KNOWLEDGE MANAGEMENT’S NEXT FRONTIER: DEVELOPING

ORGANIZATIONAL KNOWLEDGE BY ANALYSING UNSTRUCTURED

CONTENT USING VISUAL ANALYTICS

HERMAN J. VAN NIEKERK (Ph.D M.Ed)

Director: Suritec (Pty) Ltd

11 Lansdowne Road

Claremont

Cape Town, South Africa

herman@suritec.co.za

ANNELIE PRETORIUS (Ph.D MA)

Researcher: Academic Programmes

10 Bird Street

Central

Port Elizabeth, South Africa

Annelie.pretorius@nmmu.ac.za

Organizational knowledge remains an important and valuable corporate asset. Knowledge

Management must therefore continuously strive to improve the discipline in both theory and

practice. Recent advances in KM thinking and practice have been limited and it appears that most

knowledge management processes have reached a level of maturity. This paper explores the

primary elements of the knowledge management process and concludes that the analytical and

knowledge creation process of Knowledge Management can be significantly improved through the

use of visual analytical tools. A grounded theory research methodology was adopted since there is a

lack of prior research where visual analytics were used in analysing vast amounts of textual data in

order to create organizational knowledge. Customer complaints records from the retail and

insurance industries were obtained and used as case studies to demonstrate how vast amounts of

text data can be visually analysed to assist with analytical interpretation and decision-making. The

research concluded that visual analytical tools can play a significant role in creating organizational

knowledge and simultaneously improve the analytical process of Knowledge Management. Visual

Analytics is a highly relevant emerging field and it offers many applied research and practical

opportunities to advance the field of Knowledge Management.

1. Introduction

There has been an enduring recognition for the importance of knowledge. Marshall

(1965: 115) argues that capital consists, in the greater part, of knowledge and

organization and that knowledge is the most powerful engine of production organizations

can focus on. Drucker (1993) and others continued with this theme and argued that the

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basic economic resource is no longer capital, natural resources or labour, but is and will

be knowledge. However, the financial crises of the last couple of years have raised many

questions about the role capitalism plays in business and society. De Soto (2012: 1)

argues that reformers and policymakers must recognise that they are not dealing with a

financial crisis but with a knowledge crisis and claims that capitalism does not need to be

rethought or re-invented; it simply has to be re-discovered. Similarly, discussions at the

2012 World Economic Forum in Davos, Switzerland highlighted the importance of

knowledge and the message which emerged is clear: Knowledge is the new capital.

Knowledge is the most important organizational resource and the fundamental basis

of competitive advantage and through superior knowledge an organization can

understand how to exploit and develop its traditional resources better than its competitor

(Skyrme 2002: 10). It is thus important that corporate strategists and epistemologists

reassess Knowledge Management to see where there are shortcomings and what can be

done to improve the entire spectrum of KM processes and practices.

Most aspects of the KM process have reached some level of maturity both in terms

of organizational practices and use of technology. However, it appears that advances in a

critical aspect of the KM process, the analysis and interpretation of organizational

content, has been slow. Much emphasis is placed on the analysis of structured data which

falls within the domain of Business Intelligence. However, on the other hand

organizations still struggle to analyse their unstructured, text-based content. This is a

valuable source of organizational information which can yield important discoveries to

improve decision-making in key organizational activities.

Key concepts and new terminology are also emerging and the discipline of

Knowledge Management needs to take notice of these developments in order to improve

KM and to stay relevant. This paper will provide a short overview of developments in

KM and will primarily explore the importance and value of unstructured content. It will

then offer solutions to the analysis of unstructured content as one of the primary sources

of an organization‟s knowledge assets. Knowledge Management‟s next frontier has

arrived: mastering the analysis and interpretation of unstructured content using visual

analytical tools.

2. Literature Review

The last decade saw a number of KM models emerging. Kakabadse, Kakabadse and

Kouzmin (2003: 75) identified and analysed five dominant models in knowledge

management and suggested a multi-model and multi-disciplinary approach to KM.

