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Sensemaking and Analytics for Organizations

Archive for the ‘Knowledge Management’ Category

“The Heretic’s Guide to Best Practices” wins bronze at the 5th Annual Axiom Business Book Awards.

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I’m delighted to announce that the book that Paul Culmsee and I published recently has been awarded a bronze medal at the Axiom Business Book Awards for 2012, under the category Operations Management/Lean/Continuous Improvement.

http://www.axiomawards.com

We are truly honoured that the panel found our efforts worthy of an award.

If you are interested in finding out more about the book, please check out the review by Shim Marom and the one by Scott McCrickard.

There are also a number of customer reviews on Amazon.

http://www.amazon.com/Heretics-Guide-Best-Practices-Organisations/dp/1938908406

The Heretic’s Guide is a self-published book with no big publisher marketing behind it, so we’d greatly appreciate your spreading the word!

On the ineffable tacitness of knowledge

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Introduction

Knowledge management (KM) is essentially about capturing and disseminating the know-how,  insights and experiences  that exist within an organisation.  Although much is expected of KM initiatives, most end up delivering document repositories that are of as much help in managing knowledge as a bus is in getting to the moon. In this post I look into the question of why KM initiatives fail, drawing on a couple of sources that explore the personal nature of knowledge.

Explicit and tacit knowledge in KM

Most KM  professionals are familiar with terms explicit and tacit knowledge.  The first term refers to knowledge that can be expressed in writing or speech whereas the second refers to that which cannot.  Examples of the former include driving directions (how to get from A to B) or a musical score; examples of the latter include the ability to drive or to play a musical instrument.  This seems reasonable enough: a musician can learn how to play a piece by studying a score however a non-musician cannot learn to play an instrument by reading a book.

In their influential book, The Knowledge-Creating Company, Ikujiro Nonaka and Hirotaka Takeuchi proposed a model of knowledge creation1  based on their claim that:  “human knowledge is created and expanded through social interaction between tacit knowledge and explicit knowledge.” It would take me too far afield to discuss their knowledge creation model in full here – see this article for a quick summary.  However, the following aspects of it are relevant to the present discussion:

  1. The two forms of knowledge (tacit and explicit) can be converted from one to the other. In particular, it is possible to convert tacit knowledge to an explicit form.
  2. Knowledge can be transferred (from person to person).

In the remainder of this article I’ll discuss why these claims aren’t entirely valid.

All knowledge has tacit and explicit elements

In a paper entitled, Do we really understand tacit knowledge, Haridimos Tsoukas discusses why Nonaka and Takeuchi’s view of knowledge is incomplete, if not incorrect. To do so, he draws upon writings of the philosopher Michael Polanyi.

According to Polanyi, all knowledge has tacit and explicit elements. This is true even of theoretical knowledge that can be codified in symbols (mathematical knowledge, for example). Quoting from Tsoukas’ paper:

…if one takes a closer look at how theoretical (or codified) knowledge is actually used in practice, one will see the extent to which theoretical knowledge itself, far from being as objective, self-sustaining, and explicit as it is often taken to be, it is actually grounded on personal judgements and tacit commitments. Even the most theoretical form of knowledge, such as pure mathematics, cannot be a completely formalised system, since it is based for its application and development on the skills of mathematicians and how such skills are used in practice.

Mathematical proofs are written in a notation that is (supposed to be) completely unambiguous.  Yet every   mathematician will understand a proof  (in the sense of its implications rather than its veracity) in his or her own way.  Moreover, based on their personal understandings, some mathematicians will be able to derive insights that others won’t. Indeed this is how we distinguish between skilled and less skilled mathematicians.

Polanyi claimed that all knowing consists at least in part of skillful action because the knower participates in the act of understanding and assimilating what is known.

Lest this example seem too academic, let’s consider a more commonplace one taken from Tsoukas’ paper: that of a person reading a map.

Although a map is an explicit representation of location, in order to actually use a map to get from A to B a person needs to:

  1. Locate A on the map.
  2. Plot out a route from A to B.
  3. Traverse the plotted route by identifying landmarks, street names etc. in the real world and interpreting them in terms of the plotted route.

In other words, the person has to make use of his or her senses and cognitive abilities in order to use the (explicit) knowledge captured in the map. The point is that the person will do this in a way that he or she cannot fully explain to anyone else. In this sense, the person’s understanding (or knowledge) of what’s in the map manifests itself in how he or she actually goes about getting from A to B.

The nub of the matter: focal and subsidiary awareness

Let me get to the heart of the matter through another example that is especially relevant as I sit at my desk writing these words.

