Chapter 4 Information and Knowledge Management

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Information and knowledgemanagement

[A]ll organisations exist in three tenses as follows:

1.. They exist in the ow of time and therefore carry much of their history into theircontemporary working methods and practices.

2.. They function in the present tense, meeting the needs of clients and customers and solving theoperational problems of their daily work.

3.. They live in the future, anticipating new developments, planning forward investments, anddeveloping strategies for long-term survival.

(Bob Garvey and Bill Williamson)

4.1 Introduction

Data, information and knowledge are the foundations on which gooddecision making are built. However rational the analysis that is conducted,however powerful the computations used to support that analysis, withoutappropriate data, information and knowledge, decision making can at bestonly be formalised guessing. Of course, that is no more than the cynenmodel suggests (section 1.2); without data, information and knowledge, theDM would be operating in the chaotic space. In the known, knowable andcomplex spaces, the DM is not so ignorant, and we need to consider how various information systems can support her.

To paraphrase the quotation above from Garvey and Williamson

(2002), DMs exist in the ow of time, past, present and future: theirpast history setting the context for their current decisions; operating inthe present, making decisions and solving problems; and anticipatingfuture events, planning and developing strategies for the long term.These three functions correspond loosely with the three types of infor-mation systems:

databases – i.e. systems that hold historical data and allow them to bequeried and analysed in a variety of ways;

knowledge management systems (KMSs) – i.e. tools for deployingwhat has been learnt from the past to address the problems of thepresent; and

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4.2 Human memory

Time and memory are true artists; they remould reality nearer to the heart’s desire.

(John Dewey)Psychologists have suggested that there are two different types of humanmemory: short-term , more recently referred to as working memory , andlong-term. Working memory is both temporary and short-lived, and inmany ways is similar to the main (RAM) memory of a computer. It holdsthe information that is the focus of our attention, sustains this informationby rehearsal and is the workspace where ideas and concepts are registered,transformed and manipulated. A full description of working memory lies

outside the scope of this book; see Baddeley (2007) for a recent review.Here we note that a critical feature of this type of memory is that it haslimited capacity – that is, only a relatively small amount of informationcan be held and transformed at the same time. If you doubt this, try thefollowing experiment on your friends.

Ask them to write down the answers to the following three questions (readeach question aloud once and allow your friends time to write each answerbefore giving them the next one!): ‘What is 2 þ 2?’ ‘What is 29 þ 92?’ ‘Whatis 35,986 þ 57,989?’

They should have little trouble with the rst question, since the answerpops out from long-term memory; after all, everyone knows that two andtwo make four. The second problem is more difcult, since your friendsare unlikely to know the answer in advance. So, in their working memory they need to store the two numbers, taking up some of the availablecapacity. Then they need to add the units together; adding is a mentalactivity that takes capacity to implement; and next they need to remember

the units total and the carry it into the tens column, requiring furthermemory capacity! Now they need to add the two digits in the tenscolumn and the carry term, requiring further capacity, to arrive at thenal answer. Storage and computations such as adding use up workingmemory capacity, but the demands of adding two digits are usually within the capacity limitations. The third problem is impossible for mostpeople. Interestingly, you can teach yourself methods for completingthis, but most people have not done so. Indeed most people cannot evenhold the two numbers in their working memories for any period, becausethe demands of storing ten digits are beyond their working memory capacity.

93 Information and knowledge management



If holding ten digits is beyond working memory capacity, one wondershow we cope when we have to make decisions in fast-changing, infor-mation-rich environments when large quantities of information have to beregistered, evaluated, aggregated, stored and compared – these being thekinds of mental operations that underpin human decision making. Theanswer is that we simplify our thinking, take account of less informationand process it in a less elaborate way, and in doing so reduce the infor-mation-processing load. As we argue throughout this chapter, however,these simplications in thinking can lead to errors and biases in judge-ments and decision making.

The second type of memory, namely long-term memory, retains largequantities of information over relatively long periods of time. Long-term

memory is similar to a hard disk, CD or DVD ling system in a computer.Tulving and Donaldson ( 1972) distinguished between episodic memory ,which stores personal experiences (e.g. that I went to the theatre last week),and semantic memory , which stores our organised knowledge about theworld (e.g. that dogs can bite or that ice cream tastes good). Both theseinvolve declarative knowledge – i.e. knowing that something is true or not. Inrecent years there has been some discussion of procedural knowledge – i.e.knowing how to do something, such as riding a bicycle or writing a letter.

While a full description of long-term human memory lies outside thescope of this book (see Baddeley, 2004, for an interesting introductory text), there are some aspects that are important for understanding decisionmaking and how to support it. For example, we remember better thoseevents with high impact, rather than those that are commonplace: see theavailability bias (section 2.7).

Second, there is considerable evidence to suggest that long-termmemory works by association and reconstruction rather than simple recall.Computers recall precisely what they store, both in terms of the datathemselves and their format: what they put away they take out. Humanmemory is a more reconstructive process. We remember associationsbetween features of an event or fact and then, when we wish to ‘recall’ it,we reconstruct it from these associations. Moreover, we may not use all theassociations, nor in the same order that we stored them (Hogarth, 1980;Klein and Methlie, 1995), so our memories of events may depart signi-cantly from how they actually occurred. In addition, when we retrieve amemory, aspects of the current context may be incorporated into that

memory, thereby changing it further. This means that we often have fulland rich memories of facts and events that depart in important ways fromthe objective details.

