Seven Laws of Information – A Foundation for “Digital Wisdom”

186721518Information is increasingly being perceived as a valuable asset in today’s modern society, de facto that in some occurrences information is by far the most valuable asset of a business and its activities. This tendency is refered often with the quote: “data is the new oil”.

What is problematic about Information is that it has an intangible character embedded to it, which makes it very hard to evaluate its real actual nominal value. Moody and Walsh (1999) recognizes this issue and introduces seven laws or postulates associated with the natures of information in order to understand its underlying value and how information differs from regular assets. I believe that by understanding the very nature of information (combined with the DIKW – hierarchy, which was my second blog post), one is able to become more wise, but also able to obtain a new form and dimension of  wisdom, which I would like to call it “digital-wisdom”.

1. Infinitely shareable

The first law states that information is infinitely shareable. Essentially this means that information has the unique ability of being shared among numerous parties, “without consequent loss of value to each party” (Moody & Walsh, 1999). The fact that information can be unlimitedly replicated and shared, with no real additional costs, makes it possible for many parties to use it at the same time. However, the duplication of information does not mean an increase of financial value of the information set (Uckelmann et. al., 2011). In contrast, information is very different from regular assets because assets are appropriable, i.e. you either have it or you do not (Moody & Walsh).

2. The value increases with use

The second law is that the value of information increases with use. This is intriguing because usually resources deprecate with use. In spite of this statement, it is important to point out that information does not provide any value if it is not used at all (Uckelmann et. al., 2011). Thus Moody and Walsh (1999) concludes that information in itself “has no real value on its own” and is in it unused form seen rather as a liability.

3. Perishability

The third law indicates that information is perishable. In practice this means that information depreciates over time and can thus be compared to any other asset (Moody & Walsh, 1999). The useful lifetime of information is therefore often relatively short, though it can be extended to a certain point when used for decision-making (Moody & Walsh, 1999).

4. Accuracy

The fourth law postulates that the value of information increases with accuracy. It is apparent that the more accurate information, the more valuable it beholds. However, 100 percent accuracy is rarely required in a business context, while a 100 percent accuracy is a must in some cases, such as maintenance or data banking records (Uckelmann, 2011). In regards of decision-making, the level of “accuracy of information is as important as having accurate information”, because the margins for errors can be incorporated into the context (Haebich, 1997). For this reason, the view extends itself to the fifth law.

5. Synergies of combined pieces of information

The fifth law establishes that the value of information increases when combined with other information. Practically this states that integration or comparison of information generates new additional value. Therefore, it is evident that even a slight standardization of this information integration process will accumulate with high benefits. The inclusion of both identifiers and coding schemes are facilitating computing tools for achieving these benefits (Uckelmann, 2011). Often the integration process is a great hurdle for many organisations, thus it is suggested that the focus ought to be aligned with the pareto principle, or the 80/20 rule, where the idea is that most of output is generated from the 20 percent effort or input (Moody & Walsh, 1999).

6. The more, ain’t better

The sixth law states that more information is not necessary better. Nevertheless, increasing amounts of information do result in more value to a certain extent, however crossing the information overload point causes significant problems and issues (Uckelmann, 2011). Moody and Walsh (1999) points out an interesting empirical paradox related to information and decision-making, which is that the perceived value of information continue to increase even after the information overload point has been reached. The reason for this delusion is most likely related to the misconception that more information helps to avoid mistakes and reduces the uncertainty involved (Moody & Walsh, 1999).

7. Not depletable

The seventh and last law appoints that information is not depletable. At heart this refers to the fact that information is self-generating – the more one use it, the more one obtains of it (Uckelmann, 2011). This differs greatly from traditional assets and resources, who cease to exist the more it is used (Moody & Walsh, 1999).


By understanding the very nature of information, it is evident that it is a very misunderstood and poorly managed asset, especially in terms of duplication; lack of standardisation; and lack of attention to its quality (Moody & Walsh, 1999). If other assets were managed in a similar manner as information (e.g. financials or people) then firms would most likely go out of business. Therefore, in order to manage information properly, one needs to understand its many unique features and facets (Alberts, 2001).

The “digital wisdom”, as a concept, is in its very early stages and needs to be futher clarified. For now it will answer to the simple question: “know-why?“, when associated with digital and other heavily data oriented technologies. And evidently, the seven laws of information helps to clarify this matter to some extent, though there is still a long way to go. Finally, my future belief is that new regulations, such as GDPR, will empahsize the importance of “digital wisdoms” for both consumers and firms. This because “digital wisdom” also comprises both ethics and foresight within the context of digital data, which clearly is needed in the future.



