Category: Wisdom

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 et.al., 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 et.al., 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 et.al, 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 et.al, 2011). This differs greatly from traditional assets and resources, who cease to exist the more it is used (Moody & Walsh, 1999).

Summary

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 et.al, 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.

/Drill

References:

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.

Effectual Reasoning – A piece of Wisdom for both Startups and Life

As we all know, lately there is a lot of hype – “fuzz an buzz” – related to the subject of entrepreneurship. Damn, nowadays it seems like everybody is working with or starting their own startups. Me being a notorious Devil’s advocate and a supporter of long-term thinking, have a somewhat of a critical standpoint toward this phonomenon of just striking gold and doing it fast. In my opinion, beautiful visions and mision are worth far more than their weight in gold.

The other day I was discussing with a very inspiring professor of Entrepreneurship. To challenge him and this academical subject, I decided to ask the professor for the most intresting and appealing idea wihtin his field that he have come accross. My expectations was to yet again to see emperor’s new clothes. Though as it turned out, I couldn’t have been more wrong.

So what did the professor give me? It was a paper on the philosophy of “Effectual reasoning”.

What is Effectual Reasoning?

Effectual vs. Causal Thinking

As the word “effectual” is inverse of “causal” and originates from the science of entrepreneurship. Causal rationality, or predictive reasoning, “begins with a pre-determined goal and a given set of means, and seeks to identify the optimal – fastest, cheapest, most efficient, etc. – alternative to achieve the given goal” and is often applied in strategic thinking (Sarasvathy, 2001). Thus the underlying logic is that the future is both predictable and controllable.

“Causal thinkers are like great generals seeking to conquer fertile lands (Genghis Khan conquering two thirds of the known world)“

 

However, effectual reasoning differs in the sense that it does not begin with a specific goal and that the future is unpredictable, but controllable and still to be made (Sarasvathy, 2001). According to Sarasvathy (2001) effectual reasoning “begins with a given set of means and allows goals to emerge contingently over time from the varied imagination and diverse aspirations of the founders and the people they interact with”. Thereby effectual reasoning adjusts and adapts goals according to the surrounding contingencies and unfolding unexpected events.

“Effectual thinkers are like explorers setting out on voyages into uncharted waters (Columbus discovering the new world)”

It is clear that effectual reasoning is “inherently creative” philosophical approach that breaths execution and thus demands something more beyond the regular and domains specific skills and abilities, such as imagination; spontaneity, risk-taking and salesmanship (Sarasvathy, 2001). These features can be compiled into five conceptual principles.

The Five priciples of Effectual Reasoning

The first principle goes by the name “bird-in-hand” and refers to the process where the individual inspect their own means. In general, the means are divided into three categories that involves the audit of: “who they are” – their abilities and traits; “what they know” – experiences and knowledge; and, “whom they know” – social and professional networks (Effectuation, 2011; Sarasvathy, 2001).

The second principle is “affordable loss” which essentially is an evaluation of the potential downside risk for the overall project, if the worst case scenario would occur (Effectuation, 2011). The idea with the affordable loss principle is that it pre-established. That is how much resources is allowed and affordable to lose for a given project. If the pre-determined limit is reached, then the project is automatically rejected.

The third principle is called “lemonade” and revolves around the element of surprise (Effectuation, 2011). This means that contingencies and surprises are the norm for all projects and therefore ought to be welcomed and leveraged, instead of perceived as a misfortune.

The fourth principle is named “patchwork quilt” principle and is about building and establishing strategic partnerships (Effectuation, 2011). Thus, this principle encompasses trust and how to leverage this through collaboration.

The final principle is “pilot-in-the-plane” and is the combination of the previous four principles into an entity that forms “the belief that the future is neither found nor predicted, but rather made” (Effectuation, 2011). In a nutshell, the effectuation as philosophy is about iterating and leveraging competences and means in the most effective and adaptive way.

Thus, the priciples can be summarised in the following picture:

FivePriciples

Serendipities – The Core of Effectual Reasoning

The element of surprise is generally perceived as an undesirable and avoided state for any human-being and situation. This Despite the overlooked and powerful opportunities that serendipities potentially can provide with. A serendipity is defined by oxford dictionary as “the occurrence and development of events by chance in a happy or beneficial way”. In other words, serendipities is an actual word for unpredictable occurances and situations that provide with the most insightful knowledge, wisdom, and happiness for us all. Thus, serendipities are the core of effectual reasoning. (To read more about it, check this blog post).

“Serendipities are the occurrence and development of events by chance in a happy or beneficial way”.

On that note, embrace surprises and unpredictive event. These might be the things/opportunities you are actually looking for. And apply the philosophy of effectual reason to make and form your future. Do it. Do it now.

/Drill

 

References:

Effectuation, 2011. Principles of Effectuation. [Online] Available at: http://www.effectuation.org

Sarasvathy, S.D., 2001. What makes entrepreneurs entrepreneurial?.

 

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?”

Data

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

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

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

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).

/Drill

 

References:

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.