94 Decision behaviour, analysis and support



Finally, the associative nature of memory means that, while retrievinginformation of a particular type, related memories become temporarily more accessible – a phenomenon often referred to as priming . As a result,thinking about a past success makes all other successes temporarily moreaccessible (and in doing so puts failures into the background). Thus, if we use our past experience to assess the likely success of a new venture, thepriming of previous successes may lead us to overestimate this likelihood.

In conclusion, we need to recognise that our memories may be biased,and if we depend too heavily upon them when making judgements anddecisions then these activities may also be biased. In this chapter weconsider ways in which computers can be used to overcome these biases.

4.3 Data, information and knowledge

His had been an intellectual decision based upon his conviction that if a little knowledge was adangerous thing, a lot was lethal. (Tom Sharpe)

In everyday language we scarcely distinguish between data, information andknowledge. There is a feeling of increasing value to the user in passing fromdata to information to knowledge, perhaps, but no clear distinction. Weintend to be more precise. Following Boisot ( 1998), Laudon and Laudon(2006), Marakas (2003) and Turban et al . (2006), we dene:

data as facts about things, events, transactions, etc. that are notorganised for any specic context;

information as data organised and summarised to be meaningfulwithin a specic context, usually a decision, but perhaps an inferenceor forecast; and

knowledge as generic information – e.g. scientic understanding – thatis relevant to several contexts, together with the skills and values thatare used in solving problems. Having knowledge implies havingunderstanding, experience and expertise.

So Sherlock Holmes might once have cried out for ‘Data, data, data!’, butwhat he really wanted was information to combine with his voluminousknowledge so that he could solve the case. We may also reect that thecommon curse of information overload is a misnomer. While we may beswamped by data, information – by denition – is a summary of relevantdata that supports our decision making. Thus, complaints about infor-

mation overload indicate that we are not summarising the data suf-ciently for our purposes – though, of course, there could be so many datathat they overwhelm our cognitive capacity to summarise them.




There are many qualications that we should make to these denitions.Perhaps the most important is that a piece of information for one person inone context may be quite irrelevant to another person in another context,and so simply an item of data to him or her. Indeed, in another context it

may also be irrelevant to the rst person, and so simply become data again.Knowledge, however, is more long-lasting. It includes generic information,such as the theories and models of science, economics and so on. Theoriesand models are structures that suggest how data should be organised,inferences drawn and predictions made in a range of contexts. Knowledgealso includes the generic skills that enable us to form and use information inspecic contexts. Information becomes knowledge when the recipientrecognises a new ‘understanding’ derived from it. Table 4.1 provides asummary of several distinctions between data, information and knowledge.We also include skills and values within the general meaning of knowledge.Our skills are an expression of our tacit knowledge (see below); and, whilewe might question whether our values and preferences per se are part of ourknowledge, they are certainly part of our self-knowledge.

Whereas data and information can always be made explicit and codied –i.e. expressible in some permanent form – not all knowledge can. There is adistinction between a person’s unexpressed tacit knowledge – e.g. a skillsuch as riding a bicycle or performing an analysis – and explicit knowledge,

such as that encoded in an economic model. Philosophy traditionally dis-tinguishes three types of knowledge: ‘knowing how’ – procedural know-ledge that refers to a person’s skills and that may be tacit or explicit;

Table 4.1 From data to knowledge

Data ! Information ! Knowledge

Description Observations of states

and events in the world.

Data endowed with

relevance to a context.

General learning and

understanding drawing onexperience throughreection and synthesis.

Characteristics Easily captured; easily structured; easily represented; oftenquantiable; raw resource.

Needs agreement onmeaning; built withanalysis; reducesuncertainty within acontext.

Transferable betweencontexts; some knowledgeexplicit – e.g. science; sometacit and personal – e.g.skills; hard to store andcommunicate.

Method of acquisition Observation. Judgement. Experience.

Source: Earl




‘knowing that’ – i.e. propositional or declarative knowledge such as aneconomic or environmental model, which relates to the generic informationused in many contexts; and ‘knowing things’, which is knowledge of objects,people and systems and which, again, is generic information that may beused in many contexts. These two last forms of knowledge are generally explicit or may be rendered so.

Many have argued that one cannot use explicit knowledge withoutdrawing on some tacit knowledge: knowing how to apply a piece of explicitknowledge is itself a tacit skill. Simply to understand a theory, a supposedpiece of explicit knowledge, one must have language skills and muchsemantic knowledge to decode and know how to apply the theory; and, if one should argue that these language skills and semantic knowledge can,

in turn, be codied, then one needs further skills to decode these, thusbuilding an innite regress. These issues are not easy to resolve; theories of knowledge and language have kept philosophers debating for centuries,with no clear conclusions (Flew, 1971; Polyani, 1962; Ryle, 1949). Eventhose who believe that, ultimately, all knowledge will be rendered explicitaccept that this is not the case today. If it happens, it will happen in thefuture. Until then, we must accept that any application of knowledge willinvolve some aspect of tacit knowledge. It is our expectation, therefore,

that a DM will need to bring both tacit and explicit knowledge to bearwhen making a decision.How is knowledge used in decision making or in the related tasks of

making an inference or forecast? We offer an outline in gure 4.1. In theformulation phase the DM will consider the issues that she faces, herobjectives in dealing with them and identify what she might do. In theknown and knowable domains this will be a relatively well-rehearsed task,drawing on much explicit knowledge; in the complex space she will needto draw on her judgement, experience and creativity to make what senseshe can of the situation, thus drawing on much tacit as well as explicitknowledge. She will add further details to her formulation by consideringwhat data she has, selecting those that are relevant and assembling theminto information. To do this, she must draw on her existing knowledge tolter the relevant data and then organise these in ways that inform herdecision making.

