making process, you are data-informed. Only you can decide how much weight that data may carry in your decision-making process.
Tips for Finding the Right Balance
You don’t have to pick a side. The best approach is usually a mix. Let structure guide you, but inform it with reflection and expertise.
Here are a few ways to keep your decisions smart, flexible, and human-centered:
Use data as a flashlight, not a hammer.
Let it illuminate options, risks, and opportunities. Don’t use it to shut down every other perspective.Invite multiple interpretations.
The same metric can mean different things to different teams. Have that conversation and feed what you have learned into your decision-making process.Ask: “What is this data not showing us?”
Be intentional about surfacing missing data, qualitative inputs and equity impacts. These may not be measured, or they may fall through the cracks.Don’t outsource judgment.
Especially when using AI or automation, set clear guidelines for when human review or override is needed.Teach data literacy, not just data use.
Help your team learn to ask good questions and interrogate what they see, not just pull reports.
Don’t Just Follow Data - Partner With It
You don’t need to choose between being purely analytical (data-driven) and being thoughtful (data-informed). The strongest organizations and professionals are both.
Let data shape your decisions, but not dictate them. If you are reading this, it’s because your work isn’t all about metrics. It’s about people, purpose, and impact. Because not everything that matters fits in a spreadsheet.