I ask the following question:

What is it that enables me to write these lines using my knowledge of the English language, papers on knowledge management and a host of other things that I’m not even aware of?

I’ll begin my answer by quoting yet again from Tsoukas’ paper,

 For Polanyi the starting point towards answering this question is to acknowledge that “the aim of a skilful performance is achieved by the observance of a set of rules which are not known as such to the person following them.” …Interestingly, such ignorance is hardly detrimental to [the] effective carrying out of [the]  task…

Any particular elements of the situation which may help the purpose of a mental effort are selected insofar as they contribute to the performance at hand, without the performer knowing them as they would appear in themselves. The particulars are subsidiarily known insofar as they contribute to the action performed. As Polanyi remarks, ‘this is the usual process of unconscious trial and error by which we feel our way to success and may continue to improve on our success without specifiably knowing how we do it.’

Polanyi noted that there are two distinct kinds of awareness that play a role in any (knowledge-based) action. The first one is conscious awareness of what one is doing (Polanyi called this focal awareness). The second is subsidiary awareness: the things that one is not consciously aware of but nevertheless have a bearing on the action.

Back to my example, as I write these words I’m consciously aware of the words appearing on my screen as I type whereas I’m subsidarily aware of a host of other things I cannot fully enumerate:  my thoughts, composition skills, vocabulary and all the other things that have a bearing on my writing (my typing skills, for example).

The two kinds of awareness, focal and subsidiary, are mutually exclusive: the instant I shift my awareness from the words appearing on my screen, I lose flow and the act of writing is interrupted.  Yet, both kinds of awareness are necessary for the act of writing. Moreover, since my awareness of the subsidiary elements of writing is not conscious, I cannot describe them. The minute I shift attention to them, the nature of my awareness of them changes – they become things in their own right instead of elements that have a bearing on my writing.

In brief, the knowledge-based act of writing is composed of both conscious and subsidiary elements in an inseparable way. I can no more describe all the knowledge involved in the act than I can the full glory of a  beautiful sunset.

Wrapping up

From the above it appears that the central objective of knowledge management is essentially unattainable because all knowledge has tacit elements that cannot be “converted” or codified explicitly. We can no more capture or convert knowledge than we can “know how others know.”  Sure, one can get people to document what they do, or even capture their words and actions on media. However this does not amount to knowing what they know. In his paper, Tsoukas writes about the ineffability of tacit knowledge.  However, as I have argued,  all knowledge is ineffably tacit. I hazard that this may, at least in part, be the reason why KM initiatives fall short of their objectives.

Acknowledgement and further reading

Thanks to Paul Culmsee for getting me reading and thinking about this stuff again!  Some of the issues that I have discussed above are touched upon in the book I have written with Paul.

Finally, for those who are interested, here are some of my earlier pieces on tacit knowledge:

What is the make of that car? A tale about tacit knowledge

Why best practices are hard to practice (and what can be done about it)


Footnotes:

1 As far as I’m aware, Nonaka and Takeuchi’s model mentioned in this article is still the gold standard in KM. In recent years, there have been a number of criticisms of the model (see this paper by Gourlay, or especially this one by Powell). Nonaka and von Krogh attempt to rebut some of the criticisms in this paper. I will leave it to interested readers to make up their own minds as to how convincing their rebuttal is.

Written by K

February 9, 2012 at 10:30 pm

On the limitations of business intelligence systems

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Introduction

One of the main uses of business intelligence  (BI) systems is to support decision making in organisations.  Indeed, the old term Decision Support Systems is more descriptive of such applications than the term BI systems (although the latter does have more pizzazz).   However, as Tim Van Gelder pointed out in an insightful post,  most BI tools available in the market do not offer a means to clarify the rationale behind decisions.   As he stated, “[what] business intelligence suites (and knowledge management systems) seem to lack is any way to make the thinking behind core decision processes more explicit.”

Van Gelder is absolutely right:  BI tools do not support the process of decision-making directly, all they do is present data or information on which a decision can be based.  But there is more:  BI systems are based on  the view that data should be the primary consideration when making decisions.   In this post I explore some of the (largely tacit) assumptions that flow from such a data-centric view. My discussion builds on some points made by Terry Winograd and Fernando Flores in their wonderful book, Understanding Computers and Cognition.

As we will see, the assumptions regarding the centrality of data are questionable, particularly when dealing with complex decisions. Moreover, since these assumptions are implicit in all BI systems, they highlight the limitations of using BI systems for making business decisions.