Similarly, Snowden (2002: 100) argues that KM has entered a third generation which

requires the clear separation of context, narrative and content management and challenges

the orthodoxy of scientific management. Complex adaptive systems theory is used to

create a sense-making model that utilises self-organising capabilities of the informal

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communities and identifies a natural flow model of knowledge creation, disruption and

utilisation.

Despite these and other efforts it appears that Knowledge Management has come of

age and recent advances in both theory and practice have generally been slow. In a recent

paper Lambe (2011: 193) provided a thorough overview of the antecedents and status of

KM and argues that the current malaise and fragmentation within knowledge

management are at least partially caused by a lack of awareness of its own historical

roots. Earlier Jean-Baptiste, Faucher and Lawson (2008: 3) attempted to reconstitute

knowledge management by extending existing models adopting a complexity-based

perspective. This extended model highlights the non-linear relationships among

existence, data, information, knowledge, wisdom and enlightenment.

While there are thus some attempts to advance the theory of Knowledge

Management this has not yet impacted significantly on KM practice. Despite this

apparent lack in progress a general understanding has emerged the last few years of what

constitutes Knowledge Management and it is now generally acknowledged that a socio-

technical approach is required.

KM is defined by a set of processes and knowledge practices. Birkenshaw (2001: 33)

argues that KM is a „„set of techniques and practices that facilitate the flow of knowledge

into and within the firm.” Standards Australia (2003: 1) defines knowledge management

as “a multi-disciplined approach to achieving organisational objectives by making the

best use of knowledge. It involves the design, review and implementation of both social

and technological processes to improve the application of knowledge, in the collective

interest of stakeholders.”

KM is therefore comprises both social and technological processes. This paper will

explore in particular two key elements, namely (1) The processes underpinning

knowledge management and (2) the emerging practice and tools related to visual

analytics to create organizational knowledge. This paper will subsequently review these

two elements and argue that with the exception of the analysis of unstructured content,

most KM processes have reached an acceptable level of maturity. Areas for improvement

within the analytical process will be identified and the concept of Visual Analytics will

be advanced as an emerging and highly relevant concept in order to improve Knowledge

Management practice.

2.1 Knowledge Management Process

In an extensive review of the literature spanning from 1994-2009 Mishra and

Bhaskar (2011: 347) concluded that the processes of KM is centred broadly on

knowledge creation, knowledge sharing, knowledge up-gradation, and knowledge

application. In a study by Kippenberger (1998: 14) involving nearly 40 respondents, the

majority of respondents agreed that KM is defined as “the collection of processes that

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govern the creation, dissemination, and utilisation of knowledge to fulfil organisational

objectives.”

Jean-Baptiste et al. (2008: 4) argue that it is not so much the elements of a KM

system that is important, but more comprehension of how they interact. The traditional

view of knowledge based on the data, information, knowledge and wisdom (DIKM)

construct cannot be mixed among themselves and are perceived as distinct and separate

categories. New data, information, knowledge and wisdom are respectively added to their

established base. Jean-Baptiste et al. (2008: 7) challenge this traditional, linear and

mechanistic view – a position which will be adopted in this paper that there is

interconnectedness between these constructs which cannot be dealt with separately.

These KM processes will be explored in more detail in the sections to follow. The

following diagram visually displays the primary Knowledge Management processes and

associated practices with each process.

Fig. 1: The KM Process and supportive activities (Self constructed).

2.1.1 Organizational Knowledge Creation

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Organizational knowledge creation is the result of organizational learning. It remains

a basic truth: competitive success is the result of superior learning and this is especially

so in times of turmoil and crises (Bottger and Barsoux, 2008: 1).

