4 Common Data Collection Tools You May Be Unaware Of

If you're old enough, you may remember a time when your personal data equated to a few photographs and some Word documents stored on your computer. Skip forward a couple of decades, and the world we live in is unrecognizable, at least in data terms. 

Today, data is everywhere. The sheer scale of what humanity produces each day is staggering. Recent estimates put the global annual daily production of data at over 402 million terabytes. For some context, that's the equivalent of roughly 49 gigabytes of data per person per day, if we assume a world population of around 8.2 billion. All this data needs to be collected, and some of the tools that do this are things we're all familiar with. Social media platforms and online retail stores are good examples — Facebook generated about 4,000 TB of data each day in 2023, which could be concerning given it was involved in one of the worst data breaches in internet history. Every tweet, every email, every WhatsApp message, and every web search adds to this, and can be considered a data collection tool. 

While these are fairly obvious sources, many other data collection tools are less well known, which is why you should keep your work and personal data safe. Here we look at five of the common data collection tools that have slipped under the radar. 

Some uses of data are immediately obvious. For instance, if you do an internet search for a Samsung smart TV, you will likely be bombarded with ads about TVs on all your feeds. This doesn't need much thinking about — so-and-so is on the market for a new TV, let's show them what they want. However, some tools go deeper than that; they collect the data from within the data to reveal what's really happening. In fact, even the U.S. military is testing new ways to handle data management. 

Most commonly used in business, process mining tools extract information from event logs and audit trails generated within business systems. Whenever a user approves a request, updates a record, submits a form, or triggers an automated workflow, it's recorded. Process mining tools gather this information – timestamps, user actions, system events, and transaction ID is extracted — and reconstruct the process to gather insights about how the work actually gets done, as opposed to how people think they run. 

Tools like Microsoft Process Advisor, Celonis, and Appian Process Mining use these logs to build insights into workflows. Armed with the correct data, the software can identify process bottlenecks, optimize resources, improve automation, and reduce operational costs. In other words, these tools gather the data that show how real-world performance differs from expected performance and why. 

We're not talking about controversial systems like Flock Safety cameras here, although they are certainly data collection tools in their own right. Rather, the observation tools we're discussing are a touch more subtle, quietly watching how people interact with a website or app without ever revealing they're doing so. 

One example is session-recording software, which captures anonymized user interactions to understand how digital interactions actually happen. Tools such as Contentsquare, Microsoft Clarity, and Crazy Egg record clicks, scrolls, mouse movements, and navigation paths. None of this is personal data; it aims to understand the behavioral patterns that occur when a person interacts with a site. The anonymity is important here, and not just to protect a user's identity, but because people behave differently if they know they're being watched. This trait even has a name — the Hawthorne effect — and it can drastically affect survey results, both physical and digital. 

This matters for digital tools because the whole point is to gather authentic behavior — the clicks people actually make, the forms they abandon, the parts of a page they ignore. If users know their movements are being tracked, their behavior changes, and the data collected becomes less representative of reality. While there is something unnerving and Big Brother-ish about this collection method, the reality is less sinister. Hidden observation avoids the Hawthorne effect and gives a more accurate picture of real-world usage. 

It might seem strange in the digital age, but some "old-school" data collection tools are still incredibly relevant. Surveys, interviews, focus groups, and checklists are frequently used by organizations to gather information, and according to user-research platform Maze, they remain important even in AI-heavy research environments. The Maze 2026 industry report shows that teams still rely on surveys for scale and interviews for nuance, with both methods pairing to offer "big picture" insights. AI is now used by the majority of research teams, but mainly for tasks like synthesis and transcription, and the Maze report notes that human judgement is becoming more important, not less. 

Organizations are also still using other "human-driven" tools to capture information. For instance, administrative records and checklists can identify gaps between intention and reality. Another commonly used manual collection method is rubrics. These are essentially structured scoring guides that break a skill or behavior into clear criteria, allowing someone to judge how well a person performs. They're especially useful for gathering and quantifying data about things that can't be counted; such as social skills, communication quality, or how independently a task was completed.

Finally, observational notes, exit interviews, and session assessments are all methods that continue to provide insights into how people perform, learn, or engage with systems and circumstances. 

Another set of tools used to gather information that's hard to quantify are arts-based data-collection methods. Similar to rubrics, these tools help evaluators capture things like emotions, experiences, relationships, and the meanings behind situations. But instead of scoring performance against criteria, arts-based methods invite participants to create something. This could be a drawing, a collage, a written story, or even a live performance. This might seem a fairly random way to collect data, but the thinking is that such an approach helps people to express ideas they struggle to verbalize.

This makes them useful when the goal is to explore how people feel, how they interpret a situation, or how different ideas connect. Importantly, the data in art-based methods isn't actually the artwork. Rather, it's the explanation behind it; evaluators work with participants to understand what their "artwork" represents. This includes establishing what the creation represents, what emotions or ideas it conveys, and what connections it reveals. The information gleaned from this interaction can be analyzed for common themes, patterns, insights, and patterns, just like any other qualitative data.