Data and information are not the same–and they should not be confused.

Just What Is Data?

Data comes from the Latin word datum, meaning “something given.” Over time, the English language has evolved to use data as plural; we amass hundreds of thousands of pieces of “data” in the course of our work.

This gives us our first clue that data represents units of measurement. We store it in bits and bytes, megabytes, gigabytes and more. Data is the 1s and 0s that fill hard drives, and it’s designed to be read by computers–not humans.

The Properties of Data

The interesting properties of data provide a useful litmus test when we’re not sure if it’s data or information we’re faced with.

For example:

• Data is, when clean, a fact.
• Data can be stored easily, and at a low cost.
• Data can be copied easily, often using computerized methods.
• Data can exist in more than one place, so data is often duplicated.
• Data can be modified and moved quickly and simply.
• Data can be misrepresented, depending on its interpretation.
• Data has no value until it is used.
• Data does not mature, nor does it improve with age — in fact, data decays.

All data has to be interpreted to be useful to humans, which leads us to our definition of information.

And What Is Information?

The word information has existed in the English language for far longer than the word data. The concept of informing someone is well understood, and that gives us some clues to meaning.

When we talk about data, we think of megabytes of binary code. In contrast, think of all the ways we can measure information. We can use practically any meaningful unit: time, distance, amount, rankings, speed, and weight. We can add additional variants to the units, too (for example, an amount of money in a particular currency).

We can see that information has context. It gives us a fact relative to something else. It offers a yardstick for our decision making. It lets us derive some kind of conclusion once we understand it.

Defining Information

We might refer to information as:

• Data that has been processed to make it useful
• The right information in the right place
• Data plus meaning
• The foundation of correct decisions
• A known fact, or thing, used as a basis for inference or reckoning
• Data in context

After all, without context, information does not inform.

 

Where They Fit: The DIKW Pyramid

Think about a date, such as January 1.

What is January 1? What does it mean to you?

Expressed as 01/01, or 01012015, or 20150101, it’s data. Expressed as New Year’s Day, it’s information.

But knowing what we know about the world, we know that New Year’s Day is not always January 1. The knowledge that surrounds the information causes us to deduce that January 1 may or may not mean New Year’s Day.

Wisdom would be knowing which countries use the Gregorian calendar, and which do not.

The process of deduction we’ve followed mirrors the DIKW Pyramid, which describes how data becomes information, becomes knowledge, becomes wisdom.

Consider another example. You are a pilot. The number 10,000 flashes on your display. No label, no description, no units. It is data, but it means nothing to you.

• If the display reads ‘10,000 feet above sea level’, it is information.
• If we are aware of mountains soaring to 12,000 feet, that’s knowledge.
• Choosing to climb above the mountains requires wisdom.

These two examples prove that every layer in the DIKW Pyramid is founded on the accuracy of the data below it. The bits and base form a foundation on which our information, knowledge, and wisdom are balanced.

Had the pilot’s display read 13,000, his or her decision making would have been wrong, based on the fact that the data resulted in an inaccurate assessment of the situation. This is why data quality is so critical to businesses, too.

Better Data – Better Business

Your business collects data, stores it, copies it, modifies it, and (possibly) shares it. Your business may even buy and sell its data.

Data does not depend on information, but information depends on data. Data has no meaning, but information should provide a logical meaning to the raw materials we work with.

Poor data leads to the loss of our competitive advantage. It creates uncertainty, since it causes the DIKW Pyramid to topple. There are almost always economic consequences of poor data management, or a lack of clarity. And when we make deductions based on faulty data, the information we obtain is of a lesser value.

“The bitterness of poor quality remains long after the sweetness of low price is forgotten.”

This is a lesson every business should heed. With better data, and better information, we have better businesses.

It’s never too late to improve data quality, or start again with new insights into the way data should be managed. When you know better, you can do better. When you take steps to improve, you will always have better information to drive your business forward.

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