The concept of organizational knowledge and learning has been researched for many

years and is arguably one of the most mature areas of knowledge management. In 1995

Nonaka and Takeuchi introduced their Theory of Organizational Knowledge Creation

and launched their SECI modela which still remains one of the most influential models of

organizational knowledge creation. Nonaka and Takeuchi (1995) proposed that

organizational knowledge is created through the continuous social interaction of tacit and

explicit knowledge involving four sequential modes of knowledge conversion:

socialization, externalization, combination and internalization, before returning once

more to socialization. The SECI model remains a highly influential model for

organizational knowledge creation. However, building on the work of a number of

authors Gourlay (2006: 1416) heavily criticised the model which does not explain how

new ideas are produced, nor how depth of understanding (necessary for expertise)

develops. The model furthermore neglects previous research, makes a mystery of

collaboration, and their model of knowledge remains unconvincing. The model also

overemphasises the role of tacit knowledge in organizational knowledge creation.

Despite these criticisms Nonaka‟s theory highlighted important issues. Building on

the earlier work of Polanyi (1958) who coined the term tacit knowledge this theory

identified two main sources of organizational knowledge, namely tacit and explicit

knowledge. Harris and Berg (2003: 5) explain explicit knowledge as knowledge that has

been captured and is represented in an explicit form (such as, a database, Web content, a

document or an e-mail). Explicit knowledge is pervasive in information management. It

is embedded in business rules and metadata that drive work management and business

process automation.

Tacit knowledge on the other hand is by definition not captured. People carry tacit

knowledge around in their minds in the form of insight, judgment, experience,

craftsmanship and creative talents – this knowledge can be expressed or represented in

some way, but never fully captured. Employees therefore carry around much of an

enterprise's tacit knowledge (Harris and Berg, 2003: 5).

This paper does not deny that some of the most valuable knowledge from an

enterprise perspective is tacit knowledge. However, the focus of this paper is on

enterprise content and in particular unstructured content. Following on Jean-Baptiste‟s et

al. (2008: 4) earlier argument one should clearly understand the relationship between

data, information and knowledge. Data comprises both structured and unstructured data.

Structured data falls within the domain of data mining and advanced statistical software

tools are used to interrogate structured qualitative data to discover patterns which could

assist in planning and decision-making. Unstructured data on the other hand is found in

a Socialization, Externalization, Combination and Internalization (SECI)

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documents, emails and other formats which are collectively known as unstructured

content.

Until now organizational knowledge creation was mainly limited to two main

strands. Tapping and capturing employees‟ tacit knowledge is a well-researched topic. It

is also well applied in organizational learning theories and through various models such

as the SECI model and using KM practices such as Lessons Learnt and After Action

Reviews (AARs). Secondly, and in addition to capturing tacit knowledge there are many

attempts to analyse the structured content / data of an organization through the use of

business intelligence statistical software and tools.

However, the biggest challenge is the analysis of unstructured content and to create

organizational value from the mass of unstructured content available within

organizations. Some developments in Social Network Analysis (SNA) have attempted to

research the impact of knowledge and social networks. This had limited value in creating

deep organizational knowledge. The next real frontier in KM is the challenge to analyse

its unstructured content. Recent developments and technological advances in the field of

visual analytics now present forward-looking organizations with the ability to tap into

this mostly unused source of organizational information to create usable knowledge

which will bring added business value.

Organizational knowledge creation is mostly associated with organizational learning

theories and where the human element is the primary focus. Knowledge Management

practices such as After Action Reviews (AARs), Best Practices and Lessons Learnt are

closely associated with generating organizational knowledge and have been extensively

researched and applied in practice and is not the focus of this paper.

On the other, the use of analytical reasoning tools in knowledge creation is limited to

tools such as mind maps and other similar software. In order to create organizational

knowledge a process of analysis must take place. In other words, data is taken and

interrogated with the aid of analytical tools. The data is analysed to show trends and

patterns which allow the analyst to interpret vast amounts of unstructured data on which

decisions can now be made. Once this information is acted upon one can learn from it

and hence new knowledge is created. The use of analytical tools to aid interpretation will

be the primary focus of research in the next section.

