Big data in marketing: what is it and why does it matter?

Big data and analytics are not new concepts, yet they remain an obstacle for many marketing teams. If your success depends on proving your work’s value, sifting through piles of data can feel overwhelming.

The 2025 Marketing Data Report shows just how much pressure marketers face:

• 230% increase in marketing data usage since 2020
• 26% say finding relevant insights is their main challenge
• 56% don’t have time to analyze data thoroughly

The good news? Working with big data can be simpler than you think, provided you have the proper strategies and tools in place. Let’s go beyond vanity metrics and uncover clearer, more effective marketing insights.

Understanding big data in marketing

What is big data? Big data refers to datasets so extensive or complex that traditional data processing tools or strategies can't easily handle them. You can think of big data through the lens of the 3Vs:

• Volume: The sheer amount of data generated and stored.
• Velocity: The speed at which data is generated and processed.
• Variety: The diverse types and sources of data collected, including structured, unstructured, and semi-structured formats.

You’re probably already familiar with the intersection of big data and marketing, and you might not even know it. Whether you’re a marketing veteran or the new kid on the block, odds are very high that you interact with it daily.

Think of the search results you see on page one, the recommended music on your commute, or the shows that top your streaming app as soon as you log in. These features rely on large datasets, collected and analyzed to make your experience more relevant.

Marketing teams also leverage big data. Every online interaction—like browsing an online store or clicking an ad—generates a trail of information.

In many ways, the game is the same as it ever was: identify what works, highlight what doesn’t, and determine what needs some adjustments. Better yet, anticipate future opportunities and claim the early bird’s worm. What’s changed are the data sources, the scale (by orders of magnitude), and the tools available and/or necessary to process all the raw data.

And to be clear, no one has perfected this game. There will always be external factors, such as viral moments, changing consumer preferences, or unpredictable events, that can disrupt your best-laid plans. Still, without monitoring the data you collect, you can’t spot these shifts early or prepare for them effectively. Big data provides the visibility you need to adapt quickly and keep your marketing on track.

Types and applications of big data in marketing

Before proceeding, we must draw some distinctions between categories of data. We’re going to break this down along three axes:

• Source
• Format
• Specificity

Source

The data source, broadly speaking, refers to the origin of the data. Those groups are 1) external, 2) internal, and 3) transactional.

External data refers to information gathered from outside your company, such as competitor research, industry reports, economic indicators, market trends, or publicly available data. Essentially, external data provides context about your broader business and competitive environment.

On the other hand, customer-related data is categorized as zero-party and first-party data:

• Zero-party data refers to information that customers willingly share, such as their preferences, interests, or intentions (e.g., survey responses or quiz results).
• First-party data is information collected directly from customers through their interactions with your business, such as website visits, purchase history, customer service calls, and product reviews.

Competitor data falls clearly within the external data category. Competitor insights typically involve observing and analyzing external-facing activities such as their sales figures, marketing campaigns, buyer trends, and overall performance within the marketplace.

Internal data refers to information that originates and resides entirely within your organization. This type of data typically includes operational details like internal budgets, departmental costs, resource allocations, internal performance metrics, and project statuses. Essentially, internal data refers to metrics and insights generated and managed within your company, independent of external interactions or customer input.

Format

Structured data refers to data that is neatly organized, categorized, and clearly labeled. It is typically numerical or textual and fits seamlessly into spreadsheets, databases, or CRM tools. Marketing examples include customer contact lists, purchase history tables, or lead-generation forms, each piece of information clearly identified and ready for immediate analysis.

Unstructured data, on the other hand, is information that lacks a predefined format or clear categorization. This data often includes rich media types such as video, audio, social media posts, or lengthy blocks of text like product reviews or blog comments. In marketing, examples include customer sentiment expressed through tweets, recorded customer service calls, or engagement data from videos.

Here's a quick analogy: structured data is the organized layout of a grocery store, while unstructured data is a fridge full of opaque and unmarked leftover containers.

Specificity

The third characteristic to consider is the specificity of data, that is, whether the information collected is quantitative or qualitative.

• Quantitative data provides numerical measurements, clearly showing what happened. Examples include conversion rates, sales figures, page views, or customer ratings on a numeric scale (e.g., rating satisfaction on a scale of 1 to 5).
• Qualitative data offers more profound insights into why something happened. This includes customer feedback, detailed reviews, focus-group responses, or open-ended survey answers. For instance, responses to questions like "What did you like about the product?" give marketers valuable context behind the numbers.

Together, quantitative and qualitative data provide a comprehensive view, combining precise measurement with a deeper understanding of consumer behavior.

Mix and match

You’ll likely find trends in data characteristics, with some sources leaning toward specific formats or levels of specificity, and vice versa. For example, financial data is almost exclusively structured and quantitative in nature. However, overall, you’re likely to encounter a variety, with some data proving more actionable or more straightforward to use than others.

It can be tempting to favor structured and quantitative data over other types of data. But there are often valuable insights in the less organized data, if you’re willing to sift through it a bit. And just because you’re measuring something in nice round numbers doesn’t mean it means anything (e.g., LinkedIn profile views is an exact, but largely useless, figure).

This begs the obvious question: “How do I make sure I’m getting the most out of my data?”

