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We often speak of data with the characteristics of information, I’m guilty of this just as much as anyone. The distinction is important to make however, Data being a thing such as a number “32”.  With only “32” this number could mean anything, it could mean the number of cows on a farm, the number of people in a line or queue, a flight number, etc.

Information is data plus context, as in this case “degrees Fahrenheit outside today”. In this example, the information is that the temperature outside today is 32 degrees Fahrenheit.   With the application of context (explicit or implied) we moved from knowing the data of 32 (a number that mean anything) to 32 represents degrees in Fahrenheit, outside, and the time frame of today.

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Data is something that we in the Information Technology industry work with on a daily basis, what is often lost is that the value lays dormant in the information. We don’t call it the Data Technology industry, we call it the Information Technology industry, this isn’t an accident. There are specialties of managing data (Data Management) and managing information (Information Management), these are separate for a reason.

Ultimately the goal is to take action on information, the path may seem that it is to obtain data and act, however there are implied steps (implicit context and/or the application of context) that must happen first. This is due to following the steps of acquiring data, applying context (making it information), adding the information to knowledge, and then using wisdom to take (the right/constrained) action. Action is when value is exercised from the data or information. Everything in-between are steps to try to get to value.

What about Knowledge and Wisdom/Action? Going back to the example of “32 degrees Fahrenheit outside today”.  There are other things we know about this statement, specifically that Fahrenheit isn’t used in many places around the world, so we know our likely location is in one of those places. We also know that Fahrenheit can be converted to Celsius and that 0 Degrees Celsius is the freezing temperature of water. If we convert 32 Degrees Fahrenheit to Celsius we find that it is 0 Degrees Celsius or we may have already known that 32 Degrees Fahrenheit is the freezing point of water already and not have needed the conversion.

By already having information such as 32 Degrees Fahrenheit and 0 Degrees Celsius are the respective freezing point of water, we have already built up Knowledge that we could apply to the incoming information of  “32 degrees Fahrenheit outside today”. By using this Knowledge, we can determine that it is cold outside and that we should bring and maybe put on a coat before leaving. Putting on the coat before leaving is using Wisdom/Action based on the Information and Knowledge that we had. The value is in not being cold when we are outside because we brought a coat with us.

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An important thing to remember in the differentiation between Data and Information is that in computer science we are used to working with data. We now call people Data Scientists who specialize in working with, manipulating and interpreting data. A Data Scientist is trying to take Data and transform it into information or several different forms of information ultimately to take action. This leads into the cycle of Data to Information to Knowledge to Action that was outlined. in the example above.
One final insight is that as each respective step is taken, that step often creates more Data, Information, Knowledge, and/or Action (see the back propagation arrows in the diagram above).


  1. I prefer peter checklands model data -> capta -> information -> knowledge

    the gist of it is that data is just a stream of all the uncountable number of facts available to us, humans sample the data to create a subset of interest and that selective process created what he called capta, I think it helps to make clear that “data” is always sampled, that knwledge allows for better conversations about the sampling, given that, what you have labeled as data is, in checkland’s model, capta

    see for example fig 4.1 p113 “learning for action” checkland and poulter isbn 0-470-02554-9

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