A Simple Guide Regarding the Difference Between Data and Information

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Today’s world revolves around data and information. In the past, knowledge was passed down from generation to generation, written down by trained scholars, and stored in libraries that only the rich can afford. Now, anyone with an Internet connection has unprecedented access to data. 

Not only is our species consuming data at unfathomable rates; we are also generating it faster than we can keep track of it. By 2025, the world’s data production will reach 463 exabytes – an amount equivalent to 92 times the number of words spoken by humans in recorded history. 

Needless to say, all of this data can be overwhelming. It can also be confusing, especially to people who are looking at it for the first time. Knowing how to tell between data and information can help you make sense of a constantly growing and evolving mountain of data and harness it to define and achieve your business development goals. 

Before we dive into the difference between data and information, let’s take a look at their respective definitions.

Are data and information the same thing?

Most people tend to use the words “data” and “information” interchangeably. For most purposes, this isn’t really a big deal. However, if we are going to stick to the textbook definitions, you will see that while data and information are related, they are not one and the same. 

If they are not exactly the same, what are they, and how are they different from each other? Also, how are they related? Let’s find out so we can figure out the difference between data vs. information. 

What is data?

The English word “data” comes from the Latin word datum, which means “something given”. The classical definition of the word implies that data is a fact that is given or collected as the basis for calculation in mathematics. Data comes from a diverse set of sources, including customer surveys, instrument sensors, marketing chatbot software, and face-to-face interactions with users.

However, data in its simplest form does not tell you anything. Here are three reasons data by itself is not very useful:

Random

When we talk about “data”, we often think of graphs and charts and trendlines. However, data lacks that organization. In fact, it often consists of numbers that don’t make sense on their own. Here is an example:

The numbers above seem to have come out of a (poorly constructed) random number generator. We have no idea about the order with which each number arrived or how each number is related to the others. They’re just there, waiting for us to make sense of them.

Unprocessed

The phrase “raw data” is actually redundant as data is raw by nature. It doesn’t have any structure or order. While it could be useful, we still don’t know what to do with it. In fact, we don’t even know which pieces of data can be useful and which ones are just noise. 

In the example above, the numbers are not yet processed and we don’t know what they measure – or if they stand for anything at all. 

Meaningless

Finally, data is meaningless without context. A set of numbers without units or order does not tell us anything. Strictly speaking, there is no such thing as “data storytelling” as data on its own does not follow or advance a narrative. However, you can process data to help tell a story, as you will see later on when you’ll see some examples of data.

What is information?

The term “information” comes from the Latin informatio, which could mean “to instruct”, “discipline”, or “to give form to the mind”. As the root word implies, information is meant to teach a person or form the same person’s opinion about something. It is meant to influence people. 

That being said, you can use information to convince people to do a specific action. Here are three main reasons information can be useful to you:

Structured

Information consists of data with structure. A structure can be as simple as correlating one set of values to another in a manner that will allow a person to at least imply meaning from the overall data set. Let’s take a look at the same set of numbers we discussed in the previous section, but this time arranged in chronological order:

As we can see in the illustration above, these numbers are no longer random. Instead, they correspond to specific times of the day. At this point, we know that the top values fall and then rise from 4:00 PM to 1:00 PM, but still doesn’t tell the entire story. However, we are still one step closer to making sense of these numbers. 

Processed

At this point, we know the relationship between the numbers and the time. But what do these numbers tell us? To get a better understanding of the numbers, we have to process them a bit. One way of processing data and turning it into information is applying units to them. Units give us an idea of what is being counted or measured. 

In our example today, the numbers indicate the temperature in Bristol: 

You can already form a story in your mind at this point. At 3 PM, the temperature reaches its peak of 21°C, starts going down just before 7 PM, then picks up again the next morning. You may also choose to disregard specific data points that aren’t relevant to you. For instance, you won’t look at the temperature for 4 AM if you have no intention of going out at that time. 

Meaningful

Finally, a piece of information takes on more meaning as you take it in the context of similar information. You can tell longer stories and make more reasonable assumptions as you process and consume information. Knowing today’s weather can help you plan for the rest of your day, but knowing the weather for the rest of the week can help you make longer-term decisions: 

 In the example above, you can see that the temperature can go down to 13°C in the next seven days. You might think that it’s a fluke, but when you look more closely at the pattern, you can see that the minimum temperature will swing between 13°C and 14°C for the rest of the week, which means you’ll need to get your sweaters out of storage in preparation for fall and winter. 

What is the difference between data and information?