Leveraging big data for strategic advantage

Data doesn't exist in a vacuum, and examining it in isolation rarely leads to meaningful insights. Simply having a large volume of data doesn't guarantee useful results. To truly benefit from big data, marketers first need clarity on why they're collecting it, specifically, what business questions they're trying to answer.

Many marketing teams end up overwhelmed by data because they don’t clearly define the purpose or metrics that matter, which is not necessarily their fault. It's like being a doctor seeing a new patient: you don't measure everything, you focus specifically on the indicators relevant to the diagnosis. For marketers, this means first agreeing on clear KPIs and benchmarks, ensuring that each data point collected serves a clear strategic purpose.

Additionally, while data is powerful, it isn't inherently insightful. You need human expertise to interpret it and place it within context. Marketing data without an expert’s perspective is just raw information.

In fact, according to our 2025 Marketing Data Report, marketers have grown more comfortable interpreting and leveraging data effectively. The survey shows that only 14% of marketers feel limited by their data expertise.

Additionally, 70% describe their marketing technology stack as somewhat mature, meaning they have the right tools, even if they're not yet fully integrated. Marketers increasingly recognize that the goal isn't perfection; it's actionable insight. To effectively leverage big data strategically, keep these guidelines in mind:

• Aim for data-informed decisions (not just data-driven): Instead of blindly following data, use your expertise to interpret what the data is telling you. Identify which insights truly matter before diving into analysis.
• Right-size your data: More data isn't always better. Collect only as much data as your team can meaningfully analyze and use effectively.
• Clearly define metrics and KPIs: Ensure your entire team agrees on what's being measured and why. Clearly defined goals and metrics reduce confusion and prevent misinterpretation.
• Accept imperfection: Perfect data doesn’t exist. Acknowledge limitations, focus on meaningful trends, and make informed decisions rather than seeking unattainable accuracy.

Even messy data can be managed with AI

Let’s wrap up with a look at how the right tools can simplify even the most chaotic data challenges. After all, big data means big workloads, unless you have a smarter way to cut them down to size.

Unstructured and qualitative data can be incredibly valuable, but from an operational perspective, it often feels almost illegible at first glance. When it comes to managing manual sorting, which is labor-intensive and time-consuming, manual parsing usually restricts you to very small data sets. Anything larger quickly becomes unmanageable.

Specialized solutions can automate the sorting of images, video, audio, or massive text datasets. And thanks to advancements in machine learning, these tools are now far more accessible and affordable than they were just five or ten years ago.

Machine learning and LLM algorithms have finally closed the gap where traditional computing struggled: making sense of unstructured information.

Unlike other controversial uses of AI in business, there’s little debate here—few humans are lining up for the opportunity to manually sort through 10,000 "might be a cat" images.

Beyond messy data, AI can also easily analyze structured datasets at scale. Visualizing and interpreting insights becomes far simpler, especially since visualization tools have been part of the marketer’s toolbox for years.

In short, AI doesn’t just help you manage big, messy datasets. It helps you transform them into actionable insights faster—and with far less human effort.

Overcoming challenges in big data marketing

Big data is not all cupcakes and rainbows. There are some real pitfalls, and a fair number of them. And they do, in all fairness, risk derailing the entire effort if not handled appropriately.

Some of the most common and impactful challenges you might face include:

• Data privacy concerns, including GRC considerations.
• Ethical data handling, since even “legal” practices can still lead to PR disasters and social harm.
• Sustainable data management is necessary, as databases rarely, if ever, become smaller or less complex over time.
• Resource constraints, from budget limits to storage limits.
• Getting everyone on the same page, e.g., breaking down silos, encouraging adoption, and implementation.

Obviously, this could be an entire B2B data convention keynote on its own. But you’re busy people, so let’s see if we can’t pare this down to some more concise pieces of advice:

• Fight fire with fire: Issues like data quality, governance, and even privacy compliance can be much easier to address with the right tools, and there are indeed tools for aggregating, scrubbing, standardizing, and merging data sets; it will likely take some setup and initial investment, but it’s usually a one-and-done implementation, and then the machine does the rest.
• Set up an ethics committee: It’s easy to forget, but all of this data originates from people. And even if you’re taking the necessary precautions to comply with privacy mandates, the data can still get you into trouble if you treat it as socially inert. Data, especially demographic data, has biases baked in, and unless you have humans reviewing these things at some point, the analytics will just treat it as gospel.
• Lead by example: It’s hard to elicit buy-in from stakeholders if the cook isn’t eating their own meals, so to speak. If they see leadership using the tools and systems, proving their value and demonstrating their worth, they’ll be more amenable to changes in how things are done.

Next steps for understanding big data

Big data can dramatically transform your marketing, but only if you're strategic about what you measure, why you measure it, and how you analyze it. Remember that data itself is neutral. It’s the human expertise, clarity of purpose, and proper tools that make data truly valuable.

Instead of getting lost in excessive metrics, focus on becoming data-informed: clearly define your objectives, right-size your datasets, and trust your marketing team’s expertise to interpret insights within context. Embrace imperfection, leveraging AI tools to manage both structured and unstructured data efficiently.

Ultimately, mastering big data in marketing isn't about gathering more data. It's about turning the right data into meaningful insights, driving more thoughtful decisions, and staying agile in an ever-changing landscape.