The 6 Types of Data Everybody Should Know to Avoid Confusion
Everybody tosses the word “data,” but few actually know what it actually means and does. MountainTop Data CEO Sky Cassidy explains the 6 different kinds of data everyone should know something about in order to avoid confusion.
Whether you subscribe to the scientific definition of data (information on which operations are performed by a computer transmitted in the form of electrical signals) or the philosophical definition (that which is known utilized as the basis of reasoning or calculation), MountainTop Data CEO Sky Cassidy thinks 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 hopefully this will help to clarify the overused but super useful word, ‘data’,” Cassidy says.
Cassidy explains that one of the roots of the problem with the word data is when it is used as a pronoun. It is overused and causes confusion. Confusion is the enemy, particularly in sales and marketing. Data is a useful word because it’s short and within a group that works with the same type of data, it can be used like a pronoun to reference what everyone knows you’re talking about. “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 ‘stuff’, it really means nothing on its own. So, what could they have said?” says Cassidy.
Following are 6 categories of data. There are many more niches of data, but these 6 are a good way to start, a good foundation. It’s useful to mention that while information is generally thought of as something of value that is extracted from “data”—re: the DIKW Pyramid—our practical use of the word “data” will not be getting that technical.
Simply put data is facts and statistics collected together for reference or analysis. The one big hole that 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 that is ‘data’ or ‘information’ and I’d be technically wrong, but everyone refers to it as ‘data,’ so we’ll keep it in the practical sphere of ‘data’ here,” Cassidy says.
Classification of Data
Numbers (Quantitative Data): “This is a super general category,” says Cassidy “and I’m starting with it to drive any data scientists still reading this totally crazy, but it’s referenced to a lot.” When it comes to all kinds of reports most data of any type is converted into numbers. Numbers are great for analysis because they can be played with to create more data and KPIs of every type and sort can be made from those TPS reports. “What does the data say?” typically means the numbers. If you don’t know what numbers are being talked, then comes the confusion. 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 it later, “data” can be used. For example: “The data tells us Team A gets a bonus and Team B has to go.”
Non-Numerical Data (Qualitative Data): “If it can’t be represented by numbers, you can bet it’s qualitative data,” says Cassidy. 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. Saying: “I need the Qualitative Data” doesn’t exactly get the job done or win you any friends. As with other data categories, it is 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 that same data on every shopper in the US 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.
Analytics: 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 and so forth.
Database: This is typically data used in direct sales and marketing. Also referred to as list, marketing list, sales list, direct marketing data, campaign list, or target list. This is the database of prospects and/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 say the type of database it actually is, such as Sales Database, Marketing Database, Client Database, etc.
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. Furthermore, before using the word “data” as a pronoun, make sure it’s already clear what is being referenced.
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