What makes a good decision maker – the three ingredients

What makes a good decision maker – the three ingredients

A 2004-2005 Teradata Report on Enterprise Decision-Making showed that over 70% of the respondents in their survey said that poor decision-making is a serious problem for their business. Other research in the UK conducted by the research agency YouGov for the Investors in People organization highlighted the negative impact that poor decision-making was having on employees. Other research in the area of organizational performance is consistent, with some suggesting that well over half of all decisions fail in some way. Specifically that the outcomes of the decision making process was poor – the ‘wrong’ decision had been taken or simply never implemented. It seems obvious that if decisions can fail for up to half of the time then organizations need to pay very close attention to the quality of their decisions and the people carrying out this role, but to-date, there is not much evidence that they are.

Decision-making is an age old problem that is being compounded today by increasing knowledge, population, complexity, speed and change. The number of decisions being made is increasing as is the pace of change and the number of people in an organization involved in the thousands of day-to-day decisions, from a new product launch to a decision on a new hire that have to be made. Decision-making is an essential requirement of management especially at the senior level and is perhaps the true core competence of leaders … ‘our society has largely neglected the fact that sound judgment and decision making are the crux of many professions’ (Smith et al 2004). The very best leaders appear able to make crucial decisions effortlessly ‘standing on their feet’ drawing on unseen resources of domain knowledge and experience that set them apart from the crowd. How effective managers and staff are in general at making good decisions is a moot point and understanding the process of high performance decision making is becoming critical to organizational success.

Problem-solving and decision-making are closely linked as each requires creativity in identifying, developing then evaluating options from which a course of action needs to be chosen. A problem always denotes that there are options to be chosen from – if there is a known solution then this is a puzzle not a problem. A decision is basically a problem solving process under uncertainty, so the steps or stages of decision making are more or less the same as those for problem solving. There are many n-step problem solving routines to guide decision making but at the heart of the decision making problem are the following three main aspects: how the problem is seen, how alternatives are generated, and how the decision is actioned.

The three key aspects of high level decision making:

  • Proactive cognition is a feature of people who actively seek to change the environment and not passively react to it. People strong in this feature scan for opportunities in the world preferring to make choices rather than follow procedures and create novel options by seeing problems differently rather than considering just the obvious.
  • Deciding is about making the decision, weighing and chewing over the options and above all considering the risk of alternatives. People who are high in this ability often draw on extensive domain knowledge and experience enabling them to make mental short cuts and come to a decision apparently quickly hiding the deep processes really going on.
  • Finally Action Control – making and enacting the choice. The effective planning and scheduling of actions to deliver the decision are examples of behavior of people who are able to ensure that decisions are not postponed, procrastination is avoided and decisions are implemented without delay.

A good decision is the direct result of clear criteria, the scope of the choice to be made together with the risk of each alternative – then taking action. From this perspective a good decision is the outcome of a process of optimally achieving a given objective from a certain starting point. A good decision is a logical one (or at least defensible and traceable) based on the available knowledge to hand that answers a particular organizational problem. Measuring whether or not decision making is good or bad needs to assess both the process and the outcome, the effectiveness and the quality of the decision making, as these are two quite distinct things. Effectiveness is how well the process of decision making is managed and how learning takes place – what caused a poor outcome to occur is fed back into the process. Output quality is to some extent comes well after the decision has been taken and is a reflection whether the right choice was made as well as on how well the choice was placed into action and implemented. There are many examples, particularly in the political world, where there is an assumption that the decision is the same as the implementation. A good decision making process therefore integrates the choice with the implementation and looks at the outcome to ensure learning takes place.

It is this latter point that completes the circle – it is a balanced decision making process that seems to be about right. Consciously attending to all three areas of the decision making process is key. The seeking of ways to control and act on the world, weighing up options carefully thinking about the choice then ensuring implementation takes place are the three key facets of effective decision making and features of high performers. When this balanced process is in place in more organizations perhaps then can the abysmal performance we currently have in this core area be improved.