2.1.2 Analysis and Interpretation

Knowledge discovery and interpretation are essentially human actions. Analysis

occurs in two ways, namely: (1) humans use their analytic capability to analyse and

interpret content and (2) using advanced statistical software to analyse vast amounts of

structured data. The latter aspect made rapid advances the last few years and many

vendors provide highly advanced statistical software applications which are used to

interrogate and analyse structured databases. This is mostly referred to as Business

Intelligence and is not the primary focus of this paper.

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From a cognitive perspective analysis of textual, qualitative data remains a difficult

task. Within the field of Competitive Intelligence a number of techniques have emerged

to assist analysts with analytical interpretation of qualitative data. Heuer and Pherson

(2011) described more than 50 structured analytical techniques ranging from Scenarios to

Lynchpin analysis. One of the most advanced and applied techniques today is the

Analysis of Competing Hypothesis (ACH). These techniques are mostly used by political

and intelligence analysts and the extent to which these techniques are applied in business

remain limited. This aspect of analysis falls outside the scope of this paper.

The problem of acquiring and using new knowledge is a major issue for many

modern enterprises striving for increasing their efficiency. The factors that make the task

of data analysis and acquiring new knowledge even more complex are considerable

volumes of data to analyse, the absence of formalised models of objects studied, the

necessity to obtain prior knowledge about data received, limitations of technology and

inconsistency of data (Andreev, Volhontsev, Iwkushkin, Karyagin, Minakov, Rzevski

and Skobelev, N.D:1).

Until recently the above was a major shortcoming and the analysis of unstructured,

text-based data was limited and in most cases not possible. This approach is also referred

to as text harvesting. However, with the emerging field of Visual Analytics attempts to

analyse this vastly untapped source of organizational knowledge has now become reality.

This concept and the approaches used will be explored in much greater detail in the

section dealing with Visual Analytics.

2.1.3 Organize and Storage

The organization and storage of corporate information underwent a number of

changes during the last decade. Enterprise Content Management is now an acceptable

concept and also changed a number of times. AIIMb defined ECM as “the strategies,

methods and tools used to capture, manage, store, preserve, and deliver content and

documents related to organizational processes. ECM tools and strategies allow the

management of an organization's unstructured information, wherever that information

exists.” (2011: 1).

Unstructured content enters an organization's IT infrastructure from a variety of

sources such as email, instant message, text document, spread sheets, electronic forms

and paper documents. Taxonomy provides a formal structure for information, based on

the individual needs of a business. Categorization tools automate the placement of

content (document images, email, text documents, i.e., all electronic content) for future

retrieval based on the taxonomy (AIIM, 2011: 1).

The organization and storage of data is a well understood and applied field within

Information Management and most organizations have implemented and applied

b Association for Information and Image Management

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taxonomy structures. A number of taxonomy standards have been developed such as

Ontology Inference Language (OIL), the Dublin Core standardc and Topic Maps. Topic

maps are a new ISO standard for describing knowledge structures and associating them

with information resources. As such they constitute an enabling technology for

knowledge management (Pepper, 2000: 1).

Organizations have been mostly successful in managing their content through

properly designed taxonomies and accompanying metadata. This paper will therefore not

explore this in greater detail.

2.1.4 Share and Distribute

Collaboration is a vital cog in the “wheel” of knowledge management. The exchange

of knowledge between individuals and enterprises is accomplished by knowledge sharing

technology, enabling tools that provide communication and knowledge capture in the

form of wikis, blogs, online repositories, and instant messaging applications (Hedgebeth,

2007: 49).

Arguably the most popular tool to share and distribute information in a company is a

corporate intranet. An intranet is an internal or private computer network that is

accessible only to people within an organization. Increasingly, intranets are being used to

deliver tools and applications with the purpose to collaborate and share information.