Similarly, making use of the available information to make inferencesor forecasts requires the application of knowledge too. Boisot ( 1998)

describes knowledge as providing the perceptual and conceptual ltersthat the DM uses rstly to select and organise data into informationand then to use that information to support an inference, forecast or




decision. After doing this a number of times, the DM may recogniseregularities in the information that she has used in several contexts, arecognition of a common pattern that she can learn and apply in thefuture. The ability to recognise such patterns and form new knowledgerequires insight and higher-level knowledge of how one learns; some of this will be explicit, but much may be implicit.

We therefore see an inevitable need for tacit knowledge in decision-making, especially in complex contexts. This observation leads us to believethat the ambition within AI to develop truly autonomous decision-makingsystems will ultimately fail. All the knowledge in a computer system isnecessarily codied – i.e. explicit – by virtue of being rendered in an artefact:such a system cannot contain tacit knowledge. Similarly, we see KB-DSS asbeing conned to the known and knowlable domains, unless the interactionbetween the DM and the DSS is sufciently sophisticated to allow her to

introduce her tacit knowledge into the analysis.We have explored the relationship between knowledge and decision

making in the context of a single decision maker. Much knowledge iscreated and applied by groups through the social processes of discussion,learning and sharing, however. Nonaka ( 1991) offers the following per-spective on such processes (see also Marwick, 2001, Nonaka, 1999, andNonaka and Toyama, 2003), postulating these four modes of creatingknowledge (illustrated in gure 4.2):

socialisation – sharing experiences and skills in communities, learningfrom each other, often by collaborating on activities (Wilson et al .,

2007);

Inferences,forecasts,decisions

Data → Information → Knowledge

Figure 4.1 (Previously learnt) knowledge must be applied to transform data to informationand information to knowledge; and also to make inferences, forecasts ordecisions




externalisation – articulating the knowledge explicitly in words, tables,gures, models, etc. (Williams, 2006);

combination – drawing together and systematising explicit knowledgeinto more generic, simpler and more widely applicable forms; and

internalisation – understanding the implications of generic explicitknowledge and deploying this understanding in our behaviour anddecision making.

Tacit knowledge can be shared during socialisation by mechanisms such asshowing and copying. For example, think how someone may have shown you how to ride a bicycle rather than explaining everything in explicitdetail, or how many skilled trades are taught by apprenticeship rather thanclassroom training. Tacit knowledge therefore circulates on the left-handside of gure 4.2, while knowledge that can be rendered explicit throughexternalisation crosses to the right-hand side.

‘Information systems’ has become the generic term for referring tocomputer systems in organisations that support their processes and decisionmaking. In the early days of computing the limits to storage and compu-tational power meant that information systems were conned to transactionprocessing, recording sales, maintaining accounts, paying wages, etc. Later,in the 1970s, when more power was available, management information systems were developed that could produce reports organising and sum-marising the necessary data for management decision making, typically in

the general domain (see gure 3.7). During the 1990s systems evolvedfurther, and it was possible to collate and summarise data from many sources to support the corporate strategic domain of decision making: see

Tacit knowledge

Explicit knowledge

Socialisation

Internalisation

Combination

Externalisation

Figure 4.2 Nonaka’s perspective on knowledge creation




the discussion of executive information systems below. Currently, theconvergence of information and communication technologies (ICT) issupporting more and more subtle means of collaboration, and, as we shallsee later, this is the key to building knowledge management systems.

How might we keep data, information and knowledge in computersystems? For data the answer is easy. They are raw facts: they can be kept ina database. For information the answer is in one sense just as easy – and inanother almost irrelevant. Since information is data organised and pro-cessed to be relevant to a context, we clearly want to keep it while thatcontext is ‘live’; and we can do so in reports – i.e. words, tables, gures, etc.When the context is in the past, however the information is informationno longer. It decays back to data, unless it becomes relevant for learning or

for building further knowledge. In the former case, we need only check that the original data are still in the database, and then we can discard the‘information’. In the latter case, we could argue for keeping all the (ex-)information in case there is something that can be learnt later – though nottoo much later. Current practice in organisations is to debrief at the end of any analysis, exploring the information used and summarising the partsthat may be useful in the future. The value in such debrieng is greatest if conducted very soon after completion, while memories are fresh. More-

over, summarised rather than original documents are more useful toothers, particularly if they are summarised and indexed using the languageand jargon local to the group sharing the knowledge: see section 5 below for further discussion.

4.4 Databases, data warehouses and data mining

There are three things I always forget. Names, faces, and – the third I can’t remember.

