Blog Assignment

1.Define the meaning of the terms data, information and knowledge according to Thomas Davenport's Information Hierarchy (1997).
Answer:
a. data:Thomas Davenport and Larry Prusak’s notion that ‘data’ are ‘discrete,objective, facts about events’ (1998:2). In organisations, data represent‘structured records of transactions’ (Ibid.). The important point here is thatthere is no inherent meaning in data. Data may be the raw material ofdecision making but they cannot, alone, tell you what to do.[1]

b. information is distinctly different from data because it has meaning.Indeed, this is where the hierarchy represented above begins to make somesense, because we can conceptualise
‘information’ as simply ‘data investedwith meaning’ (i.e., information = data + meaning).Peter Drucker makes thesame point by noting that information is ‘data endowed with relevance andpurpose’. Charles Jonscher’s is even more precise when he talks about how‘Information is data interpreted by the person who is being informed’(Johnscher, 2000:36).[2]

c. knowledge is broader, deeper, andmore useful than information. It also underscores the value of the hierarchyoutlined above because, just as information is data distilled and interpreted,knowledge is information distilled and interpreted.In the same way that weadd ‘meaning’ to data to transform it into information, we add purpose toinformation to transform it into knowledge (i.e., knowledge = information +purpose).knowledge involves both a dimension of ‘resolution’ and of ‘action’. Itinvolves resolution in the sense that, while you may have conflictinginformation, it is unusual for some one to say they have conflicting knowledge.[3]

2. What are the characteristics of the above terms?
a. data:from a information management perspective, data is relatively easy to capture, communication and store. Nothing is lost when ot's represent as a string of bits, which certainly comfort to IT personnel.[4] All organisations need data, and some organisations are entirely relianton them. However, more data are not always better. Too many data make ithard to distinguish the useful data from the noise.[5]

b. information: information need some unit analysis, and it is much harder to transfer with absolute fidelity. [6] Information, then, can be thought of as a message that is intended to have animpact on the receiver. It adds meaning to data in a number of ways, withsome of the most common ones being through:
Contextualisation (why were the data gathered in the first place?)
• Categorisation (what are the key components of the data?)
• Calculated (for instance, through statistical summaries)
• Corrected (involving the discovery and removal of errors)
• Condensed (with the main points summarised in a more concise form)[7]

c. knowledge:it can embeded in machines, but it is tough to categorize and retrieve effectively. Davenport and Prusak (1998:6-7) suggest that some of the components thatwe might all agree contribute to ‘knowledge’ include:
Experience: Knowledge develops over time. This means experienceprovides a historical perspective from which to view and understand newsituations and events.
Practical Utility: Knowledge means being able to distinguish what ‘should’work from what really does. It is where ‘the rubber meets the road’. It is the difference between what is taught in business schools and what reallyhappens in the business world.
Speed: The knowledgeable are able to recognise patterns and provideshort cuts to solutions rather than build one from scratch every time. As aresult, knowledge offers supercharged problem solving.
Complexity: Knowledge is about dealing with complexity. This means it iscomfortable with the ambiguity of real-world situations. By denyingcomplexity, those without knowledge offer simple solutions that invariablyfail.
Evolving: Because the key to knowledge is knowing what you don’t know,the knowledgeable are also able to refine their knowledge through furtherexperience, study, and learning. Knowledge either examines itself andevolves or it is dogma.[8]

3. Give and example for each term mentioned above.
data:Davenport andPrusak use the example of a customer buying some gas for her car: The datawill record how many litres she bought, when she bought them, how much shepaid, and how she paid. But the data cannot tell us anything about why shewent to that gas station and not another, nor how happy she was with theservice, nor whether she is likely to go back to purchase more gas in thefuture.[9]

information:For instance, putting thedata into context is something only people can do. As we will see, it is this‘social construction’ element of information that has important consequencesfor knowledge management.[10]

knowledge:One of themost famous philosophers of the twenty century, Ludwig Wittgenstein,illustrated this general problem with definitions by talking about the concept of a ‘game’. Wittgenstein argued that it was not possible to establish thenecessary and sufficient conditions for an activity to be judged a ‘game’because ‘when one tries, one invariably finds an activity that one’s definitionincludes but that one would not want to count as a game, or an activity thatthe definition excludes but that one would want to count as a game’ (inChalmers, 1982:93).[11]

4. Is there any possibility of a fourth level of Information Hierarchy? Elaborate.
Yes, there is the possibility of a fourth level of Information Hierarchy, which is the Wisdom.wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.).It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad.[12]

Rference:
[1],[2],[3],[5],[7],[8],[9],[10],[11] www.nodoubt.co.nz/articles/data.pdf (March 2003) Data, Information, and Knowledge Auckland, New Zealand
[4],[6] Thomas H. Davenport, Laurence Prusak (1997) Mastering the Information and Knowledge Environment, Information Ecology. pp10-11 Oxford University Press US
[12] Gene Bellinger, Durval Castro & Anthony Mills(2004) Data, Information, Knowledge, and Wisdom