# The Answer is 42. On Data, Information and Knowledge

A recent discussion with some colleagues on the differences between data, knowledge and information made me realize that there still is a lot of confusion when it comes to the use of terms; confusion that goes well beyond my earlier blog post on indicators, measures and metrics.

In this blog post I'll discuss the differences between data, information and knowledge by using an example of counting cattle from space.

People who have either read or seen the comic science fiction series “The Hitchhiker's Guide to the Galaxy” know that 42 is the “Answer to the Ultimate Question of Life, The Universe, and Everything”, calculated by supercomputer Deep Thought.

It took the computer 7.5 million years to compute and check the answer, which turns out to be 42. But when the instruction for the calculation was given there actually wasn’t a clear picture as to what the question was. The question to the instruction developed over time.

That makes it the perfect analogy to start a discussion about data, information and knowledge in the light of decision-making with the use of evaluative evidence.

I think we can all agree that 42 in itself is data. Even with the question “What is the answer to life?” the response being 42 is still data. But when we add the information that this number has been calculated, does that change the typology of the answer? Does it then become information? Not in this example, because we do not know what type of calculation took place and what the original data looked like that was used for the calculation.

### Counting Cattle From Space

But let’s say we are counting cattle from space. I know, still pretty spacey. But hang in there for a moment; it is not as far-fetched as it sounds. Let’s say we are using satellite imagery to do a census on cattle grazing as part of the M&E of a protected area intervention. The aim is to identify the drivers of habitat degradation in that protected area. By means of a computer model we identify cattle, based on a hierarchical object-based classification method of both cattle and cattle pens and on-the-ground data on the average number of animals per pen.

In a pilot calculation the answer from our computer at the GEF Independent Evaluation Office is 42. The question however is “What is the average number (per acre) of cattle grazing in the protected area?” Is that data, or is that information? There has been an extensive calculation and a data triangulation process that has used raw data. Some would say this is still data, some would say this is information.

What if the question is “What has been the decline in the average number (per acre) of cattle grazing in the area since it became a protected area?” By using a reference point (the moment the area became protected. Let’s say there were 100 cows grazing per acre at that point in time) and by looking at the development through time, the answer 42 becomes information. A relationship is being analyzed, the relationship between the area being protected and the decline in grazing within that area over time. We could even compare this development against a business-as-usual scenario by taking into account the data from a comparable area that has not become protected area.

### The DIKW Pyramid

What makes data become information? And what makes information become knowledge? The DIKW pyramid is a model for representing functional relationships between data, information, knowledge, and wisdom. There are some who reject the DIKW pyramid, because it is difficult to explain and leads to bad labels. But difficulty or complexity has never stopped me from pursuing something so let’s have a look at the version below of the DIKW Pyramid, which is my interpretation of how these levels interact and inform real-world decision-making.

Data comes in the form of raw observations and measurements. I tend to see data both as raw facts or chunks of facts about the state of the real world, as well as a symbol that attempts to capture the true picture of a real event.

Information is created by analyzing relationships and connections between the data. It is capable of answering simple “Who/What/Where/How many/When/Why is” style questions. Information is a message with an (implied) audience and a purpose. Quite often, when we talk about ‘data science’ or ‘data driven decision-making’ it is information and not data that feeds into the actual decision-making.

Knowledge is perhaps the concept hardest to define and definitions may refer to information having been processed, organized or structured in some way, or else as being applied or put into action. One view is that knowledge is a product of a synthesis in the human mind, and exists only in the thoughts in someone’s mind. This would mean that knowledge can only be shared as information and then become knowledge again in someone else’s brain. ‘Knowledge management’ under such a definition would basically be thought management.

My feeling is that knowledge (explicit as well as tacit) is created by using the information for action. Knowledge answers the “How” question. Knowledge is contextualized; a local practice or relationship that works, and can be shared by properly sharing the context that makes the information become knowledge. And interesting point made

Wisdom is created through use of knowledge, through knowledge users’ communication, and through reflection, i.e. by embedding values, beliefs and experience into knowledge. Wisdom answers the “Why do” question as it relates to actions. In a sense it is what helps us make a better informed decision between two seemingly similar choices, or what helps us to apply knowledge toward the attainment of a common or higher good.

Any of these terms are relative concepts and knowledge can be considered as information (data) on a higher, more abstract domain-of-application level. An example; When humans make decisions and use information for action we tend to talk about knowledge. But these days computers make a lot of decisions on data and information without any human intervention, which begs the question if a computer can be knowledgeable.Another point would be that the pyramid is not really a pyramid, but should perhaps look like an hourglass in which there are both lines going up as well as down. Data can be derived from knowledge and information; the quantification step in the Most Significant Change technique is a good example of this type of reverse processing.