(Italo Svevo)Data are ‘objective’ facts about the world: events, numerical statistics,transactions, etc. While we might argue about whether data can be truly objective, they are by denition context-free, in the sense that they havenot been organised, summarised, analysed or presented with a view tosupporting a particular activity. When they are so organised and hencemeaningful to a user they become information. Data, therefore, form theraw material that information systems hold and use. Within an infor-

mation system data reside in databases . There are many types of database,each with its particular advantages and disadvantages. We do not intendto venture into any technical discussion of databases; for this, see, for




example, Laudon and Laudon ( 2006), Mallach (2000) and Turban et al .(2006). Rather, we focus on conceptual issues concerning how databasesmay enter into the decision support process offering level 0 support.

In section 2 we noted that the limited capacity of human workingmemory restricts the amount of data that can processed and subsequently stored in long-term memory. In addition, retrieval from long-termmemory is founded on reconstructive processes. We remember associ-ations between features of an event or fact and then, when we wish ‘recall’it, we reconstruct the memory from these associations. This provides avery rich memory based on the meanings (to us) of facts and events, ratherthan the objective details. This often introduces bias and inaccuracies intoour memories. Thus, we are not good at accurately memorising large

quantities of facts and data. Nor can our memory really be termed‘unbiased’ (see our discussion of the availability bias in section 2.7).Moreover, recall can be a slow process: how often have we had memorieson the ‘tip of our tongues’, only to wake up during the next night havingnally remembered them?

The storing and recall of data in computers is very different. Input is notrestricted by the kinds of capacity limitations that are present in workingmemory. Long-term storage is not founded on associative mechanisms,

and retrieval is not reconstructive as it is with human memory; what isstored is retrieved unaltered, therefore. In addition, retrieval processes incomputers are often much faster – a matter of microseconds.

The process of interacting with a database is, in rough terms, as follows.Relevant data for a context – i.e. information – are extracted by a query atthe time they are required. These queries are written in a query language ,structured query language (SQL) being the most common. Many systemsallow the user to build queries via an intuitive graphical interface, however.A report is generated when the results of a query are formatted andsummarised into a document or a screen that can be viewed and used by DMs. A database management system (DBMS) is a generic environment inwhich databases can be built. It allows users to dene the formats andcharacteristics of the data that will be stored, to input and query data, todevelop reports and to manipulate databases themselves and maintaintheir integrity and security, and may allow several users to access and work with the data concurrently in a secure manner.

The use of databases queries to develop management reports is an

example of level 0 decision support: see gure 3.7. The query processselects and organises data relevant to the DM’s context, quickly andwithout bias. Sometimes the summaries and simple analyses in the report




may forecast how the future might develop, providing some level 1 sup-port, but there is no level 2 or 3 support here. This point should be noted,because in the past some texts on databases and information systemsseemed to suggest that all DSSs are essentially database reporting systems.We argue that many DSSs go far beyond this.

Organisations seldom have a single database: they have tens, perhapshundreds, each built to handle the data relevant to one task or activity.Several databases can exist in the same DBMS. For instance, many of ushave two or three simple databases on our PCs, perhaps built within anofce suite database application. In such cases it is relatively simple togather data from two or more of the databases. In many organisations,however, history and geographical dispersion may mean that many of the

key databases exist in a variety of DBMSs. The value of the potentialinformation shared between these databases can be immense to a com-pany, for example in comparing customer buying patterns in differentregions or countries; gathering data from across these can be difcult,however.

A data warehouse is a system in which all the organisations’ databases arebrought together and archived in one place, together with the softwaretools to enable detailed querying, more general exploration of the data and

the generation of reports (Laudon and Laudon, 2006; Mallach, 2000;Marakas, 2003). Data warehouses seldom archive all the data that haspassed through an organisation but, rather, produce snapshots of the dataat intervals sufciently short to capture a useful history of its processes. Fora supermarket this might be daily, whereas for a small foundry it might beweekly or monthly. Originally data warehouses were single systems,essentially located in one physical place on one physical system, buttechnological advances mean that they may be distributed across many systems; indeed, they may be virtual, accessing summaries ‘on the y’ fromfull archives of the original databases.

Using the snapshots of its history contained in a warehouse oftentogether with current data an organisation may explore patterns of behaviour and use these to underpin its decision making. To helpmanagement explore the volume of data held in data warehouses thereare two types of tool, but note that the distinction between them is asubtle one:

on-line analytical processing (OLAP), which allows the user to correlate

the highly multidimensional data and to nd expected or commonly occurring patterns; and

data mining – i.e. tools that seek to recognise possible new patterns.




The use of such tools to explore vast amounts of data has allowed

organisations, inter alia , to: increase prots and efciency by exploiting general trends across theirsubunits;

increase prots and efciency by exploiting local differences betweentheir subunits or between subgroups – segments – of their customers(see case vignette 4.1); and

personalise their dealings with clients and customers, so buildingstronger relationships.