Within the KM discipline the sharing and distribution of knowledge is probably the

most widely researched and applied practice. In particular, corporate intranets are

depicted as a key part of the solution to the knowledge management. Intranets use

internet technology to build internal computer networks, thus offering the potential for

information sharing and collaboration across departments, functions and different

information systems within the organization.

However, the advances in technology and the use of intranets have not removed the

challenges related to the obstacles posed by corporate culture not conducive to

knowledge sharing. Practices such as Communities of Practise (CoPs) are used in

attempts to change employees‟ behaviour to adopt a culture of sharing and collaboration.

These are well known and used practices. The recent advances in Web2.0 technologies

and Wikis as information sharing platforms have greatly contributed in advancing

knowledge management in practice and will not be explored in this study.

2.1.5 Apply and Adapt Knowledge

Applying knowledge to inform decision-making is one of the last but important steps

in the knowledge management cycle. Tally (2011: 3) argues that decision making is not a

well-defined field. It includes a variety of processes that are all intermediate steps

c ISO 15836:2009 establishes a standard for cross-domain resource description, known as the Dublin Core Metadata Element Set

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between thought and action. They are the precursors to behaviour. They express our ideas

into their active consequences in the world.

In an extensive study covering over 90 companies Nicolas (2004: 23-29) argued that

KM has evolved and is now in a strategic-learning phase where learning priorities are

aligned with business strategies which necessitates that KM should have an impact on

decision-making. Nicolas (2004: 26) identified three phases in the decision-making

process, namely intelligence, conception and selection. He concluded that both tacit and

explicit knowledge are involved through the decision-making process but with different

intensity in each phase. His study concluded that KM plays a role in decision-making

though it might fluctuate in intensity depending in which phase of decision-making it is

used.

It appears that this aspect of KM is less researched and focussed on than the other

KM processes. However, it falls outside the scope of this study and will not be explored

in any depth.

2.1.6 Reflection / Review

The use of reflective practices has been well researched within the field of

Organizational Learning. Reflection is an active process. It involves the examination of

past experiences and gaining some conclusions that can inform future activities. Raelin

(2002: 66) states that reflection is the “practice of periodically stepping back to ponder

the meaning of what has recently transpired to us and to others in our immediate

environment. It illuminates what others and we have experienced, providing a basis for

future action. In particular, it favours the process of inquiry, leading to an understanding

of experience that may have been overlooked in practice. (. . .) It typically is concerned

with forms of learning that seek to inquire about the most fundamental assumptions and

premises behind our practices.”

One form of learning that has emerged from reflective practice is the After Action

Review (AAR) which is widely used within Knowledge Management. AAR is a process

technique that uses a review of experience to avoid repeated mistakes and reproduce

success. Initially developed by the United States Army, many organizations have adopted

and employed the process. AARs have been extensively researched by Dixon (2000),

Garvin (2000) and others.

2.2 Assessment of the KM Processes

The preceding literature review indicates that most elements of the KM process are

well developed and embedded in knowledge intensive enterprises. Most of these

processes are also well supported with IT tools such as portals, Business Intelligence

software and collaborative tools such as wikis. KM practices such as Communities of

Practice, After Action Reviews and Lessons Learnt are also widely in use.

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However, it appears that the analysis and interpretation process is a neglected area of

both research and practice. With the exception of the use of Business Intelligence and the

analysis of structured data using data mining techniques this field requires much more

attention to improve the discipline of Knowledge Management. Unstructured content

comprises a vast amount of an organization‟s information assets and should be a core

focus area of Knowledge Management.

The recent emergence and developments in the field of Visual Analytics provide KM

with new tools and impetus to improve its practice. The following section will explore

Visual Analytics and its relevance for KM in more detail.

2.3 Visual Analytics

Visual analytics has become the most optimal way for people to explore and

understand data of any size. Technologies based on visual analytics have moved from

research into widespread use in the last few years, driven by the increased power of

analytical databases and computer hardware (Hanrahan, Stolte and Mackinlay, 2009:1).

