Like many people from an Information Systems background I have followed the path of texts where the concepts of data and information are introduced. Bocij et al,(2006) provides examples of data such as raw facts, numbers or non-random symbols. Information is derived from a process or interpretation of data which then provides the data with meaning for the observer. Knowledge is more difficult to pin down. It is described by Turban et al,(2006) as " Information that contextual, relevant and actionable". Chaffey(2006) suggests knowledge is: " the next level of sophistication or business value in the cycle from data through to information to knowledge."
I was surprised to find similar representations in more knowledge management centred papers . Kakabadse et al (2003) suggest "the chain of knowledge flow is data-information-realization-action/reflection-wisdom" yet still represent this as a linear diagram. This approach to the representation of the the relationship between the entities: data, information and knowledge is commonly presented to the reader in a format similar to the one below which appears to stress a sequential and unidirectional flow from one state to the next.
Previously I had accepted these definitions and could relate them to my programming background. An example could be such that a either singularly or collectively figures could be considered data. Processing that data along with other relevant data together in using a database could produce a record which can then be considered information. Further records on the customer can be analysed to produce knowledge about the customers spending habits and then produce a flag if abnormal spending habits occurred. Example of this in action when your debit card is declined when purchasing an item outside of your normal spending habit and a security call is required by you to confirm that you are the one making the purchase.
Re-examination of the assumptions
The standard assumption that has been presented so far is that knowledge is derived from information which in turn is processed data. Upon further study the term "knowledge" now seems less defined and unlikely to be the product of a translation from data to information to knowledge, constricted by a distinct linear progression. Stenmark(2002) identifies this a problem when he states that many texts provide make an over simplification of the relationship between data, information and knowledge.
"it may not be possible to correctly state the true relationship between these entities, there is nothing that indicates that is should be linear"
Tuomi (2000) makes two comments on the linear viewpoint, the first being:
"raw data do not exist, and that even the most elementary perception is already influenced by potential uses, expectations, context, and theoretical constructs"
This implies that even though standard practice is to assume data as the starting point from which to build knowledge, the data itself must have already been interpreted in some way to actually exist; it did not just magically materialise. A second point from Tuomi (2000) states:
"data emerge only after we have information, and that information emerges only after we already have knowledge"
Tuomi has presented this idea within his "reversed hierarchy" model and is taken in the context of the construction of a database and Stenmarks findings also relate to an information systems context. Although this may have some bearing, the concepts could be transferable to other situations or contexts.. If we take both the common idea of presenting the progress of data through to knowledge as a linear representation and combine that with Tuomi's reverse hierarchy then we could argue the idea of knowledge positioned within a cycle of events where each entity within the cycle is dependant on the other.
I have tried to express a simplification of that in the following diagram:( you may need to click on it to expand the diagram fully)
The diagram identifies areas of study with knowledge at the intersection.
On the left hand side I have included the original idea of data, information and knowledge. This can be expanded to illustrate that from knowledge it is possible to be redirected back to more specific information or the need for more specific data which in turn reinforces that knowledge. This is an attempt to incorporate the "reversed hierarchy" model proposed by Tuomi(2000). The right hand side also illustrates that knowledge may either create or give structure to new data, or information, or that data or information from another area of study can reflect upon and thus increase the breadth and depth of knowledge. This may then be fed back into the left hand side loop. At all times the relationship between data information and knowledge is cyclical rather than linear.
Example in practice
"it may not be possible to correctly state the true relationship between these entities, there is nothing that indicates that is should be linear"
Tuomi (2000) makes two comments on the linear viewpoint, the first being:
"raw data do not exist, and that even the most elementary perception is already influenced by potential uses, expectations, context, and theoretical constructs"
This implies that even though standard practice is to assume data as the starting point from which to build knowledge, the data itself must have already been interpreted in some way to actually exist; it did not just magically materialise. A second point from Tuomi (2000) states:
"data emerge only after we have information, and that information emerges only after we already have knowledge"
Tuomi has presented this idea within his "reversed hierarchy" model and is taken in the context of the construction of a database and Stenmarks findings also relate to an information systems context. Although this may have some bearing, the concepts could be transferable to other situations or contexts.. If we take both the common idea of presenting the progress of data through to knowledge as a linear representation and combine that with Tuomi's reverse hierarchy then we could argue the idea of knowledge positioned within a cycle of events where each entity within the cycle is dependant on the other.
