14 Point Criteria for Defining The Value of Information (VoI)
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The Value of Information (VoI) is a concept from decision analysis: how much answering a question allows a decision-maker to improve its decision. Like opportunity cost, it’s easy to define but often hard to internalize.
The VoI is sometimes distinguished as Value of Perfect Information, also called Value of Clairvoyance (VoC), and Value of Imperfect Information. They are closely related to the widely known expected value of perfect information and expected value of sample information. Kindly Note — VoI is not necessarily equal to “value of decision situation with perfect information” — “value of current decision situation” as commonly understood.
For example, consider two army commanders in a war at two different battle fronts. One of the commanders has an adequate supply of ammunition, food ration, and drinking water (more than he could desire) for his battalion and another has exhausted his supplies. If one were to approach these two individuals with information about a “drinking water well” in the surrounding, such information will obviously have greater value for the one who has exhausted his water supply. For the one who is thirsty, this information is the most valuable piece of information for him at that point of time as it will determine if his troops will survive. If by chance the information reaches this thirsty battalion late and his troops start dying out of thirst, then the value of the same information becomes zero. So we can see that the same information can have a different value for different people at different points in time. Hence, it will be quiet fair to conclude that value of information is relative. There is no absolute value of information.
Data is all about the collection of facts in a capsulated form. Multiple-Data capsules form Information stack which in turn creates knowledge. In a layman’s term — the knowledge can be described as a mix of information, understanding, capability, experience, skills, and values. The real question is what we derive out of that knowledge? And, the answer is Wisdom, which can be described as the ability to think and act using the knowledge which has been built up on the information stack and data capsules. [1]
When applying the Rowley’s DIKW (Data-Information-Knowledge-Wisdom) pyramid [2] in a business context, the process of moving up from data to wisdom could be described as business intelligence, mainly because business data has the potential to result in business knowledge and wisdom.
Image Source: DIKW pyramid: data, information, knowledge, and wisdom / Source: Wikipedia
Criteria which defines the value of information*:
1. Accuracy/precision/correctness — Information should be precise and close to reality. Also, information should be free of distortion, bias, or errors.
2. Consistency — The information should be free of contradictions or convention breaks.
3. Applicability — Information should be able to be applied directly.
4. Clarity/format — Information should be well, understandable and clearly presented to the user.
5. Comprehensiveness/completeness — The scope of information should be adequate. There should be not too much nor too little
information
6. Conciseness — The information should be to the point and should void of unnecessary elements.
7. Convenience — The information should correspond to the user’s needs and habits
8. Currency — The information should up-to-date and not obsolete
9. Traceability — The background of the information should be traceable, such as the used data, author(s)
10. Accessibility — The information should be continuously accessible without not too many obstructions
11. Flexibility — The information should be able to adapt to (the changing demands of) the user?
12. Integration — The system should allow data to be integrated from various sources
13. Reliability — The system operation should be reliable
14. Timeliness/Speed — The information should be processed and delivered rapidly without delays. The information should also match the user’s working pace
* Most important criteria joined from Eppler (2003) [3] and Wixom & Todd (2005)[4]
When evaluating information-based products, the efficiency, effectiveness, context coverage can be measured by using quantitative data. The other criteria are mainly qualitative and subjective. Do note, the subjective value approach varies widely with individuals. In the subjective valuation of information, no probabilities are calculated. The subjective value of information is the person’s (receiver’s) comprehensive impression about the information content. To measure all criteria quantitatively surveys with Likert scales are required for instance.
But, when assessing the value of future information products, it can be challenging because the information cannot be used yet. When creating information products it is recommended to keep the usability criteria in mind. For some criteria, it is possible to measure their value by making estimations. Thus, we can say that the issue of the value of future information is a complicated one.
However, the normal mathematical and economical explanation of the VoI suggests that if an event occurs whose expectation was low and information of its occurrence is known then such information is valuable. For example, let us say that the reader of this text, a soldier manning the Doka La pass gets the information that China is going to escalate its troop movement across the pass and epicenter of the conflict will be the very spot on which he is located, then that information is more valuable to him than the information (say) that he has to report at officer’s mess for a dinner event with his colleagues. In the former case the information is more valuable to him as he is not expecting it but in the latter case he already knows the information with certainty and expects it fully and hence the value of such information is less. All decision mechanisms work on this model of information. This suggests the value of information of an event is the negative logarithm of the probability of occurrence of the event. Therefore, the more unlikely the event the more its information tends to have higher value, if communicated correctly. This is also exhibited in our behavior as eons of evolutions have shaped us in a manner that we tend to attach more value to unlikely events.
Conclusion:
When computing VoI, we can’t just consider one possible answer, but all possible answers considering their relative likelihood. Secondly, If we don’t expect to change in our decision after receiving a certain information, or if we think that the expected value of the information is lower than the cost of the information, it would be better not to run the tests as we can stop computing as soon as we find a positive outcome good enough and likely enough that the VoI so far is higher than the cost. Learning the facts is often fun, but for it to fit into VoI some decision has to depend on that fact. When playing poker, we know what all are- and while that may alter your enjoyment of the hand, it won’t affect how any of the players play. We as a watcher shouldn’t pay much for that information, but the players would pay quite a bit for it because in high-stakes poker games, the VoI can get rather high.
Recommend for Further Reading:
On the Concept of the Value of Information in Competitive Situations by Jean Pierre Ponssard, Ecole Polytechnique, MANAGEMENT SCIENCE Vol. 22, №7, March 1976 | Copyright © 1976, The Institute of Management Sciences | Download the Pdf
Decision Analysis 2, The Value of Information, May 2, 2013 | Download the Pdf
References:
[1] [4] Top, J (2015). Information value in a decision making context — A case study and definition of a measurement model, Faculty of Science, Institute for Computing and Information Sciences, Radboud University Nijmegen.
[2] Rowley, J. E. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of information science, 33(2), 163–180.
[3] Eppler, M. J. (2003). Managing information quality: Increasing the value of information in knowledge intensive
products and processes. Berlin: Springer
[4] Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information systems research, 16(1), 85–102.