Alberts, D.S., Garstka, J.J., Hayes, R.E. and Signori, D.A., 2001. Understanding infor mation age warfare. ASSISTANT SECRETARY OF DEFENSE C3I/COMMAND CONTROL RESEARCH PROGRAM  WASHINGTON DC, pp. 9-17.

Moody, D.L. and Walsh, P., 1999. Measuring the Value of Information – An Asset Valuation Approach. ECIS (pp. 496-512).

Uckelmann, D., Harrison, M. and Michahelles, F., 2011. An architectural approach towards the future internet of things (pp. 260-263). Springer Berlin Heidelberg.

DIKW Hierarchy – Understanding the Concept of Wisdom

A fundamental corner stone for all information and knowledge literature is the data-information-knowledge-wisdom (DIKW) hierarchy model. A beloved child has many names, thus the hierarchy is also called: information hierarchy, knowledge pyramid and wisdom hierarchy. Essentially this model involves four entities of: data, information, knowledge and wisdom. These are ordered in a hierarchical structure (Figure above) with respect to one another in order to explain the relationship and transformation process between these various entities (Rowley, 2007). The idea with a hierarchical structure is that the higher types of entities “includes the categories that fall below it” (Ackhoff, 1989).

The figure in the beginning of the post is an illustration of the DIKW pyramid, which includes vectors that indicates the characteristics in the aspect of meaning and value (Rowley, 2007).

According to Aven (2012) the origins of the DIKW hierarchy in its current state and form can be traced back to the 80s, however, the first appearance of the model was registered in T.S. Eliot’s poem “The Rock” in 1934 with the following citation:

“Where is the life we have lost in living?

Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in the information?”


The lowest element of the DIKW pyramid is data. Data is in its purest form is plain symbols, such as numbers or letters, and is per definition unorganized or unprocessed and lacks whatsoever any meaning or value (Rowley, 2007). Therefore, data can be seen as observable and measurable properties that are represented in symbols.


Information is defined as data that is given a context or data that have been organized into a structure (Rowley, 2007). The fundamental principle is that a refining process has been applied to the data, which now evolves to information that possesses a meaningful purpose or value.


Knowledge as a concept is far more complex to comprehend than the other lower elements. Aven (2012) defines knowledge as structured and organized information as a result from cognitive processing and validation. Other definitions go into similar abstractions that knowledge is generated as a mix of both data and information, where the human contribution of experiences and rules increases over time. An oversimplified suggestion is that knowledge answers to the “how” questions (Cooper, 2014). Despite the differences, all definitions agree that knowledge can be divided into two categories: tacit and explicit knowledge. Where explicit knowledge can be transferred and documented, while tacit knowledge cannot because it is part of an individual’s human mind (Bocij, Chaffey & Hickie, 2003). All in all, Rowley (2007) summarises this well that all definitions of knowledge combine a mix of information, understanding, capability, experience, skills and values.


Wisdom is arguably the most elusive of all these four elements and concepts. According to Ackoff (1989) wisdom is the only element that concerns with the future, while the other elements deal with the past. A similar view is shared with Awad and Ghaziri (2004) who suggest that “Wisdom is the highest level of abstraction, with vision foresight and the ability to see beyond the horizon”. Rowley (2007) elaborates the definition even further and concludes that wisdom involves moral and ethics, such as human intuition, understanding, interpretation and actions. Cooper (2014) likewise defines wisdom as “an extrapolative process which includes knowledge in an ethical and moral framework”. Another definition by Perwitt (2002) presents wisdom as a double loop of learning in reflection to the three earlier element stages, though an integration of both mind and soul are required in order to obtain wisdom. Therefore, wisdom has a clear element of spirituality embedded to it.

On that note….

Finally, the DIKW pyramid can be well summarised and described by Zeleny’s (2005) appealing metaphor as follow: “know-nothing” (data), “know-what” (information), “know-how” (Knowledge) and “Know-why” (Wisdom).




Ackoff, R.L., 1989. From data to wisdom. Journal of applied systems analysis, 16(1), pp.3-9.

Aven, T., 2013. A conceptual framework for linking risk and the elements of the data information–knowledge–wisdom (DIKW) hierarchy. Reliability Engineering & System Safety, 111, pp.30-36

Awad, E.M. and Ghaziri, H.M., 2004. Knowledge management, 2004. ed: Prentice-Hall, Upper Saddle River, New Jersey.

Cooper, P., 2014. Data, information, knowledge and wisdom. Anaesthesia & Intensive Care Medicine, 15(1), pp.44-45.

Rowley, J.E., 2007. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of information science. 33(2), pp. 163–180

Zeleny, M., 2005. Knowledge-information autopoietic cycle: towards the wisdom systems. International Journal of Management and Decision Making, 7(1), pp.3-18.