Royston

The Keyword Density of Non-Sense – by Dr E. Garcia

On March 24 a FINDALL search in Google for keywords density optimization returned 240,000 documents. I found many of these documents belonging to search engine marketing and optimization (SEM, SEO) specialists. Some of them promote keyword density (KD) analysis tools while others talk about things like “right density weighting”, “excellent keyword density”, KD as a “concentration” or “strength” ratio and the like. Others even take KD for the weight of term i in document j, while others propose localized KD ranges for titles, descriptions, paragraphs, tables, links, urls, etc. One can even find some specialists going after the latest KD “trick” and claiming that optimizing KD values up to a certain range for a given search engine affects the way a search engine scores relevancy and ranks documents.

Given the fact that there are so many KD theories flying around, my good friend Mike Grehan approached me after the Jupitermedia’s 2005 Search Engine Strategies Conference held in New York and invited me to do something about it. I felt the “something” should be a balanced article mixed with a bit of IR, semantics and math elements but with no conclusion so readers could draw their own. So, here we go.

Background.

In the search engine marketing literature, keyword density is defined as

Equation 1

where tfi, j is the number of times term i appears in document j and l is the total number of terms in the document. Equation 1 is a legacy idea found intermingled in the old literature on readability theory, where word frequency ratios are calculated for passages and text windows – phrases, sentences, paragraphs or entire documents – and combined with other readability tests.

The notion of keyword density values predates all commercial search engines and the Internet and can hardly be considered an IR concept. What is worse, KD plays no role on how commercial search engines process text, index documents or assign weights to terms. Why then many optimizers still believe in KD values? The answer is simple: misinformation.

If two documents, D1 and D2, consist of 1000 terms (l = 1000) and repeat a term 20 times (tf = 20), then for both documents KD = 20/1000 = 0.020 (or 2%) for that term. Identical values are obtained if tf = 10 and l = 500.

Evidently, this overall ratio tells us nothing about:

1. the relative distance between keywords in documents (proximity)

2. where in a document the terms occur (distribution)

3. the co-citation frequency between terms (co-occurrence)

4. the main theme, topic, and sub-topics (on-topic issues) of the documents

Thus, KD is divorced from content quality, semantics and relevancy. Under these circumstances one can hardly talk about optimizing term weights for ranking purposes. Add to this copy style issues and you get a good idea of why this article’s title is The Keyword Density of Non-Sense.

The following five search engine implementations illustrate the point:

1. Linearization

2. Tokenization

3. Filtration

4. Stemming

5. Weighting

Linearization.

Linearization is the process of ignoring markup tags from a web document so its content is reinterpreted as a string of characters to be scored. This process is carried out tag-by-tag and as tags are declared and found in the source code. As illustrated in Figure 1, linearization affects the way search engines “see”, “read” and “judge” Web content –sort of speak. Here the content of a website is rendered using two nested html tables, each consisting of one large cell at the top and the common 3-column cell format. We assume that no other text and html tags are present in the source code. The numbers at the top-right corner of the cells indicate in which order a search engine finds and interprets the content of the cells.

The box at the bottom of Figure 1 illustrates how a search engine probably “sees”, “reads” and “interprets” the content of this document after linearization. Note the lack of coherence and theming. Two term sequences illustrate the point: “Find Information About Food on sale!” and “Clients Visit our Partners”. This state of the content is probably hidden from the untrained eyes of average users. Clearly, linearization has a detrimental effect on keyword positioning, proximity, distribution and on the effective content to be “judged” and scored. The effect worsens as more nested tables and html tags are used, to the point that after linearization content perceived as meritorious by a human can be interpreted as plain garbage by a search engine. Thus, computing localized KD values is a futile exercise.

Burning the Trees and Keyword Weight Fights.

In the best-case scenario, linearization shows whether words, phrases and passages end competing for relevancy in a distorted lexicographical tree. I call this phenomenon “burning the trees”. It is one of the most overlooked web design and optimization problems.