An example

To keep the discussion grounded, I’ll use a scenario to illustrate how assumptions of data-centrism can sneak into decision making. Consider a sales manager who creates sales action plans for representatives based on reports extracted from his organisation’s BI system. In doing this, he makes a number of tacit assumptions. They are:

  1. The sales action plans should be based on the data provided by the BI system.
  2. The data available in the system is relevant to the sales action plan.
  3. The information provided by the system is objectively correct.
  4. The  side-effects of basing decisions (primarily) on data are negligible.

The assumptions and why they are incorrect

Below I state some of the key assumptions of the data-centric paradigm of BI and discuss their limitations using the example of the previous section.

Decisions should be based on data alone:    BI systems promote the view that decisions can be made based on data alone.  The danger in such a view is that it overlooks social, emotional, intuitive and qualitative factors that can and should influence decisions.  For example, a sales representative may have qualitative information regarding sales prospects that cannot be inferred from the data. Such information should be factored into the sales action plan providing the representative can justify it or is willing to stand by it.

The available data is relevant to the decision being made: Another tacit assumption made by users of BI systems is that the information provided is relevant to the decisions they have to make. However, most BI systems are designed to answer specific, predetermined questions. In general these cannot cover all possible questions that managers may ask in the future.

More important is the fact that the data itself may be based on assumptions that are not known to users. For example, our sales manager may be tempted to incorporate market forecasts simply because they are available in the BI system.  However, if he chooses to use the forecasts, he will likely not take the trouble to check the assumptions behind the models that generated the forecasts.

The available data is objectively correct:  Users of BI systems tend to look upon them as a source of objective truth. One of the reasons for this is that quantitative data tends to be viewed as being more reliable than qualitative data.  However, consider the following:

  1. In many cases it is impossible to establish the veracity of quantitative data, let alone its accuracy. In extreme cases, data can be deliberately distorted or fabricated (over the last few years there have been some high profile cases of this that need no elaboration…).
  2. The imposition of arbitrary quantitative scales on qualitative data can lead to meaningless numerical measures. See my post on the limitations of scoring methods in risk analysis for a deeper discussion of this point.
  3. The information that a BI system holds is based the subjective choices (and biases) of its designers.

In short, the data in a BI system does not represent an objective truth. It is based on subjective choices of users and designers, and thus may not be an accurate reflection of the reality it allegedly represents. (Note added on 16 Feb 2013:  See my essay on data, information and truth in organisations for more on this point).

Side-effects of data-based decisions are negligible:  When basing decisions on data, side-effects are often ignored. Although this point is closely related to the first one, it is worth making separately.  For example, judging a sales representative’s performance on sales figures alone may motivate the representative to push sales at the cost of building sustainable relationships with customers.  Another example of such behaviour is observed in call centers where employees are measured by number of calls rather than call quality (which is much harder to measure). The former metric incentivizes employees to complete calls rather than resolve issues that are raised in them. See my post entitled, measuring the unmeasurable, for a more detailed discussion of this point.

Although I have used a scenario to highlight problems of the above assumptions, they are independent of the specifics of any particular decision or system. In short, they are inherent in BI systems that are based on data – which includes most systems in operation.

Programmable and non-programmable decisions

Of course, BI systems are perfectly adequate – even indispensable –  for certain situations. Examples of these include, financial reporting (when done right!) and other operational reporting (inventory, logistics etc).  These generally tend to be routine situations with clear cut decision criteria and well-defined processes. Simply put, they are the kinds of decisions that can be programmed.

On the other hand, many decisions cannot be programmed: they have to be made based on incomplete and/or ambiguous information that can be interpreted in a variety of ways. Examples include issues such as what an organization should do in response to increased competition or formulating a sales action plan in a rapidly changing business environment. These issues are wicked: among other things, there is a diversity of viewpoints on how they should be resolved. A business manager and a sales representative are likely to have different views on how sales action plans should be adjusted in response to a changing business environment. The shortcomings of BI systems become particularly obvious when dealing with such problems.

Some may argue that it is naïve to expect BI systems to be able to handle such problems. I agree entirely. However, it is easy to overlook over the limitations of these systems, particularly when called upon to make snap decisions on complex matters. Moreover, any critical reflection regarding what BI ought to be is drowned in a deluge of vendor propaganda and advertisements masquerading as independent advice in the pages of BI trade journals.

Conclusion

In this article I have argued that BI systems have some inherent limitations as decision support tools because they focus attention on data to the exclusion of other, equally important factors.  Although the data-centric paradigm promoted by these systems is adequate for routine matters, it falls short when applied to complex decision problems.

Written by K

November 24, 2011 at 6:20 am

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