Knowledge-assisted visualization has been a fast growing field because it directly

integrates and utilizes domain knowledge to produce effective data visualization (Wang,

Jeong, Dou, Lee, Ribarsky and Chang, 2009: 1). Similarly, content analytics has the

potential to transform the way companies operate in ways both large and small, according

to industry watchers and analysts. The latter noted that text analytics tools are getting

better at interpreting the contents of documents, records and other text-based information

(Kelly, 2011: 1).

Visual analytics integrates new computational and theory-based tools with

innovative interactive techniques and visual representations to enable human-information

discourse. The design of the tools and techniques is based on cognitive, design, and

perceptual principles. This science of analytical reasoning provides the reasoning

framework upon which one can build both strategic and tactical visual analytic

technologies for threat analysis, prevention, and response. Analytical reasoning is central

to the analyst‟s task of applying human judgments to reach conclusions from a

combination of evidence and assumptions (Thomas and Cook, 2006: 4).

Hanrahan et al. (2009: 2) define visual analytics as the process of analytical

reasoning facilitated by interactive visual interfaces. Thomas and Cook (2005: 33)

expanded this and state that visual analytics strives to facilitate the analytical reasoning

process by using software that maximizes human capacity to perceive, understand, and

reason about complex and dynamic data and situations. It must build upon an

understanding of the reasoning process, as well as an understanding of underlying

cognitive and perceptual principles, to provide organizational and business appropriate

interactions that allow analysts to have a true discourse with their information.

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Hanrahan et al. (2009: 3) identified seven essential elements of a visual analytical

application. The following three characteristics are highlighted here namely: Visual

Exploration, Augmentation of human perception and Visual Expressiveness.

Visual Exploration: Querying, exploring and visualising data as a single process.

Augmentation of Human perception: Visual thinking is explored and encouraged –

the brain‟s ability to process pictures faster than text is leveraged. Genuine visual

analytics applications encourage visual thinking by leveraging the powers of human

perception. The human brain possesses an amazing capacity to process graphics

faster than it can process tables of numbers.

Visual Expressiveness: visual displays have depth, flexibility and multi-dimensional

expressiveness.

TechTarget (2011: 2) argues that one of the reason visual analytics is so important is

the iterative questioning of the data that it enables. Analysts can interact with the data in a

number of ways to create different visualizations. The following diagram represents how

information is exploited through a human-machine interaction in order to create

knowledge.

Fig. 2: Information Retrieval as adapted from FPS (Goosen, 2011).

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2.5 Conclusion

Recent advances in technology based on Visual Analytics provide KM practitioners

and corporate analysts with highly advanced tools to analyse the unstructured content of

an enterprise. The areas where Visual Analytics can be applied are limitless but include

inter alia Forensic Analysis, Market Research, Competitive Intelligence and other areas

where unstructured content is a primary source of information.

For the purpose of this study sample data were taken from cases in a Customer

Complaints department in the retail sector and policy retention unit in the insurance

industry. Using a Visual Analytical tool the data was analysed to show how large

volumes of unstructured text can be analysed to improve decision-making and

competitiveness.

3. Research Methodology

The research approach adopted in this study is a grounded theory methodology

(Strauss and Corbin: 1998). This approach was adopted since there is a lack of prior

research in this area where visual analytics were used in analysing vast amounts of

textual data in order to create organizational knowledge. Grounded theory allows

tentative theories or theoretical propositions to be explored through additional instances

of data (Schwandt, 2007: 131). This is the case with this research and a grounded theory

approach was therefore considered to be the best research methodology for this specific

research.