For a description of OLAP technologies, see, for example, Laudonand Laudon (2006) or Mallach (2000). Data-mining techniques are many and varied. First, there are the long-established methods of statisticalanalysis, such as multivariate analysis (Krzanowski and Marriott, 1994,1998), regression analysis (Gelman et al ., 1995) and time series analysisand forecasting (West and Harrison, 1989). Then there are severalnew methods developed within AI, such as articial neural networks(ANNs) (see section 5.5), genetic algorithms and fuzzy sets (Mallach, 2000;Marakas, 2003; Turban et al ., 2006). A promising research area is the

automatic construction of Bayesian belief nets by exploiting the empiricalcorrelations in very large2 databases (Cheng et al ., 2002; Korb andNicholson, 2004). Because data mining is generally used to explore very large multidimensional databases, there is a need to nd intuitively simplemethods of presenting the results to DMs. Much effort has therefore been

Case vignette 4.1 Data mining, beer and diapers 1

An often cited example of an early success of data mining relates to sales of beer anddiapers (babies’ nappies) in the early evening. A data-mining analysis of the sales withina chain of US supermarkets discovered that sales of diapers and beer were highlycorrelated in early evening sales, but not in general. An investigation suggested thathusbands, returning from work, were being sent out to buy diapers, because the familywere running low on them. While in the supermarket, many of the husbands were alsopicking up a pack of beer, to drink that evening. The supermarket experimented withplacing a display of beer beside the diapers to stimulate such behaviour further. Sales of beer increased signicantly.

1 As we complete writing this text, we have heard that this example, taught by many of ourcolleagues and reported in textbooks, journals and even the Financial Times , may be an urbanlegend (DSS News , 2002, volume 3, number 23; http://dssresources.com/newsletters/66.php ). It

still makes the point, though, and memorably encapsulates the general success of data mining.2 The size of the database is very important here. Correlations between many variables arenotoriously difficult to estimate even from large data sets. The very large volume of dataavailable in a data warehouse is therefore essential.


http://dssresources.com/newsletters/66.php

http://dssresources.com/newsletters/66.php



put into data visualisation: see, for example, Cleveland ( 1994), Davis andKeller (1997), Klosgen and Lauer (2002), MacEachren ( 1992), Marakas(2003) and Tufte ( 1997).

There are many cognitive issues to be addressed when discussing the useof the automatic pattern discovery algorithms that lie at the heart of datamining. First, the immense volume of the data held in data warehousesmeans that it is virtually impossible for an unaided human to explore –‘eyeball’ – the data and to spot patterns. In addition, the data are typically high-dimensional, whereas the human can see relationships in only two orthree dimensions. Thus computer support for discovering patterns isessential. Moreover, people are easily deceived by spurious ‘patterns’ quiteunrelated to any underlying covariation, or they see patterns that they wish

to see (Jennings et al ., 1982). Unfortunately, automatic pattern recognitionalgorithms can nd spurious patterns too: they are simply looking forconditional or local correlations and relationships within the data, andthese can arise by chance. For instance, in case vignette 4.1, the correlationbetween the sales of beer and diapers was found only in the early evening –a conditional correlation. Statistically based data-mining techniques might be able to assess whether any pattern is spurious through appeal to sig-nicance level ideas or posterior probabilities. Many data-mining algo-

rithms are not based on formal statistical methods, however. In such cases –indeed, in all cases – it is wise for the DMs to look at the patterns discoveredand ask themselves whether they make sense.

Consider case vignette 4.1 again: the management investigated thepossible link between sales of beer and diapers and identied a plausibleexplanation of why such a relationship might exist. Had the possible link been between, say, chilli powder and nail clippers, they might well havedecided that it was spurious and not rearranged their store. Moreover, inthis example there was subsequent empirical verication that the placingof beer near to the diapers led to greater sales.

Data mining is sometimes referred to as a knowledge discovery technique,and, indeed, in our terminology it may be. For instance, the discovery of alink between beer and diapers sales is a piece of generic information that canbe used in many design decisions on supermarket layout. Data mining canalso discover patterns that are relevant only to a specic context, however,thus nding information rather than knowledge.

Another approach to exploring the data held within an organisation’s

distributed databases is provided by executive information systems , alsocalled executive support systems . These ‘provide executives with easy accessto internal and external information that is relevant to their critical success




factors’ (Watson et al ., 1997). EISs are, essentially, very clever queryingand reporting software, drawing together and summarising data frommany sources across a variety of DBMSs. Using a very intuitive human–computer interface (HCI), they provide simple charts and tabulations

to help senior managers explore an organisation’s databases: see casevignette 4.2. The technology of EISs is distinguished from that of datamining in that EISs produce broad summaries that allow senior managerscontinually to review the progress of their organisation against its plannedstrategy. Data-mining technologies dig down into large data sets, seekingmuch more detailed, local patterns. Support for such continual reviewingand monitoring of the performance of their strategy is essential if man-agers are to protect themselves against defensive avoidance (see gure 4.3).

EISs provide the opportunity to monitor the success of the current courseof action and assess whether there are any serious risks emerging.It should be admitted, however, that there may be a considerable gap

between theory and practice in the use of EISs. Xu et al . (2000) found thatmany executives were disappointed by too great a focus of implementedEISs on operational data and that the systems generally did not providesufciently high level summaries to support strategic decision making.

The technologies underpinning EISs are being used more and morethroughout organisations, providing support for operational, tactical andstrategic decisions. The term business intelligence tools is used to describe allthese techniques that provide the level 0 support to help guide the DMsthrough vigilant decision making towards a requisite decision.