Constructing a lexicographical tree out of linearized content reveals the actual state and relationship between nouns, adjectives, verbs, and phrases as they are actually embedded in documents. It shows the effective data structure that is been used. In many cases, linearization identifies local document concepts (noun groups) and hidden grammatical patterns. Mandelbrot has used the patterned nature of languages observed in lexicographical trees to propose a measure he calls the “temperature of discourse”. He writes: “The `hotter’ the discourse, the higher the probability of use of rare words.” (1). However, from the semantics standpoint, word rarity is a context dependent state. Thus, in my view “burning the trees” is a natural consequence of misplacing terms.

In Fractals and Sentence Production, Chapter 9 of From Complexity to Creativity (2, 3), Ben Goertzel uses an L-System model to explain that the beginning of early childhood grammar is the two-word sentence in which the iterative pattern involving nouns (N) and verbs( V) is driven by a rule in which V is replaced by V N (V >> V N). This can be illustrated with the following two iteration stages:

0 N V (as in Stevie byebye)

1 N V N (as in Stevie byebye car)

Goertzel explains, “-The reason N V is a more natural combination is because it occurs at an earlier step in the derivation process.” (3). It is now comprehensible why many Web documents do not deliver any appealing message to search engines. After linearization, it can be realized that these may be “speaking” like babies. [By the way, L-System algorithms, named after A. Lindermayer, have been used for many years in the study of tree-like patterns (4)].

“Burning the trees” explains why repeating terms in a document, moving around on-page factors or invoking link strategies, not necessarily improves relevancy. In many instances one can get the opposite result. I recommend SEOs to start incorporating lexicographical/word pattern techniques, linearization strategies and local context analysis (LCA) into their optimization mix. (5)

In Figure 1, “burning the trees” was the result of improper positioning of text. However in many cases the effect is a byproduct of sloppy Web design, poor usability or of improper use of the HTML DOM structure (another kind of tree). This underscores an important W3C recommendation: that html tables should be use for presenting tabular data, not for designing Web documents. In most cases, professional web designers can do better by replacing tables with cascading style sheets (CSS).

“Burning the trees” often leads to another phenomenon I call “keyword weight fights”. It is a recurrent problem encountered during topic identification (topic spotting), text segmentation (based on topic changes) and on-topic analysis (6). Considering that co-occurrence patterns of words and word classes provide important information about how a language is used, misplaced keywords and text without clear topic transitions difficult the work of text summarization editors (humans or machine-based) that need to generate representative headings and outlines from documents.

Thus, the “fight” unnecessarily difficults topic disambiguation and the work of human abstractors that during document classification need to answer questions like “What is this document or passage about?”, “What is the theme or category of this document, section or paragraph?”, “How does this block of links relate to the content?”, etc.

While linearization renders localized KD values useless, document indexing makes a myth out of this metric. Let see why.

Tokenization, Filtration and Stemming

Document indexing is the process of transforming document text into a representation of text and consists of three steps: tokenization, filtration and stemming.

During tokenization terms are lowercased and punctuation removed. Rules must be in place so digits, hyphens and other symbols can be parsed properly. Tokenization is followed by filtration. During filtration commonly used terms and terms that do not add any semantic meaning (stopwords) are removed. In most IR systems survival terms are further reduced to common stems or roots. This is known as stemming. Thus, the initial content of length l is reduced to a list of terms (stems and words) of length l’ (i.e., l’ < l). These processes are described in Figure 2. Evidently, if linearization shows that you have already “burned the trees”, a search engine will be indexing just that.

Similar lists can be extracted from individual documents and merged to conform an index of terms. This index can be used for different purposes; for instance, to compute term weights and to represent documents and queries as term vectors in a term space.

Weighting.

The weight of a term in a document consists of three different types of term weighting: local, global, and normalization. The term weight is given by

Equation 2

where Li, j is the local weight for term i in document j, Gi is the global weight for term i and Nj is the normalization factor for document j. Local weights are functions of how many times each term occurs in a document, global weights are functions of how many times documents containing each term appears in the collection, and the normalization factor corrects for discrepancies in the lengths of the documents.