The basic idea of the grounded theory approach is to read (and re-read) a textual

database (such as a corpus of field notes) and "discover" or label variables (called

categories, concepts and properties) and their interrelationships. The grounded theory

methodology is thus well suited when using visual analytical tools as it closely resembles

this basic premise of interrogating textual databases. The use of visual analytics is further

reinforced by Charmaz (1990 and 2006) who identified a number of features that all

grounded theories have such as (1) simultaneous collection and analysis of data and (2)

creation of analytic codes and categories developed from data and not by pre-existing

conceptualisations (theoretical sensitivity).

Visual analytics allow the researcher to continuously interact with, and analyse the

data, leading to new discoveries. A Grounded Theory approach is thus deemed to be the

best methodology to conduct this research. Using this methodology this study seeks to

offer a concrete interpretation how visual analytical tools can be used to interrogate and

analyse vast amounts of unstructured data in a customer complaints environment.

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Due to the confidential nature of the data and the results of the analysis the names of

the companies cannot be revealed. The network view image used is also not based on the

data which was analysed and only serves as an illustration of what was done.

3.1 Data Collection

Two case studies were used. The first case study focused on Customer Complaints

received at a Call Centre in the retail sector. These complaints were captured by call

centre operators and were all text based. A total of 20 000 records were received which

were then cleaned and structured into XML categories embedded in the data such as call

centre operator code, store code/location and other relevant fields such as date and time.

The second case study relates to a call centre in the insurance industry. The objective

was to determine reasons why clients cancel or move their insurance policies, sometimes

to competitive firms. A total of 10 000 records were received as a pilot study.

3.2 Data Analysis

Data analysis was done using a Visual Analytical tool from Future Point Systems

(FPS)d. This tool makes use of highly advanced computational linguistics software to

analyse text and to make a number of visualisations possible. The data was provided in

Microsoft Excel (*.xslx) files where the fields contained text and not numbers. Using a

configured processor in Future Point Systems Starlight Data Engineer (SDE) the Excel

files were initially converted to XML and then the content was processed to identify and

normalise entries and identify possible relationships between the entities. The SDE

processor output was saved as XML which was then imported into Starlight Visual

Information System (VIS) for analysis.

The first view analysts normally use to get a high level overview is called a Topic

view. The Topic View enables users to quickly look for groups of similar documents by

subject matter or theme using the unstructured text in the data. This unique visualization

technique enables users to quickly explore qualitative data. The image in Figure 2

represents a topic view of the customers‟ complaints in the retail industry. Similar words

/ text were grouped together to quickly allow the analyst to what records were grouped

together. These include high density clusters such as chicken, disappointed with quality,

packaging, rotten and milk sour. The colour coded bar on the right provided 95 different

areas of operations like Butchery, Consumables, Seafood Counter, etc. and combined

with the topic clustering quickly gave a visual interpretation where the major areas of

complaints originated from.

d For more detail visit http://www.futurepointsystems.com

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This view provided a high level overview of where problems exist in the customer

complaints area. From this view the data can now be manipulated to provide different

views of exactly the same set of data, and/or to drill down into specific topic clusters such

as [chicken] to get a more detailed view of what the specific complaints about that

specific cluster are.

Fig. 3: Topic View: Customer Complaints in a Retail Company.

The second visualization used in the same set of retail data is the Category View

(Figure 4). This visualization collects values from sets of data, sorts the values into

distinctive buckets, or categories and subcategories, and presents that information

visually to the user. This view illustrates how values are distributed throughout a set of

records.

The category view highlighted which products received the most complaints (Food

category - Milk, Salads). Food complaints were mostly about foreign objects in food and

could also be linked to specific suppliers / specific shops.

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Fig. 4: Category View: Customer Complaints in a Retail Company.

The visual analysis of these customer complaints brought new insight into the

company‟s customer complaints and created valuable knowledge which was previously

unknown. The analysis of more than 20 000 records containing large amount of free text

was previously also not possible. The company could take immediate actions to improve

the situation and unlocked huge value from their unstructured content. One example was

that the company was unaware of the extent to which customers were dissatisfied with

the packaging quality of the products. This could now be investigated in detail to ensure

better customer satisfaction.