In the end I think we all agree that decisions are often not made on data alone, but on information, knowledge and wisdom, which are established or derived (directly or indirectly) in part from data. Through processes like evaluation, research, observation and feedback we generate new data, information and knowledge.

Do you feel knowledge can be saved, or only exists in a person’s thoughts? If a farmer shares his knowledge on historical rainfall patterns in his area, does it become information once he writes it down and explains the context or is it still knowledge?

Which of these concepts informs real world decision making most? Information, knowledge or wisdom?

Do you feel wisdom is the right concept to talk about? Or is it too esoteric and should we talk about understanding?

### I like the focus of the DIKW

I like the focus of the DIKW pyramid on context/knowledge and understanding/wisdom. Knowledge from experience is an important type of information/evidence that is too often overlooked. Although leaders may sometimes make decisions based on quantifiable data alone, we are more likely to be successful when we make decisions based on understanding that is supported by all parts of the pyramid.

### I agree with you on knowledge

I agree with you on knowledge from experience often being overlooked. My feeling is that wisdom is often a bridge too far, which is why I put understanding below it - to me the most practical perhaps that can be achieved.

In addition to Bernadette's comment I would like to put another layer to the very interesting DIKW pyramid. Without being very familiar with this concept, it seems to build on a very linear concept of interaction between data-information-knowledge and wisdom. I would furthermore argue that specially in certain development projects the acknowledgement of "traditional knowledge systems" or "collective memories" are not taken in to account. Information is not only part of one person but rather a collective understanding of experienced reality. In a next step, knowledge is formed though cultural and conceptual frames, which leads to the issue of objectivity. How is knowledge “managed”, and who’s knowledge counts, to who’s knowledge do we have access to etc. Continuing this line of question “wisdom” becomes a concept which is indeed esoteric but nevertheless relevant to the initial point of the pyramid- doing the right data collection.

So, in the realm of data collection the level of lived experience is limited to a very short time-frame and not seen the necessary cultural context. In this case information would be the first step and prior to data collection.

Dear Michelle and Bernadette, thank you for some great points. I took the liberty to also add your comments to the post on the Climate-Eval site.
Michelle, I’m glad you bring up the community cultural memory systems point! To some extent you could argue that it is culture that adds ‘values and beliefs’, making the difference between knowledge and wisdom. Then again, knowledge on culture, values and beliefs is not the same as knowledge that is embedded in / informed by culture, values and beliefs. And as you point out, an individual’s ideas on culture, values and beliefs will certainly differ from a collective understanding of these concepts and how they influence information, knowledge and wisdom.
And I notice that I’m typing this and I’m excluding ‘data’, though data collection can very much be influenced by culture, values and beliefs.

The part on traditional knowledge systems demands that we acknowledge the existence of purely oral knowledge transfer mechanisms. It makes me wonder what the actual difference between information and knowledge will be in a community with mostly oral traditions of knowledge transfer.

My feeling is that the pyramid should be seen as a guide, not as a strictly hierarchical structure or linear relation between concepts. While we hope that most data comes from rigorous evaluations, research, observations, feedback loops, etc. perhaps most data actually comes from reverse processing of information. This also implies that information is a first step prior to deriving to data, or data collection. Having said that, I’m not so sure that necessarily guarantees the proper introduction of a cultural context, a step I see mostly in the top parts of the pyramid.

### I have just finalised a

I have just finalised a monitoring, evaluation and learning [MEL] framework for a regional organisation and had to make some decisions as to when to use information and knowledge especially when it came to aspects such as [information/knowledge] infrastructure and [information/knowledge] management. As I worked through the document, I realised that whereas the two concepts may have different meanings, these differences may not really matter in practice. In my view the operational meaning...the shared understanding is what is important to the users.

Having said that, however, I am an academic and I do appreciate the importance of theorising.

### Dear Nite,

Dear Nite,

I agree that to some extent it matters less in practice, especially when looking at the project level. I can see how the difference is perhaps more important when you work on a higher aggregated level on knowledge management. The difference also matters when talking about indicators and mixed qualitative and quantitative data to inform decision-making.

### This interesting, me too

This interesting, me too sometimes confused on the information and knowledge. during evaluations communities are involved in FGDs is it to share information or knowledge on issues?

### That really depends on the

That really depends on the use / situation. For example (and these examples are really my opinion and not as such generally accepted facts), if you want to create awareness around an issue, then I would say you look for information. If it would be lessons learned that are generally applicable, then you could look at either information or knowledge. And if you want want feed back towards the community for continued adaptive management, then you are looking at knowledge.

### Hi Dennis, thank you for

Hi Dennis, thank you for another interesting post. I have a question for you, it is something that crossed my mind several times before, and now again, while I was reading your post. How would you define blogging? How does your writing and sharing across the web fits into this DIKW pyramid? Is it information or knowledge sharing? I am writing a blog myself, and I am really interested to hear your oppinion on this.