Now that we know the definitions of data and information, let’s look at not just one, but three key differences between data vs. information.

Information is data that has been processed

Data is raw, unstructured, and unorganized. It consists of numbers that seem to come in rapidly and at random. While it’s easy to add structure to data that you collect manually, a lot of the data we generate doesn’t have an innate structure to it.

Wearable devices such as FitBits or fall sensors generate data by constantly detecting motion. This data is stored as a series of 1’s and 0’s in your device or uploaded to an app. 

Source: Business Insider

Information, on the other hand, is structured, organized, and processed. It consists of data that has been divided and correlated with other data sets. For example, your FitBit collects data detecting and analyzing your movement, then converts it into an equivalent number of steps. It then stores that information in your FitBit app where you can track your weekly activity progress.

Information is understandable

Whenever we think of data, we usually think of strings of numbers and letters that should make sense but don’t for some reason. On the other hand, you can take information, visualize it, and understand it easily.

In the example above, the FitBit app does not just count your steps. It takes those steps and translates them into units that you can easily understand:

Source: Android Authority

If you’re having a hard time understanding what 27,802 steps mean, it’s equivalent to almost 16 miles of walking and 4,300 calories burned in a day. Those are pretty impressive numbers, but even more impressive is the way FitBit translates data on steps into their distance or calorie equivalents. 

Information can help you make decisions

People nowadays claim that their processes or decisions are “data-driven”. Unfortunately, they tend to confuse data with information. One main difference between data and information is the amount of insight the latter gives you. Data alone doesn’t tell you much, but when you set information against a set of SMART goals, it helps you arrive at a decision.

For example, information about your activity patterns for the week can help you see when you’ve exceeded your activity goals. By zooming out and looking at your activity for previous weeks, you can tell if you’re in danger of tiring yourself out. If your activity level is above a certain limit, your fitness tracker might suggest that you get some rest instead of pushing harder.

Source: 9to5Google

By feeding usable information to apps or devices, you can make decisions that will benefit you, your business, or your health. 

How do you tell if something is data or information?

We’ve covered some examples of data vs. information. In the following section, we’ll quickly review how you can see if something is data or information. We’ll also look at things like quantitative data. 

Look for units

The main difference between data and information is that data is just a series of numbers or words, while information needs units of measurement for it to make sense. Units ensure that you are counting or measuring the same thing consistently.

For example, a thermometer might say that the temperature is 32, you need to determine if it’s 32°C or 32°F. The first one is a nice, sunny day, while the second is a cold, freezing day. In other words, units add context to raw numbers.

Look for patterns and variances

While it’s possible for you to see recurring numbers in a raw data set, patterns are more common in information, especially if the measurements are done the same way all the time. By analyzing these patterns, you can set a baseline and detect instances when your data-gathering results in information that points to a variance.

For example, doctors know that the normal temperature of the human body is roughly around 37°C by observing patients and taking their temperatures. Whenever they check on someone who doesn’t feel well, the thermometer will reflect a higher temperature than usual, which leads to the conclusion that the patient is probably fighting off an infection.

Look for stories you can tell

Data on its own doesn’t tell stories. You can’t just look at a bunch of numbers and find a narrative there. Information, on the other hand, is a good opportunity to find recurring patterns that lend themselves to larger stories, especially if you connect and analyze different sets of data from similar sources. 

Let’s go back to the FitBit example earlier and imagine that you joined a fitness club at work. You get together with the club, compare your weekly activity levels, and find that most of you show decreased activity in the middle of the week. When you dig further by analyzing time spent away from the keyboard, you realize that the workload across the board picks up on Wednesdays, which results in less time for other activities.

These stories are only possible if you have the patience to take data, process it, and find patterns that go against what you’ve been expecting. By identifying these unexpected patterns, you can trace the past, tell the story of the present, and make predictions about the future. 

Wrapping up

To most people, data and information have the same meaning. While the two are related to each other, they are not identical. Understanding the difference between data and information will help you take steps towards being able to identify raw numbers or figures from information that you can already use. 

There are three key differences between data and information: structure, processing, and meaning. Data comes from different sources and is usually unformatted and not understandable. In contrast, information is data that has been processed and structured so that it can be used by humans to tell stories or make predictions. 

By understanding the key differences between the two, you will be able to study and analyze your data so that it makes sense to you and provides you with actionable insights. 

About the author

Nico

I'm a SaaS consultant with a passion for travel. I help companies scale their link building so they can dominate the SERPs for their chosen keywords. I work primarily with scale-ups and enterprise businesses.

By Nico