The third visualization (Figure 5) is based on applying the knowledge manager

network view. This visualization was applied to a set of over 10 000 records from a

company in the insurance industry and linked customer cancellations of policies to

reasons why these are being cancelled and to which brokers and/or intermediaries these

were linked. The most important aspects which emerged from this visualization were:

The most compelling reason for changing policies was that better benefits from other

policies were available.

Many customers were unaware that the company offers similar and in fact some

cases better options than competitors. In these cases the policy was cancelled and

another was issued with the same company.

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In a minority of cases it became clear that some brokers moved the policy to

competitive companies. These brokers probably got better benefits from competitive

companies and the company in question could make now changes in their broker

policy in order to retain as many customers as possible.

Nine call centre operators were employed but more than 50% of calls were captured

by only two operators. This allowed for mistakes to occur in data capturing and

potential bias to develop.

The analysis highlighted one specific competitor product as a major threat in moving

policies.

The following visualization is an example of how this was done. Due to the

confidential nature of names and companies in question the original data cannot be used.

Fig. 5: Visual Analytics – Knowledge manager Network view.

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3.3 Summary: Visual Analytics and Organizational Knowledge

This paper attempted to show that the KM process of analysis and interpretation can

be much improved through the use of Visual Analytical tools, that the latter can unlock

business value from previously untapped information sources and that it challenges the

traditional model and view of how the DIKM model is perceived.

From the research presented it is clear that Visual Analytical tools give organizations a

powerful ability to analyse unstructured content and an ability to tap previously unused

source of information. It revealed insights into previously unknown trends and visual

interpretation allowed deeper insight into and use of organizational information assets.

Unstructured content comprises up to 80% of organizations content. Leveraging the

advances in the field of Visual Analytics many opportunities present itself to create

organizational knowledge and subsequent business value.

Most of the KM processes have reached an acceptable level of maturity in both the

use of technology and practical understanding and application. The one exception is the

analysis of unstructured content which relies heavily on human interaction. Visual

representations translate data into a visible form that highlights important features,

including commonalities and anomalies. These visual representations make it easy for

users to perceive salient aspects of their data quickly. The human mind is limited in the

amount of data it can absorb and analyse. With Visual Analytical tools analysts are now

able to analyse vast amounts of unstructured, text content and allow analysts to interact

with the data to enable them to interpret and analyse large volumes with ease.

There is little doubt that developing a visual analytical capability in an organization

has advantages. One of the most important ones is the ability to interrogate and analyse

vast amounts of information through technological tools which the human brain is not

capable of. Tapping into this vast and mostly unused source of information asset will

certainly provide companies with a competitive advantage.

In order to develop this ability companies will face some challenges. These include

developing the skills and expertise to use analytical tools and to obtain relevant talent.

The latter will in the short term be one of the biggest challenges. Analysts and employees

will have to familiarise themselves with the emergence of new terminology in this field

and the functionalities associated with new technology. While the use of visual analytical

tools will not be for everyone in the organization managers need to have a sound

understanding of information management and how new technology can create business

value.

This constant interaction between analyst and data creates an iterative process

between data, information and knowledge. This constant non-linear interaction between

these constructs challenges the traditional view of the linear DIKM model and provides

practical evidence of earlier theoretical claims brought against the traditional, linear view

of the DIKM model.

18

As a research agenda, visual analytics brings together several multi-disciplinary

disciplines including computer science, information visualization and cognitive sciences.

Similar to the multi-disciplinary nature of Knowledge Management these present

countless research opportunities. Within the field of Knowledge Management

practitioners and theorists need to urgently take notice of these developments in order to

stay relevant and to contribute to a vast expanding field and body of knowledge.

The next and previously unconquered frontier in Knowledge Management has

arrived. It is now possible to analyse large amount of unstructured content and companies

need to develop the necessary skills and tools if they want to remain competitive in a

rapidly developing field of organizational knowledge management.

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