The Different Types of Data and When to Use Them

If you’re ever wondered why different data sources can tell different stories, you’re not alone. The truth is, not all data was built for the same purpose - and that’s actually a good thing. When you understand what each type of data captures and what its strengths and weaknesses are, you can use them together to make clearer, more confident decisions. 

Let’s break it down in plain English. 

What are the different types of data? 

At the highest level, you’re working with three main buckets: real data, big data, and research. Each captures something different, and has different strengths and limitations. 

• Real data: A complete data set with an exact number. It’s directly collected, complete and unmodeled data. Tickets sold for an event, I-94 Non-Immigrant Arrival Records, and lodging tax collections are all examples of real data. 

• Big data: Think of this as high-volume, statistically significant data that gives you a powerful view of trends and behavior. Think: Geolocation data, credit card spending, and survey-based lodging performance. It’s fast, granular, scalable and great for identifying patterns, but doing so requires modeling data, so assumptions are required. 

• Market research: This captures insights data can’t, like motivations, sentiment and decision-making logic. Surveys, intercept studies and focus groups are all examples of market research. They're great for explaining the “why” behind human behavior, but the data comes from relatively small sample sizes, and results are subjective and slower to collect. 

How and when should each type be used? 

The best type of data is the data that directly answers the question that you’re asking. It can be tempting to start with the dataset and work backward, but the key is this: Begin with the decision that you need to make. Then, choose the data that supports that decision. Ask yourself: “What am I trying to understand and what type of data naturally captures that? 

Here’s a cheat sheet: 

Want to know how many people attended? → Real data, like ticket sales, point of sale, or tax data. 

Want to know what they did, where they went, or how trends are shifting? → Big data, like geolocation, spending or lodging data.

Want to understand why they came, what they felt, or what influenced their decisions? → Market research, like surveys or intercepts. 

Don’t forget this important truth: No single data source gives you the whole picture. Every dataset has strengths, blind spots, and trade-offs. That’s why the most confident strategies layer them together - combining real data, big data and research to fill gaps, validate assumptions, and round out the story. Used together, they paint a clearer picture than any one data set could on its own. 

A final thought 

When you understand what each type of data does best - and use it with intention - unlock a fuller, more actionable view of your audiences and performance. Remember: 

➜Real data shows what happened 

➜Big data shows how people behaved 

➜Research shows why they did it. 

Used together, they can turn insights into clarity, and clarity into smarter, more confident decisions. 

Ready to stop guessing and start seeing clearly? Let’s talk about what Datafy can do. Schedule a meeting to chat.