The DIKW Hierarchy is a model that explains the relationships and the distinctions between Data, Information, Knowledge and Wisdom. Although these concepts are commonly used for one another in our daily life, in scientific and business thinking, they have very different meanings.
The first time I became aware of these distinctions was while taking the Systems Methodology course by Prof. Iraj Zandi at the University of Pennsylvania, a course that significantly altered the way I view the world. I remember Prof. Zandi talking about these four different but interrelated concepts, explaining them in a model proposed in 1988 by another Penn professor, Prof. Russell Ackoff, one of the pioneers of systems thinking.
Ackoff, in his article titled From Data to Wisdom, proposed that the contents of learning in an organization, regardless of size, can be represented as follows:
Information: data that are processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions
Knowledge: application of data and information; answers “how” questions
Understanding: appreciation of “why”
Wisdom: evaluated understanding
Several years later, in 1997, Gene Bellinger elaborated on Ackoff’s model and came up with his version:
Personally I contend that the sequence is a bit less involved than described by Ackoff. The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that supports the transition from each stage to the next. Understanding is not a separate level of its own.
Data: It rained today between 2:30 PM and 3:18 PM. A rainbow was visible in the sky from 3:18 PM to 3:30 PM. The sun was also shining after 3:18 PM, once the rain clouds had passed.
Information: There is a connection between the rain and the rainbow. Other combinations of a rainbow appearing after a rain shower have been observed many times previously. There were times when it rained with no rainbow following, but a rainbow was ALWAYS preceded by a rain shower. And the sun was ALWAYS shining in all of these circumstances. Whenever the clouds had not parted, a rainbow was not visible. A rainbow can only be seen after it rains and if the sun is shining.
Knowledge: The rainbow consists of different colored lights. The only light source when it happens is the sun. Somehow the light of the sun must be transformed into different colors. When there is no rain there is no rainbow and when there is heavy rain there is also no rainbow. There must be a specific concentration of raindrops in the air to form the rainbow. The raindrops in the air must somehow be transforming the sunlight into different colors.
Wisdom: We see rainbows because of the geometry of raindrops. When the sun shines from behind us into the rain, incident rays of light enter the drop and are refracted inwards. They are reflected from the back surface of the raindrop, and refracted again as they exit the raindrop and return to our eyes. Refraction is responsible for splitting the sunlight into its component colors. The rainbow will alter as you move and will differ from others’ perceptions. Because the light from any single drop is dispersed, only one ray of a particular color reaches your eye. The violet band that you see leaves the corresponding raindrops at about a 40.6° angle, and the red band that you see leaves its corresponding raindrops at 42.4°, so the red light is from raindrops higher in the sky relative to your eye. (Explanation by WebExhibits)
What made me think of all this was an article by Nichole Kelly, President of SME Digital, titled How Data Hype Is Destroying Your Social Media ROI. Kelly warns marketers about various infographics to be found all over the Internet, and urges them to double check the accuracy of the information represented within.
“In the early years of social media marketing (just 6-8 years ago, really) several major media outlets chastised bloggers claiming they didn’t cite sources and do enough research to make sure the information was accurate. The social media crowd stood up and said that our audiences would control bad information by calling it out, complaining in comments, or simply not sharing the information with others. In essence, our audience would be the filter for bad information.
Well somewhere along the way we have fallen down on the job. We aren’t being critical enough about all this data that is getting thrown at us. I see too many just believing the data because it came from a “reputable source,” — a company or an individual we have come to trust. We need to use a more critical eye before we jump on the band wagon of support. More importantly, we must be more curious when using this information to justify adjusting our marketing tactics.”
Kelly is talking about social media ROI in particular, but her points are valid in general. There is an wide range of data and information available on the Internet, some quite accurate and reliable, while others … not so much.
Before the Internet, when we needed to get data and information on a subject, we used “traditional” information sources such as books, magazines, and published reports. I remember spending tens of hours at Wharton‘s Lippincott Library in 1992, conducting research for a management paper. The Internet as we know it now did not exist back then, and I had to spend all that time hunting down relevant articles by checking out paper copies of old business magazines, waiting in line to run Lexis/Nexis queries and sitting at the library, reading industry reports which could not be checked out. The upside was that I was very comfortable with the quality of all the data and information I was getting. They were being published by professionals and were most likely double and triple checked by editors.
Nowadays, things are a lot different. No one has to evaluate or approve Internet content before it is made available to the public. Anybody with a computer and an Internet connection can put anything they want onto the Internet. One would think that this ability would create a sense of skepticism in Internet users, but no, most people are eating it up!
Accepting data without checking its authenticity and sources is bad enough, but it gets even worse: People are outsourcing their thinking, not even bothering to analyze the data to “understand the relations”, as Bellinger suggested, to get to information. People accept other people’s analysis at face value, with absolutely no regard to quality or accuracy. I tried to touch upon this in Distimo’s “Most Popular Social Networking Apps” Study and How NOT to Display Data, which is but one example. Not to sound too alarmist, but it looks like there is a mass epidemic of “cerebral laziness” out there!
It turns out that Robert Harris of Southern California College, saw this problem way back in 1997. In his work Evaluating Internet Research Sources, he revealed his CARS Checklist for Information Quality:
“Source evaluation–the determination of information quality–is something of an art. That is, there is no single perfect indicator of reliability, truthfulness, or value. Instead, you must make an inference from a collection of clues or indicators, based on the use you plan to make of your source. If, for example, what you need is a reasoned argument, then a source with a clear, well-argued position can stand on its own, without the need for a prestigious author to support it. On the other hand, if you need a judgment to support (or rebut) some position, then that judgment will be strengthened if it comes from a respected source. If you want reliable facts, then using facts from a source that meets certain criteria of quality will help assure the probability that those facts are indeed reliable.”
The CARS framework consists of qualitative checklists on Credibility, Accuracy, Reasonableness and Support. While originally focused on internet sources, I think that it applies just as well to print resources, sets an example of critical thinking and provides insight into creation, presentation and application of data and information. It is a recommended reading of mine for anyone who uses the Internet as a source of data and information. Here is a summary:
Credibility: trustworthy source, author’s credentials, evidence of quality control, known or respected authority, organizational support.
Goal: an authoritative source, a source that supplies some good evidence that allows you to trust it.
Accuracy: up to date, factual, detailed, exact, comprehensive, audience and purpose reflect intentions of completeness and accuracy.
Goal: a source that is correct today (not yesterday), a source that gives the whole truth.
Reasonableness: fair, balanced, objective, reasoned, no conflict of interest, absence of fallacies or slanted tone.
Goal: a source that engages the subject thoughtfully and reasonably, concerned with the truth.
Support: listed sources, contact information, available corroboration, claims supported, documentation supplied.
Goal: a source that provides convincing evidence for the claims made, a source you can triangulate (find at least two other sources that support it).
I will leave you with something I saw on Facebook this week. While amusing, it is also very sound advice, one that should especially be taken to heart by those of us who always want to get good data and run a good analysis and hopefully reach a level of wisdom that will help us make good decisions, be it in a business setting, or life in general. Remember: Not everyone out there is an Honest Abe!