Databases are not the only way of storing data. Word processing,spreadsheets and other les, e-mails and, above all, web pages alsocontain much data. It is because of the wealth of data available on the webthat we are now in the Information Age .3 Such data are difcult to extract,however, because they are not stored in a structured form. Within

databases, items of data are stored in precise positions both so that they

Case vignette 4.2 An example of an EIS at a power company An EIS at a large regional power company with interests in energy, resources and wastemanagement allows executives to monitor regulatory and pricing issues, anticipatingtheir impacts on the overall and individual businesses. They can quickly forecast andsummarise production and revenue in the short and long term, enabling them to see thebalance of their business and plan accordingly. Source: Watson et al . (1997).

3 This is a misnomer if we reflect on our distinction between data, information and knowledge.




Very simplified analysis: isthere any serious risk fromthe current course of action?

Is there an obvious newcourse of action thatmitigates risk sufficientlyand dominates all others?

Vigilant decision making Cycles of increasingly elaboratemodels and analysis until theanalysis is requisite.

Unconflicted adherence Stay with the present courseof action.

Unconflicted change Take a new course ofaction.

Requisite decision

Yes

Yes No

No

Figure 4.3 The progression through unconicted adherence, unconicted change and vigilance towards a requisite decision

can be easily retrieved and so that their meanings are known to thedatabase owner. Other documents are much less structured. Developmentssuch as Extensible Markup Language (XML), tagging and the semantic web,however, mean that it is becoming easier to access the data withinunstructured les and web pages.

4.5 Knowledge management

Our knowledge can only be nite, while our ignorance must necessarily be innite.(Sir Karl Popper)

In the 1960s commercial and organisational uses of computing werereferred to as data processing . Then during the 1980s, when there was




sufcient computational power to do rather more than store and processdata simply, we entered the age of information systems , which were able toselect and summarise data quickly and exibly into context-relevantmanagement information. Now there is considerable emphasis on know-ledge management . Computing has matured into exible systems that areable to store, retrieve and deploy knowledge.

From our earlier discussion, we know that knowledge managementsystems (KMSs) need to help us manage both explicit and tacit knowledge.We see KMSs as combining two general families of tools: 4

very exible data and information management systems, to allow storage and access to material stored in a variety of formats and data-bases distributed across one or more computing systems, together with

very exible querying tools to access such data, ideally using naturallanguage or at least graphical interfaces that make them more accessibleto non-technical users; and

collaborative working tools, to share and work synchronously andasynchronously on materials together with full project, workow anddiary management; such tools are known under the general heading of computer-supported cooperative work (CSCW) tools or groupware(Bannon, 1997; Kraemer and King, 1987; Mallach, 2000; Marakas, 2003).

The former provide the means to manage explicit knowledge, while thelatter allow users to share and work with tacit knowledge: see the process of socialisation discussed earlier. Case vignette 4.3 provides an example of aKMS designed to support activities in the hands-on work and operationaldomains. Note how it involves both a knowledge base and collaborationtools.

Almost everyone who uses the World Wide Web is familiar with searchengines. These tools are continually surveying and indexing the web, sothat when a user seeks information using a keyword he or she can quickly nd a range of relevant pages and sites, usually ranked by some measure of relevance to the keywords sought. Some of these tools accept queriesin more natural formats: simple plain questions. Similar tools are now being built into PC operating systems. The data and information man-agement tools in a KMS capitalise on such search tools. They allow indi-viduals in an organisation to search for knowledge and expertise as they need it, by searching all, or at least the majority of, les and databasesdistributed across their systems. Many organisations debrief and summarise

4 Note that, within the computing literature, there is often undue emphasis on the storage andretrieval of explicit knowledge, with the effect that one might think that KMSs are little morethan glorified databases.




completed projects on their intranet to increase the information andknowledge available to such searches. This use of KMSs can help shareknowledge, either by uncovering appropriate documentation that providesthe answer directly or by identifying some related activities and theirowners, who may then be contacted for further information.

Such data and information management tools allow the user to retrieveexplicit knowledge. As we might expect, there is evidence that they donot provide the broader support needed to work with and deploy tacitknowledge (Lee and Choi, 2003). It is through the use of the collaborationtools in a KMS that tacit knowledge can be shared. The sorts of tools wehave in mind here range from simple e-mail and discussion lists throughchat rooms, desktop sharing and ‘whiteboard’ tools to user-congurablewebsites that provide areas where teams can interact, share documents andmanage projects. These tools help teams work together by allowing themto share information and expertise in order to address and solve problems.

We would emphasise, however, that, while collaboration tools can supportthe socialisation process of tacit knowledge management, they may not be

Case vignette 4.3 Eureka, a KMS for Xerox eld engineersIn 1996 Xerox developed Eureka, a KMS for its eld engineers to share repair tips.At this time copiers were complex machines, based upon technologies that wereevolving rapidly. To be able to repair the myriad of potential failures of an ever-increasing number of models and product lines, the engineers were continually beingconfronted with problems that they simply had not seen beforehand. Sorting these outwas time-consuming, and the delays did not t well with a customer service ethos.Eureka sought to share their experiences with all Xerox’s eld engineers across theworld.