I have tried to express a simplification of that in the following diagram:( you may need to click on it to expand the diagram fully)
The diagram identifies areas of study with knowledge at the intersection.
On the left hand side I have included the original idea of data, information and knowledge. This can be expanded to illustrate that from knowledge it is possible to be redirected back to more specific information or the need for more specific data which in turn reinforces that knowledge. This is an attempt to incorporate the "reversed hierarchy" model proposed by Tuomi(2000). The right hand side also illustrates that knowledge may either create or give structure to new data, or information, or that data or information from another area of study can reflect upon and thus increase the breadth and depth of knowledge. This may then be fed back into the left hand side loop. At all times the relationship between data information and knowledge is cyclical rather than linear.
Example in practice
Neef et al (1998) suggest there are four different types of knowledge:know-what; know -why; know-how and know -who. What I found interesting was the crossover between what can be identified as information and knowledge within their explanations.
Relating this to a particular context this could be illustrated by managing the construction of a web development project within an web consultancy practice:
what should the website be constructed from? e.g. Colour, number of pages, fonts used. Complex areas needing knowledge to assemble and anlayze information translated from facts.
why should the website be arranged in this way? e.g. Knowledge of marketing principles and human behaviour that identify the way a person uses a website and why you should present it in a certain way.
how should the project be structured? e.g. Knowledge of time scales, whether or not to create a prototype, managing the customer to ensure that they are informed of progress or potential problems.
who should be involved and to what extent? e.g. Selecting a project based team to complete a project which may involve identifying lack of skills in a particular area so that contractors may be introduced for the project duration.
Consider the viewpoint of a consultant managing the project. The knowledge that a consultant has will need to be presented and communicated to in order to successfully complete the project. This process will include analysing ideas and knowledge concepts that can then be expressed and exchanged in a format such as documents: information. Glushko and McGrath(2008) identify this process as document engineering. The information may need to be stored and consequently split into data Tuomi(2000) . The data itself may then be used to create new information that can create further knowledge and creative ideas to complete the project successfully or infuse it with a more inspirational approach previously unexplored.
Conclusion
Although these ideas have been expressed from the bias of computing context, there seems a strong case for the relationship between data, information and knowledge not to be seen not as a a one way relationship but rather as a cycle: observation, analysis, sense making and deconstruction. Each iteration providing deeper insight and avenues for exploration and application within existing or new areas of study.
References
Bocij, P., Chaffey, D., Greasley, A., and Hickie, S. (2006). Business Information Systems, 3rd edition. Harlow, Essex: Pearson Education
Relating this to a particular context this could be illustrated by managing the construction of a web development project within an web consultancy practice:
what should the website be constructed from? e.g. Colour, number of pages, fonts used. Complex areas needing knowledge to assemble and anlayze information translated from facts.
why should the website be arranged in this way? e.g. Knowledge of marketing principles and human behaviour that identify the way a person uses a website and why you should present it in a certain way.
how should the project be structured? e.g. Knowledge of time scales, whether or not to create a prototype, managing the customer to ensure that they are informed of progress or potential problems.
who should be involved and to what extent? e.g. Selecting a project based team to complete a project which may involve identifying lack of skills in a particular area so that contractors may be introduced for the project duration.
Consider the viewpoint of a consultant managing the project. The knowledge that a consultant has will need to be presented and communicated to in order to successfully complete the project. This process will include analysing ideas and knowledge concepts that can then be expressed and exchanged in a format such as documents: information. Glushko and McGrath(2008) identify this process as document engineering. The information may need to be stored and consequently split into data Tuomi(2000) . The data itself may then be used to create new information that can create further knowledge and creative ideas to complete the project successfully or infuse it with a more inspirational approach previously unexplored.
Conclusion
Although these ideas have been expressed from the bias of computing context, there seems a strong case for the relationship between data, information and knowledge not to be seen not as a a one way relationship but rather as a cycle: observation, analysis, sense making and deconstruction. Each iteration providing deeper insight and avenues for exploration and application within existing or new areas of study.
References
Chaffey,D. (2006). E-Business and E-Commerce Management 3rd edition , Harlow, Essex: Pearson Education.
Glushko,R., McGrath,T,. (2008) Document Engineering:Analyzing and Designing Documents for Business Informatics and Web Services, Massachusetts,USA: MIT Press
Kakabadse, N., Kakabadse, A. and Kouzmin, A. (2003), “Reviewing the knowledge management literature: towards a taxonomy”, Journal of Knowledge Management, vol.7, no.4, pp. 75-91.
