Not all data is created equal — especially when it comes to business applications. While Big Data certainly can help your business grow, you shouldn’t try to smash and grab as much data as you can; that will only weigh down your organization and slow your growth. Instead, you need to work toward understanding which data will most inform and enhance your operations — and to do that; you need to understand what types of data are out there.
Fortunately, that’s not too difficult. Broadly speaking, there are only two types of data: qualitative and quantitative. This guide should help you learn what kinds of data are in each category and which you should pursue to benefit your business.
Because qualitative data is more difficult for most business leaders to understand, it’s usually best to start here. Qualitative data is composed mainly of descriptors; it is data that is traditionally observed subjectively and isn’t easily measured. You’ll often see qualitative data laid out in verbal form, with explanations such as look, taste, texture, smell and so on.
It is easy to remember the difference like this: qualitative has an L because it relies upon language.
When you ask, “What is intent data?” the best answer is that it is qualitative data. That’s because intent data tends to measure unquantifiable (i.e. difficult-to-apply-numbers-to) factors of prospects to determine whether they are likely to enter the sales funnel. However, even the relatively specific category of “intent data” can be broken down into flavors of qualitative data, which include:
Nominal or unordered data is assigned to categories that do not have explicit or implicit value or rank. For instance, placing candies into color categories without having a color preference would be utilizing nominal data. Or, as a more pertinent example for your business, nominal data is organizing your kinds of customers into categories, perhaps by industry, before you know what types of customers are best for your business.
Binomial data, also called binary data, places a person or thing in one of two exclusive categories — which you can make up to suit your business’s needs. Common binaries include right/wrong, true/false and accept/reject. Once you have the appropriate data to determine what customers are best for your business, you can use a binomial system to choose which leads to pursue aggressively and which to let sit on the back burner.
Finally, ordinal or ordered data is the exact opposite of nominal data in that it creates categories that do have an order. When you conduct customer surveys that ask customers to rank products and service on a scale of 1 to 10, you are asking them to produce ordinal data because ten is implicitly better than 9, nine is better than 8 and so on.
Meanwhile, quantitative data is what you most readily accept as data because it can be measured objectively. Quantitative data is not easily influenced by subjective experiences like mood, gut feelings and interpersonal connections. Thus, most business people prefer to rely on quantitative data, believing that it won’t lie or lead them astray.
Just like the L in qualitative, the N in quantitative should help you remember this: numbers.
How you collect and parse quantitative data is up to you — or, more accurately, your business analytics team — but it might be helpful to learn that there are two main types of quantitative data:
Discrete data is a count that cannot be made more precise, which means its entities are not distinguishable and thus result in whole integers. The number of clients your business has serviced is discrete data because you cannot have half or a third of a client.
Conversely, continuous data can be divided and reduced to finer and finer levels. For instance, you probably measure the size of your product in inches or centimeters, but you could also measure them in half-inches, sixteenth-inches or millimeters, micrometers and nanometers. Doing so can provide you with more precision.
Qualitative data is more useful than you might expect, even though you can’t use it to make flashy graphs to show your investors. Quantitative data boasts the facts and figures you might assume Big Data requires, but in truth, both qualitative and quantitative data types are significant in the decision-making process. Once you grasp how to apply qualitative data to your business processes, you might find it even more beneficial than quantitative data.