When it comes to knowledge management, all of the industry jargon can easily make your head spin. That’s why we’re going to break down the differences between data, information, and knowledge, the three main building blocks of any effective knowledge management process.
Although they’re closely linked, these terms are often considered interchangeable. But, there are also some important distinctions between them. Recognizing their differences and how they are all connected is the key to understanding how to build a successful knowledge management framework.
So let’s dive straight in.
Data is the starting point of knowledge management and can be best linked to an organization’s raw materials. According to Datarob, data encompasses “plain facts, observations, statistics, characters, symbols, images, numbers, and more that are collected and can be used for analysis.”
There are two types of data: quantitative and qualitative.
Quantitative data is quantifiable, in other words, it is easily measurable as it is numerical by nature. The total revenue a company made in 2020, how often users are clicking on a link, the combined amount of paid leave employees took in any given month – these figures are all examples of quantitative data.
Qualitative data, on the other hand, cannot be measured, as it’s descriptive and linguistic rather than numerical. This type of data includes conversations, observations, and feedback, such as a customer’s conveyed experience using a service, or how employees feel about recent changes implemented in their company.
An isolated piece of data, whether it’s a number or a remark from a customer, can be considered meaningless in itself— it needs to be put in context in order to be useful to an individual or an organization.
So, data vs. information, what’s the difference? Information is a collection of data that has been processed, refined, structured, and/or presented to create relevance and usefulness.
A sequence of prices— $10, $8, $12.50—mean nothing by themselves. It’s only when they’ve been gathered and analyzed that it can be determined that they reflect the range of amounts people in a focus group said they were willing to pay for a particular product.
While data is a stand-alone concept, information doesn’t exist without it. In fact, data is the foundation of information. To turn data into information, organizations use a variety of knowledge management systems, software, and tools. This includes databases, spreadsheets, contact details, key dates, documents, guidelines, strategies, and the list goes on. And since this data has been directed into a recognizable and digestible order, it’s now become information.
Just as information generates relevance from data, knowledge makes meaning from information. When information is analyzed in order to generate insights, draw conclusions, make predictions, and drive change, knowledge is created. What sets knowledge apart from information is that it also is made up of other elements such as experience and intuition. In other words, information is often referred to as the who, what, where, when, and why, but knowledge is more focused on the how.
Organizational knowledge is crucial to a business’s success because it adds a competitive edge. Any company can collect data and process this into information. But, what happens to that information is what can take a company to the next level.
The relationship between data, information, and knowledge is often linked to a pyramid structure, known as the knowledge hierarchy. This concept positions data at the bottom, information in the middle tier, and knowledge at the top, where each rung of the pyramid has gained an extra level of value than the stage before it.
The ultimate goal is that knowledge management tools and processes turn data to information, and then to knowledge, which then is channeled into action. Action can be anything from a change in tactic, a decision being taken, or even a learning experience for an employee or team.
Here at Lessonly, we like to think of this relationship not as a pyramid, but more like a cake. Yes, that’s right. Hear us out.
Let’s say data is a cake’s ingredients. A raw egg, a packet of flour, or a teaspoon of baking soda are next to useless by themselves. These individual items gain relevance when they’re combined with the other ingredients on the list, which is like information, where the data has been put into context. Yet, a list of ingredients alone still does not make a cake.
Knowledge is therefore the recipe, where the ingredients list has been transformed into an expertly considered formulation and sequence that results in a valuable outcome (a delicious triple-layered victoria sponge, for example).
Going one step further with this metaphor, almost anybody equipped with a recipe can make a half-decent cake (we hope). Still, an expert baker also taps into their learned experience and intuition to kick their dessert up a notch.
We like to think about data, information, and knowledge in this way, as it means they aren’t measured against each other in a hierarchical structure but regarded as working in tandem towards the desired outcome.
So, Why Do These Definitions Matter?
Why should we care about all this jargon anyway? Here’s why.
We’re living in a knowledge-intensive era. Businesses are investing heavily into driving their data and information into “actionable insights”, another popular buzzword which is when gained knowledge leads to meaningful action and improvement of your company and its team.
But misunderstandings on the nature of data, information, and knowledge, and how they are interlinked, are leading businesses to fall short of their goals. Many companies mistakenly drown their systems with more and more data and information in a “throw everything at the wall and see what sticks” approach, without considering that generating knowledge actually requires an active and skillful process.
These issues stem from the misconception that information equates to knowledge when this isn’t the case. Just because an employee may have a heap of information about a given subject, this doesn’t mean by default they have the experience and intuition to come to conclusions, value judgments, or informed decisions. That, our friends, is knowledge.
And, even when valuable knowledge is flowing throughout an organization in abundance, businesses’ need a successful knowledge management framework to capture and optimize it. Without that, knowledge runs the risk of not reaching the right people at the right time or isn’t packaged in a way that makes employees understand its use and value to them, getting lost in translation. This is why knowledge management in organizations is so crucial.
After all, knowledge without management is like an ambitious goal without a plan: a missed opportunity.
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