In the classic Term Vector Space model

Equation 3, 4 and 5

which reduces to the well-known tf*IDF weighting scheme

Equation 6

where log(D/di) is the Inverse Document Frequency (IDF), D is the number of documents in the collection (the database size) and di is the number of documents containing term i.

Equation 6 is just one of many term weighting schemes found in the term vector literature. Depending on how L, G and N are defined, different weighting schemes can be proposed for documents and queries.

KD values as estimators of term weights?

The only way that KD values could be taken for term weights

Equation 7

is if global weights are ignored and the normalization factor Nj is redefined in terms of document lengths

Equation 8

However, Gi = IDF = 1 constraints the collection size D to be equal to ten times the number of documents containing the term (D = 10*d) and Nj = 1/lj implies no stopword filtration. These conditions are not observed in commercial search systems.

Using a probabilistic term vector scheme in which IDF is defined as

Equation 9

does not help either since the condition Gi = IDF = 1 implies that D = 11*d. Additional unrrealistic constraints can be derived for other weighting schemes when Gi = 1.

To sum up, the assumption that KD values could be taken for estimates of term weights or that these values could be used for optimization purposes amounts to the Keyword Density of Non-Sense.

References

The Fractal Geometry of Nature, Benoit B. Mandelbrot, Chapter 38, W. H. Freeman, 1983.

From Complexity to Creativity: Computational Models of Evolutionary, Autopoietic and Cognitive Dynamics, Ben Goertzel, Plenum Press (1997).

Fractals and Sentence Production, Ben Goertzel, Ref 2, Chapter 9, Plenum Press (1997).

The Algorithmic Beauty of Plants, P. Prusinkiewicz and A. Lindenmayer, Springer-Verlag, New York, 1990.

Topic Analysis Using a Finite Mixture Model, Hang Li and Kenji Yamanish.

Improving the Effectiveness of Information Retrieval with Local Context Analysis, Jinxi Xu, W. Bruce Croft.


© Dr. E. Garcia. 2005

http://www.e-marketing-news.co.uk

How to do a SWOT Action Analysis

SWOT (Strengths Weaknesses Opportunities and Threats) Analysis is a simple but surprisingly effective technique to assess an organisations positioning and begin the process of turning general ideas for market growth into actionable activities. This brief guide shows how to extend the simple SWOT concept into a tool for defining the actions needed to deal with external threats and internal weaknesses in the organisations capabilities.

The process is best done within a workshop concept. So organise a team meeting of around 7 to 10 interested parties who are experts or knowledgeable in the domain to be considered.

The process:

Step 1 – First agree the area to be considered and the core assumptions. For example ‘we will consider the Softhouse organisation and the opportunities to grow the market in the States.

Step 2 – Use a brainstorming technique such as nominal group and ask the team first to think about the area we have chosen and what the issues in delivering this approach are. They write down (on their own) what could be the barriers or carriers to entering the new market in the States onto post-it notes or just make a list on paper before them.

Step 3 – They place their post it notes (or the facilitator) in turn onto the grid as shown in the diagram below – barriers to threats and carriers to opportunities.

Step 4 – Brainstorm as in step 2 and consider the organisation (Softhouse) and what are its unique strengths or capabilities and its weaknesses. The team on their own write down onto post-it notes their ideas as before.

Step 5 – They place their post-it notes (or the facilitator) in turn onto the grid as shown in the diagram – strengths to strengths and weaknesses to weaknesses.

Step 6 – The team then consider the crossing points of the SWOT for example between Threats and Strengths below (top left box) and as shown in the diagram think of specific actions to use strengths to counter any threats. These are written onto post it notes as before and placed in turn into the grid.

Step 7 – The facilitator tidies up the board removing duplicates or clarifying actions that have been written down. The board actions are then agreed prioritized then transferred to a standard action plan template.

Table One SWOT action analysis

Example Swot Action Analysis

Here is an example taken from an early draft of a business plan to illustrate the completed board. From here the actions can be taken across to a standard action plan template and owners and timescales applied. Thus from an initial consideration of the external and internal environment we can quite quickly move to a position where we can see possible practical actions we can take to move the agenda forward.

Royston