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Sky Cassidy


Defining Your Data

Six terms to avoid confusion

Published: Monday, January 4, 2021 - 12:02

Whether you subscribe to the scientific definition of data (information on which operations are performed by a computer and transmitted in the form of electrical signals) or the philosophical definition (that which is known and used as the basis of reasoning or calculation), I think most people use the word “data” incorrectly.

If you’re a data scientist, or you become upset that this will be the only time I use the singular form “datum,” this article will probably disgust you, and I apologize. On the other hand, if you’re in marketing, sales, or just about any other department, then I hope this will help clarify the overused but super-useful word “data.”

One of the roots of the problem with the word is when it’s used as a generic noun. It’s overused and causes confusion. Confusion is the enemy, particularly in sales and marketing. Granted, data is a useful word because it’s short. When used among a group of people who work with the same type of data, the term works as a reference for what everyone knows you’re talking about.

However, imagine you’re at a party. You ask someone you’ve just met what they do for a living, and they answer, “I work in data.” They might as well have said, “I do stuff.” The person’s response really means nothing on its own.

So, what could they have said?

Following are six categories of data. There are many more niches than these, but these six are a good way to start, a good foundation. It’s useful to mention that, although information is generally thought of as something of value that is extracted from data (as in the DIKW pyramid), our practical use of the word “data” will not be getting that technical.

Simply put, data are facts and statistics collected together for reference or analysis. The one big hole this definition leaves (you could shoehorn it in there with a law degree) is data used in direct sales and marketing. We could argue about whether this is “data” or “information,” but since everyone refers to it as data, we’ll do the same here.

Six classifications of data

Numbers (quantitative data)
This is a super-general category, and despite driving any data scientists still reading this totally crazy, I’m starting with it because it’s referenced a lot. When it comes to all kinds of reports, most data of any type are converted into numbers. Numbers are great for analysis because they can be played with to create more data, and key performance indicators (KPIs) of every type and sort can be made from those test-procedure specification reports. “What do the data say?” typically means the numbers. But if you don’t know which numbers are “talking,” then confusion ensues. To make this category clearer, one needs to get more specific: “What are the sales totals?” In other words, throw the word “data” right out. When you reference sales totals later, “data” can be used to refer to them. For example, “The data tell us Team A gets a bonus, and Team B has to go.”

Non-numerical data (qualitative data)
If they can’t be represented by numbers, you can bet what you have are qualitative data. The number of website visits or leads would be quantitative, but the URLs people visited, the timestamp, and other information that is more than just a count is qualitative data. Simply saying, “I need the qualitative data” doesn’t exactly get the job done or win you any friends. As with other data categories, when referencing qualitative data, it’s better to be more descriptive initially and whenever in doubt. Instead of “looking over the data,” you’re “looking over the website visitor data,” etc.

Big data
This is very large sets of data, typically unstructured masses of data, such as buying habits collected by stores that track what is bought, when, for how much, the type/category of product, and possibly the person who bought it. The data collected over time on a single shopper through their use of a rewards card or something similar would not be considered big data, but those same data on every shopper in the United States would be big data. Other examples would be stock exchange traffic, roadway traffic patterns, weather data, and the information collected by every app on every phone all the time.

Dark data
This has a huge overlap with machine data. Dark data refers to information that is created but never looked at or used. Examples of dark data are the billions of photos we take on our cell phones to never look at again, the billions of emails that are stored on servers never to be seen, or things that were created automatically like program logs and surveillance videos. Basically, the data equivalent of everything you put in that storage shed because you might need it but you never end up looking at again.

This is a tricky category because analytics isn’t data; it’s a process for analyzing raw data in order to make conclusions about that information. It’s things like looking at your website traffic information to get an idea of what products and services people are most interested in, to gauge when a campaign is effective in driving traffic, what ads are driving the most traffic, and so on.

This is typically data used in direct sales and marketing. Also referred to as a list, marketing list, sales list, direct marketing data, campaign list, or target list. A database is collected information about prospects or clients that includes things like company name, address, phone number, contact name, title, email, website, company size, industry, and any other company or contact fields used by sales and marketing. Using “database” rather than “data” will help prevent some confusion, but it is recommended to go even further and identify the type of database it actually is, such as sales database, marketing database, or client database.

People don’t have to be data scientists to talk about data. In fact, the people you speak with the most will probably understand you better using the practical terms shared here. Remember, before using the word “data” as a noun, make sure it’s already clear what is being referenced.


About The Author

Sky Cassidy’s picture

Sky Cassidy

Sky Cassidy is CEO of MountainTop Data. MountainTop Data has been providing data and data services for B2B marketing for more than fifteen years.