Data-Driven vs. Data-Informed: A Comprehensive Guide

You're collecting more data than ever, but you find it difficult to turn that information into clear decisions. And you're not alone: according to our 2025 Marketing Data Report, 56% of marketers say they don’t have enough time to properly analyze the data they collect.

As tools like AI, improved databases, and advanced analysis methods become more available, there are new ways to strengthen your decision-making processes.

This article explains the differences between data-driven and data-informed approaches, highlights the strengths and pitfalls of each, clears up common misconceptions, and outlines practical steps to build a more informed, effective use of business data.

Understanding data-driven decision-making

Data-driven decision-making happens when organizations use accurate and reliable data to drive business decisions. Companies that use this methodology set clear business goals and use data to decide on strategy, as opposed to finding the data that supports strategies conceived through intuition or best guesses.

Advantages and disadvantages of data-driven decision-making

With the promise of accurate data that drives advanced analysis and faster decision-making, a data-driven approach should be the standard. But without clear goals, careful planning, and a thorough understanding of the company’s data, such a process won’t succeed.

Consider how your company could benefit from—or fall victim to—a data-driven approach.

Examples of data-driven approaches

Data-driven decisions are especially useful in AI-ready contexts where pattern recognition and fast decisions lead to improved business outcomes.

DBS, a leading international bank, used analytics to detect shell companies in money-laundering schemes, and rolled out a data-driven decision-making approach to the wider business by training 16,000 employees as “data heroes.” And AmericanExpress implemented instantaneous fraud detection, using AI models to analyze the markers of fraud at each transaction to identify and quickly act upon potentially fraudulent payments.

Data-driven strategy has a quieter and deeper impact when used for the healthcare industry. Intermountain Healthcare wanted to increase equity in its care models. Its challenge was to blend accurate and disaggregated data across race, ethnicity, income, and sex, among other equity markers to understand how inequity shows up in the current care system. That information provided the group with dashboards that can be used to understand whether upstream factors or direct patient care (or both) contribute to unequal health outcomes.

Common misconceptions of data-driven decision-making

On its way to buzzword status, data-driven decision-making has collected some interesting misconceptions that can deter companies and individuals from even attempting to wrangle their data into usable forms:

• Data-driven decision-making doesn’t require creative thinking: Any analysis of data will require creative thinking to connect the dots. While this approach does depend heavily on the story the data tells, data selection and combination techniques shape that story. The creativity of the approach is often hidden in the structure.
• Data sources will work without maintenance: Every team is looking for the ultimate set-it-and-forget-it (until next reporting cycle) option. Because we use so many zero, first, second, and third-party data systems together, the reporting and connections require regular upgrades and maintenance.
• Open internal accessibility to data is dangerous: Some companies want to reserve decision-making to leadership, which slows the team down. Personal and sensitive information should remain protected, but the democratization of business data only leads to more creative outcomes.
• Teams don't really need data to collaborate: People need help knowing who to contact, when to reach out for help, and what team goals they can contribute to. Find ways to publish this information widely, and schedule times to collaborate between teams.

Careful planning, collaboration, training, and follow-up will improve your chances of success.

Understanding data-informed decision-making

Data-informed decision-making uses data along with other factors like employee expertise, market research, and company priorities to drive decisions. While data-driven decisions rely on accurate data to shape strategy, data-informed decisions add context and experience to hard metrics.

Companies that have concerns around data ethics in marketing, as well as those who depend upon the deep expertise and business intuition of their employees for creative solutions, may adopt a data-informed approach to decisions. This method requires contextual analysis and interpretation of data that is impossible for today’s technology.

Advantages and disadvantages of data-informed decision-making

By adding context to data, a data-informed decision-making methodology feels more human and intuitive, which creative-leaning teams like marketing, product, and HR may feel more comfortable with.

But humans are imperfect, so data-informed decisions require companies to think ahead and place guardrails and processes to keep teams on track.

Depending on the situation’s mix of intuition and metrics, blind spots may emerge for the company and negatively impact future decisions. Companies that can spot trends early and pivot quickly without corporate deliberation or red tape will benefit from a data-informed approach’s adaptability.

Examples of data-informed approaches

Due to the flexibility of the approach, most marketers will use data-informed strategies, as they allow for more subjective or individualized inputs to provide context for decisions, like budget and customer needs.

Starbucks uses customer data from its app to improve menu options, provide individualized recommendations, grow the brand, and make major company decisions. Customer data informed the decision to add sugar-free tea options when developing its grocery K-cup line, which resulted in growth beyond the coffee market.

The role of human judgement in data-informed decision-making

Data-informed decisions often happen in creative industries where human insights must guide strategy. While these insights can result in great leaps in innovation, check with a diverse group of stakeholders to uncover potential biases that may draw the team off-track.

You should be continually asking questions about your data. Whenever your dashboard or report is complete, read over it and say, "Okay, what are some other questions I could ask?" It's continually asking questions and making sure that you're doing the right analysis and the right measurement for your space and for your level—and that you're not going overboard.

For example, content marketers, bloggers, and digital marketers should be familiar with keyword search intent, that is, understanding what a person actually wants to know when they perform a web search.

An article that targets the keyword “what is a widget” speaks to a different audience than one written to the keyword “how to buy widgets.” Understanding the difference between those phrases—and which article the team should spend marketing dollars on—requires industry experience and marketing foresight that standard keyword research tools don’t have.

Comparing data-driven vs data-informed approaches

Most marketing departments aren’t lucky enough to find a perfect marketing stack from the company’s inception. Rather, they cobble together the best tools for the immediate need within the budget allowed. But this shouldn’t stop marketers from finding a better solution when they realize their tools no longer talk to one another or allow for analysis—if they ever did in the first place.