Laudon, J., Laudon, K. (2007). Management Information Systems- Managing the digital firm, 10th edition. Harlow, Essex: Pearson Education
Neef,D.,Siesfeld,G.,Cefola,J.(1998), The Economic Impact of Knowledge,Butterworth-Heinemann,available at http://books.google.co.uk/books?id=ItnyIjP6uUYC&dq=knowledge+definition&lr=&source=gbs_summary_s&cad=0, [Accessed 2nd February 2009]
Stenmark, D. (2002). "Information vs. Knowledge: The Role of intranets in Knowledge Management". In Proceedings of HICSS-35, IEEE Press, Hawaii, January 7-10, 2002, available at http://www.viktoria.se/~dixi/km/chap3.htm, [Accessed 10th February 2009]
Tuomi, I., (2000). Data Is More Than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory, Journal of Management Information Systems, 16(3), pp. 103-117.
Turban, E., Leidner, D.,McLean, E.,Wetherbe,J.(2006) Information Technology for Management, 5th Edition, Massachusetts: Wiley
I like your diagram representing data, information and knowledge. In the literature the definition of data and information are fairly well agreed but knowledge seems to mean different things depending on your perspective. The idea that knowledge can create new data or information which in turn creates new knowledge seems very plausible. This seems to explain how knowledge increase over time.
ReplyDeleteI also posted a article and have gone through many articles of our colleagues under this specific topic, but this is absolutely amazing way of present. Your diagram speaks 100s of words and you save some space for your further explanation. I think this article is a result of good knowledge management and innovative thinking.
ReplyDeletei really liked your way of thinking and your diagram on this post. The four different kinds of knowledge 'know-what; know -why; know-how and know -who' reminded me of Rudyard Kipling's poem which Elli refered to at the first lecture of quality management. The poem refers to what, why, who, how, where and when. It was an interesting coincidence but do you think the four types of knowledge could be expanded to 6 to include the know-where and know-when?
ReplyDeleteAfter reading a few literature on KID, I will agree with the analogy in your conclusion that data, information and knowledge could mean different things to different people. Before my exposure to KM, my mindset was such that data can only mean the same thing to everyone and the same applied to my views on information and knowledge. It indeed reflects the observers interaction with the entity.
ReplyDeleteThanks for the comments. Stupidly I started to edit and lost my original analogy but I have spent time on trying to solidify my ideas and provide more supporting evidence which I have since found. I have also tried to tie the example more closely to my thoughts on KID as a cycle rather than a linear progression. I hope the changes have made sense!
ReplyDeleteChris...thanks for your comment on the what, why , how , who. I would agree where and when are also key points to be considered. These two could in fact be elevated in that where and when are crucial factors in the determination of the value of knowledge and the best point to apply it: wisdom.
ReplyDeleteHi Francis... I looked at your ideas and it got me thinking about my Gas Bill which I received today (no I don’t want you to pay for it...! hehehe...)
ReplyDeleteThey purely charged me on an estimate reading based on last year data and information... the management or decision makers are basing their knowledge on past data and information, therefore the knowledge they have is out-dated...? (Value or No Value)
This has been in the news recently, with customers furious that utility companies are doing this and most cases overcharging... (this is generally done to cost cut in sending meter readers)
What I am trying to say is that some organisations have to make decisions on data and information that is simply outdated, which could harm the business.
In the utility sector they depend on data collection and presenting the information to stakeholders, which triggers action from customer’s side to cut back or from the utility company to offer better services to stay competitive...!
So the knowledge we have today, might be worthless tomorrow...?
;-)
Hi Ravi, thanks for your comments, I like the gas bill perspective ! I would suggest that in this case the knowledge was not useless however the ability for the company to respond quickly enough to the environment was probably at fault. I would argue that there is no such thing as useless knowledge however the application of any knowledge that you accumulate is much more important. Seemingly useless knowledge applied in an original and positive way can become extremely valuable.
ReplyDeleteI agree that outdated data can cause a problem but I would suggest this is more an area of information quality and specifically the time dimension: timeliness and frequency which have been their undoing. Had better processes been in place then they would have been able to respond more quickly to market trends and prevent the customer backlash caused by estimated bills. Knowledge gained from the analysis of customer feedback could have been folded back into the system to adjust the frequency of actual meter readings and alerted the organization to the need of employing more staff to improve customer satisfaction.