It was, essentially, an intranet in which engineers could describe new faults and howthey had solved them or ask for help if they had not. They also posted tips and ideas, andgenerally shared knowledge that would help them all provide a faster service. Becausethe days of fully mobile computing were still a few years off, Eureka was built so that theknowledge base could be downloaded at the beginning of each day onto a laptopcomputer. At the end of the day the engineer would upload any tips or ‘war stories’arising out of his or her efforts that day, as well as e-mail any outstanding questions. Atrst Xerox tried nancial incentives to persuade the engineers to spend the time con-tributing their experiences, but they quickly found that peer approval and gratitudeprovided a better reward. Eureka truly built a community of eld engineers, bringingbenets to Xerox of enhanced team spirit, considerable cost savings and much better customer service. Source: Biren et al . (2000).




able to replace the need for face-to-face meetings and personal contacts(McDermott, 1999; Walsham, 2001).

How might modern approaches to knowledge management affectdecision support? Before answering this question, we need to reect fur-ther on gure 4.1 and the interrelationships between data, information andknowledge in decision making. Explicit ‘knowing that’ and ‘knowingthings’ knowledge will provide contextual information to the decisionitself – i.e. one will draw down from the generic to the particular, perhapslooking at the forecasts provided by a well-established economic model.More tacit ‘knowing how’ knowledge will shape the decision process itself,perhaps by tting the economic model to current data describing thecontext. Note here that the ‘knowing how’ is the tacit knowledge of how to

t the model to the problem in question; it is not the explicit ‘knowingthat’ knowledge that is embedded in the economic model itself. In theformulation phase the DMs and their analysts will need to ‘know how’ tobring shape to a mess of issues, particularly in the complex space.

The implications of this for KMSs and DSSs are as follows (seegure 4.4).

Knowable Cause and effect can

be determined withsufficient data:

the realm of scientificenquiry

Known

Cause and effectunderstood and predictable:

the realm of scientificknowledge

Complex Cause and effect may beexplained after the event:

the realm ofsocial systems

Chaotic Cause and effectnot discernible

Tacit knowledge: judgement/expertise

Explicit knowledge:e.g. scientific models

Figure 4.4 The differing emphases on tacit and explicit knowledge in the cynen domains




In the known and knowable spaces, explicit knowledge will be deployedthrough databases, knowledge bases and models. Thus, KMSs andDSSs will be able to draw more on data and information management,

along with computational technologies, to perform detailed model-based analyses.

In the complex and chaotic spaces, where judgement will be needed toa larger extent, we may expect the emphasis with KMSs and DSSs to bemore on tools that aid collaboration, share tacit knowledge and build judgement.

4.6 Statistics and decision support

In earlier times, they had no statistics, and so they had to fall back on lies. (Stephen Leacock)

Discussions of level 0 and 1 decision support inevitably draw us into thedomain of statistics, the discipline of data analysis. Figure 4.5 suggests how statistical inference and forecasting techniques may be thought of as ttinginto the decision support process. In our view, it is essential for DMs toreect upon any decision problem from two complementary perspectives:

science – what they think might happen; and values – how much they care if it does.

We have already emphasised the importance of distinguishing between thesetwo perspectives in our discussion of the players in a decision analysis (seesection 1.4). The left-hand side of gure 4.5 shows that statistical analysishelps the DMs, their experts and analysts update and rene their under-standing of the science. When the statistical analysis is directed at exploringthe current decision context, the support provided is at level 0; whenthe analysis is directed to forecasting the future, the support is at level 1.

The right-hand side of the gure reects the modelling of the DMs’ andstakeholders’ values. Then both their understanding of the science and theirvalues are drawn together to provide guidance on the evaluation andranking of the possible actions. Of course, we should admit that the sim-plications inherent in gure 4.5 hide as many issues as they elucidate.

In selecting and drawing together data to form information, statisticalthinking is vital. Otherwise the DMs may over- or underestimate theimportance of patterns in the data – we noted this when discussing data

mining. Statistical methods ensure that we do not ignore prior know-ledge of base rates. Indeed, without statistical input the DMs risk fallingprey to the intuitive, but potentially biased, behaviours discussed in




chapter 2. In many cases prior knowledge provides the form of appro-priate models, and the role of statistics is to t the models to current dataand assess the residual level of uncertainty.

4.7 Concluding remarks and further reading

We began this chapter with a discussion of the distinction between data,information and knowledge. This discussion reected subtle differences

made by academics and not the near-synonymous use of the three wordsin everyday language. The distinction is vitally important if we are tounderstand the true power of many management techniques and com-puting systems, the advertising copy for which includes such phrases as‘information management’ (they usually manage data and produceinformation for particular contexts) and ‘knowledge-based’ (they are not

based upon tacit knowledge). We have also noted that the systems that wehave discussed generally provide level 0 decision support, although the

collaborative aspects of knowledge management systems do support awhole range of processes for sharing tacit knowledge and expertise thatcontribute to all levels.

Decision analysis

Consequence

modelling

Statistical inference and forecasting

Decision?