When you try to get a real sense of your marketing data through data blending, breaking down silos, and taking rational approaches to analysis, you will get deeper insights from your marketing data. By implementing tech solutions that help you make sense of data, your decision-making (whether data-driven or data-informed) gets easier.

Key differences between the two

Data-driven decision-making can feel rigid because it relies on the metrics available, meaning the data that the team has identified as reliable and useful. It requires strict data stewardship and governance to identify, blend, and support the data sources needed to collect and aggregate the data. Data-driven decisions can feel more reliable because, ideally, there has been consensus around the data deemed accurate.

Data-informed decision-making gives more room for qualitative information, which can expand context and include stakeholder input. However, this approach may feel too flexible for stakeholders or executives who have less insight into the contextual elements guiding decisions.

When to use each approach

Data-driven decision-making works best for companies with mature data teams that work primarily with hard numbers like finance, or that work in highly regulated industries with strict guidance on how and when data can be used. Those in industries with lots of historical data to draw from may also find data-driven decision-making gives them a competitive advantage in forecasting and decision-making.

Being data-informed requires analysts to look beyond the data. This approach works best for growing companies, those in emerging markets, or those working in volatile industries. These companies will want to pivot quickly according to changing (or previously unknown) market needs.

Data-informed decisions are ones where you are layering in your expertise. At the end of the day, some of the best data scientist marketers are psychologists, not simply statisticians.

Implementing a data-informed strategy

Moving from your previous analysis tactics to a data-informed strategy takes planning to understand the company’s data landscape, what data sources contain reliable and usable data, and which stakeholders across the company will support the data initiative. Proper attention to infrastructure and education toward data literacy will lay a foundation for strategic success.

So if you're pulling and you're gathering as much as you possibly can because you want to be a data hoarder, you're really doing yourself a disservice. You're making the data either too granular for you to capably analyze, or you're getting data in such a fashion that it can't be aggregated correctly.

For example, if your company's goal is to increase revenue via website purchases, the project team will need to understand the data from the website, CRM, web analytics, digital marketing, and any other tool that contributes to the ecommerce cycle. The team will then blend the data from those sources according to best practices for ecommerce reporting. From there, ecommerce revenue stakeholders must be taught how to access, read, draw inferences from, and act upon the reports—as well as their own expertise.

To help guide your implementation, we’ve broken the process into four steps.

1. Building data infrastructure

Start by mapping the marketing technology stack. This should include digital marketing, email, and social media inputs, as well as any custom data imports you may need, like out-of-home (OOH) marketing results. Alongside this map, log vital information including:

• Stakeholders: Which team members and roles use the tools and who can answer questions.
• Format: The way the tool stores data. For example, is a customer name stored as [first][last]; [first],[last]; or [last],[first]?
• API: Capture important details about any API calls being made, as well as their formats (e.g., JSON, REST)
• Vital data: Data fields that must be included or excluded, such as "campaign name" or "click-through rate."

Regulatory requirements: What federal or regional regulations might impact the usage of this data?

Once the metadata for each source is identified, your technology team can begin organizing the technology stack for the purposes of reporting. Often, this process will include a data warehouse, data governance plans, and other cross-departmental planning, so identify IT and data stakeholders early.

2. Promoting data literacy

Like the introduction of any new technology, you need to train employees to make strategic decisions from your data. Remind stakeholders and employees that learning the system will help them make faster, more informed decisions.

While understanding how to analyze the dashboards available to them, relevant employees should learn to access reports; perform basic functions like combine, compare, and drill down into data, and to spot inconsistencies or errors that may skew the data.

The bulk of the data literacy training should come from stakeholders within the department who are invested in the success of the data-informed decision-making initiative. Data analysts and IT don’t necessarily understand marketing’s goals, so having a department go-to for questions will alleviate a lot of stress on tech teams to understand the underlying context.

Ensure data literacy programs effectively support self-service reporting, and rely on data teams for maintenance and new projects. Use internal training and process documentation to support data literacy efforts.

3. Combining data with context

Data-informed decisions rely on contextual and qualitative information to add color and depth to quantitative data from marketing systems. To effectively balance the team's reliance on structured and unstructured data, centralize marketing data, and build reporting that focuses on the ultimate strategic goal, not just the immediate wins.

For instance, if your goal is to drive shoe sales among 18-25-year-old women, your sales data may report that slide sandals sell well within this segment. But checking this data against current 18-25-year-old influencer trends and the current social media hashtag and linking data may show gladiator sandals trending among the target market.

By pulling sales numbers alongside unstructured trend data, the marketing team can continue to push slides while also saving some marketing resources for gladiator sandal marketing to capitalize on the trend.

4. Encouraging cross-functional collaboration

In the case of the shoe company above, insights about trends and strategy will most often reveal themselves through collaboration. Sales, marketing, social media, and data may all look at the same dashboard and come to different conclusions based on their own context.

Collaboration makes use of varying levels of expertise, fresh perspectives on the information, and diverse ways of viewing the reports to positive ends. Companies that succeed in data integration that breaks down departmental silos can continue to succeed in data-informed decision-making by collaborating across department lines to better understand the story the data tells.

Final insights on data-driven vs. data-informed decision-making

Both data-driven and data-informed decision-making promise to help teams make faster, more informed decisions. Whether your company or department relies on one over the other comes down to your strategic vision and approach to data.

Companies that value speed over accuracy may opt for a data-driven decision approach, as they prefer to manage by exception. Those looking for creative and innovative solutions within rapidly changing conditions might want a data-informed approach.

For either approach, data accuracy and reliability directly influence the success of the project. Start your project with a deep understanding of the technology stack, company goals, and needs of the department. From there, consider which strategy will best enhance your decision-making processes.