### Most people would say that it

Most people would say that it is information, and I agree with this most of the time. This has to do with most people also not being clear as to whether knowledge can be shared, or whether it is only in the brain and it is information being shared.
But my personal feeling is that it can be knowledge if there is an extensive (almost exhaustive) description of the context and all assumptions made, which creates potential transfereability of the information contextually.

### In the context of impact

In the context of impact evaluation, decision making is directly linked to the state of understanding in a person’s mind. I find that information and knowledge will be in a person’s thoughts until some actions are taken at different levels. For example using most significant change stories, the point at which change occurs is when a person does something differently, compared to how this person did things before an intervention.

I also think decision making is influenced by the approaches that are used to synthesis information/knowledge whether through the decision making process or in a person’s mind. Decision making may require creative thinking. For instance, mindmapping elevates my thinking broadly and helps me to be focused.

### It's not confusion so much as

It's not confusion so much as multiple usages in multiple topical areas, situations, and applications, IMO. Those who use these terms know what they mean by them, but their listeners/ readers may reasonably assume differing meanings. In general discourse, the three terms in the topic title here are often used interchangeably; when we're talking about data hierarchies they tend to be meant as differentiating levels of data applicability, resolution ("granularity"), and depth. When holding forth on this aspect, I like to add a level below data, characterized as "stuff", and a level above knowledge, labeled "wisdom".

### Thanks for promoting the

Thanks for promoting the knowledge pyramid and building on how it applies to our M&amp;E work. I agree that these terms are concepts are really important for us to use when we are planning how to collect and use evidence (I have also seen "information" described as "potential knowledge," and "evidence" described as "knowledge that is relevant to a specific decision or action."

### Quiz answer stuff like names

Quiz answer stuff like names and dates is called knowledge in academe, but you can't do much with it, so it doesn't even rise to the data level in the functional real world. At the other end of the hierarchy, wisdom is what one uses to utilize knowledge effectively and responsibly (i.e. when not to, as well as when and how to use it). I'd say the term evidence is typically after-the-fact operational knowledge, while pretests and prototypes address prospective (before-the-fact) knowledge -- neither prove anything, but they improve the odds of being correct.

### Harry, I like the stuff. I'm

Harry, I like the stuff. I'm not sure what it will look like. The wisdom is there; knowledge -&gt; understanding -&gt; wisdom. Improving the odds of being correct. I like that too. Very pragmatic.

### Thanks, Dennis, I've found

Thanks, Dennis, I've found that elongated pyramid useful, but I can't say that it's met with wide acclaim.

### Hi Dennis, thanks for the

Hi Dennis, thanks for the blog post, and to everyone else for sharing your thoughts. I've been discussing this with Dennis for a couple of weeks now, arguing the point about knowledge needing to exist in someone's head. I ought to add that the whole debate is something I'm new to so I'd characterise my position as trying that argument on for size, rather than it being a firmly held view that I've thought about for millions of years ;-)

One of the reasons I like it is because it creates a clear distinction between information and knowledge. I don't personally find the pyramid helpful, since it seems that it all depends on one's perspective with relation to any one bit of 'stuff' as to whether it is data, information or knowledge. Dennis, you state that we can all agree that 42 is data, but what is it from Deep Thought's point of view?

### Dear James, thank you for the

Dear James, thank you for the wonderful discussion over time and your comment!

Point of view is the angle of considering things, which shows us the opinion, or feelings of the individuals involved in a situation. In literature, point of view is the mode of narration that an author employs to let the readers “hear” and “see” what takes place in a story, poem, essay etc.
Hence, I do not believe a computer has a point of view and as such there is no answer to your question on what Deep Thought's point of view is. If you believe that knowledge exists only in a mind, then in your view it might be that Deep Thought has a point of view. ;-)

### OK, let's say 'perspective'

OK, let's say 'perspective' then, rather than point of view. Deep Thought was a (fictional) hyper-intelligent AI, capable of independent, creative thought. That may never come to pass, but for the purposes of the discussion I think that we must assume it had a 'mind'. Having spent millions of years thinking about the answer to 'the ultimate question' of life, the universe and everything, I imagine it had developed quite a detailed context within which 42 meant something. I'd therefore argue that for Deep Thought, 42 was probably knowledge, within both your definition and mine. However, it's clearly not knowledge for anyone else - the great great great (times X) grandchildren of the people who built Deep Thought were understandably perplexed and disappointed, and therefore grateful when Deep Thought offered to design them an even greater computer that would be capable of working out what the question was.

(SPOILER ALERT! Stop reading now if you haven't yet read Hitchhiker's Guide to the Galaxy and have been sufficiently interested by Dennis's post to go and read it! I thoroughly recommend it - funny, thought provoking and humane)