Science Model understanding of the

impacts includinguncertainties

Statistical analysis

Combine adviceUsing an appropriate analytic technique:• AI and expert systems• operational research• decision analysis

Data

Values

Model preferences to giveobjective function(s)

(cost, value, utility, …)and weighting

Feedbackto futuredecisions

Problem formulation

Figure 4.5 How statistical inference and forecasting t into decision support




Further discussions of the distinctions and connections between data,information and knowledge, as well as the processes by which one isconverted into the other, may be found in the following sources: Boisot(1998), Coakes et al . (2002), Earl (2000), Flew (1971), Gourlay ( 2006),Laudon and Laudon ( 2006), Nonaka ( 1991), Polyani (1962), Ruggles andHoltshouse ( 1999) and Ryle (1949). Beware, however, that, although they discuss the same ideas and concepts, these authors do not all use the terms‘data’, ‘information’ and ‘knowledge’ in quite the same way. It is alsoimportant to remember that the creation of knowledge requires imagin-ation – a point that is sometimes underemphasised (Weick, 1987).

Detailed discussion of databases may be found in Laudon and Laudon(2006), Mallach (2000), Marakas ( 2003) and Turban et al . (2006), and in

many other information or decision support system texts. Harry ( 2001)gives an introduction to databases and information systems that builds ondistinctions between data, information and knowledge that are similar tothose that we made above. Anisimov ( 2003) discusses the use of OLAPtechnologies in providing decision support to management. Watson et al .(1997), along with Laudon and Laudon ( 2006), Mallach (2000) andMarakas (2003), provide discussions of EISs. Singh et al . (2002) providean interesting empirical study of EISs that demonstrates clearly that they

provide level 0 and level 1 support, but no higher. Hand et al

. (2001),Klosgen and Zytkow (2002), Mallach (2000), Marakas ( 2003) and many others discuss data warehouses and data mining.

Discussion of general issues relating to knowledge management may befound in Boisot ( 1998), Coakes et al . (2002), Courtney ( 2001), Garvey andWilliamson ( 2002), Lee and Choi (2003), Ruggles and Holtshouse ( 1999),Williams (2006) and Wilson ( 2002). Descriptions of KMSs may be foundin Laudon and Laudon ( 2006) and Marakas ( 2003). More detailed dis-cussion of the technical aspects can be found in Gallupe ( 2001), Marwick (2001), Sage and Rouse (1999), Santos (2003) and Walsham ( 2001). Halland Paradice ( 2005) discuss the relationships between KMSs and decisionsupport; see also Olson ( 2001) for a related discussion of the supportoffered to decision making by information systems. The future of KMSscan, perhaps, be seen in the Kepler scientic workow project, which usesgrid technologies to combine collaboration tools, large databases andscientic computational tools to support large multi-institutional researchcollaborations (Luda¨scher et al ., 2006).

The connections between decision analysis and statistical inferenceare not always fully appreciated. Savage’s 1954 text is a seminal work,which laid the foundations of both decision analysis and of Bayesian




statistical inference (Savage, 1972). Around the same time Wald andothe rs, inuenced by developments in game theory (see section11.6),were developing more general decision theoretic approaches to statistics(see, for example, Wald, 1945). These approaches argue that, althoughinference is not necessarily tied to decision making, in many cases we willwish subsequently to use what we learn in support of forecasting theoutcomes of our decisions (Berger, 1985; French and Rı´os Insua, 2000;Robert, 1994).

Modern statistical methodology is – forgive the gross simplication –divided between two schools: the frequentist and the Bayesian . Discussionsof the distinction may be found, inter alia , in Barnett (1999), Feinberg(2006), French (1986) and Migon and Gamerman (1999). Essentially, it

relates to whether the analysis limits itself to taking account of uncertainty arising from ‘objective’ randomness only and represented by frequentistprobabilities, or whether it also includes the subjective uncertainty inherent in the DMs’ beliefs and prior knowledge, as may be representedby subjective probabilities (see section 8.2 for a discussion of differentinterpretations of probability). We tend to adopt the latter, the Bayesianapproach. Bayesian statistics derives its name from its use of Bayes’ the-orem to update prior knowledge, represented through a prior distribution ,

to take account of the data to give a posterior distribution . Bayesian stat-istics is far more suited to decision support, since the processes forhandling uncertainties t seamlessly with the processes and techniques formodelling values: see, for example, Bernardo and Smith (1994), Bolstad(2004), DeGroot (1970), French and Rı´os Insua (2000), French and Smith(1997), O’Hagan and Forester (2004) and Savage (1972). For Bayesianforecasting methods, see Pole et al . (1994) and West and Harrison (1989).

4.8 Exercises and questions for discussion(1) . Give three examples of tacit knowledge that you possess and explain

why it is difcult or impossible to render them explicit.(2) . Some writers have suggested that there is a fourth level of under-

standing beyond data, information and knowledge, which they havecalled wisdom. Investigate this via journals and the web and discusswhat it might be.

(3) . How many databases do you use in your daily life? List them and

discuss how you would manage without them.(4) . Using the web and journals, nd and describe three examples of data

mining.




(5) . Give an example from your own experience of how a group solved aproblem by pooling their knowledge in a way that no individualcould have done. Relate your description to Nonaka’s SECI cycle(gure 4.2).

(6) . You are working for a consultancy company. You are preparing apresentation for a client who is considering acquiring anothercompany. How would you use the network of consultants in yourcompany and its knowledge base to prepare your presentation?


Chapter 4 Information and Knowledge Management

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