Final Year Project

FROM DATA TO INFORMATION
AND KNOWLEDGE MANAGEMENT.
ARE INFOGRAPHICS AND DATA
VISUALISATIONS THE MISSING
LINK?
AN INVESTIGATION INTO DETERMINING WHETHER
INFOGRAPHICS AND DATA VISUALISATIONS CAN
CREATE EFFECTIVE INFORMATION AND
KNOWLEDGE MANAGEMENT.
This project is submitted in partial fulfilment of the
requirements for the award of Bachelor of Science degree of
Loughborough University
Adepeju Abiola
Supervisor: Professor Tom W. Jackson
2015

1
ABSTRACT
Objective: Existing research identifies Infographics and Data Visualisations as an
understated method for communicating information to mass audiences.
Background: Infographics and Data Visualisations are visual representations of data,
information and knowledge. Using Infographics/Data Visualisations for the purpose of
Information and Knowledge Management is a relatively new research area, which is
evident from the lack of research combining the two topics.
Method: This project attempts to build upon insight generated regarding the recently
realised benefits of using Infographics and Data Visualisations, by tying this notion into
the more established topic of Information and Knowledge Management, using a
combination of research methods which include an experimental comparative study
and case study examination.
Results: The findings from the study were convergent with certain themes in previous
research conducted in both these areas. Information represented visually was faster
understood compared to the same information presented as plain numbers and text.
Infographics and Data Visualisations can indeed contribute to effective Information
and Knowledge Management. The results also brought to light good and bad
Infographic/Data Visualisation design and its impact with regards to understanding
information. Whilst promising, there were aspects of the comparative study within the
project which could have been improved upon, namely the sample size. Increasing the
number of participants significantly would have been extremely advantageous.
Conclusion: There are a number of real-world applications which may benefit from
research of this nature, predominately in higher education institutions, as the
participants used in the study came from a university population. Other areas in
organisations such as marketing and advertising and consumer insight have also been
identified as beneficiaries from this research project.

II
ACKNOWLEDGEMENTS
Firstly, I would like to thank my supervisor Tom Jackson for his continued guidance
and patience over the year and my personal tutor Louise Cooke for her continued
support and advice for the past four years.
I would also like to thank Gifty Opoku for being a wonderful friend throughout our
four years in university.
Finally, I would like to thank my family for their unfailing and never-ending love, and I
dedicate this project to my mother.

III
CONTENTS
List of Figures.............................................................................................................................................................................v
List of Tables ..............................................................................................................................................................................v
List of Appendices ....................................................................................................................................................................v
Chapter 1 – Introduction.........................................................................................................................................................1
1.1 Background .......................................................................................................................................................................1
1.2 Aims and Objectives ......................................................................................................................................................1
Chapter 2 – Literature Review...............................................................................................................................................3
2.1 Information Management............................................................................................................................................3
2.2 Knowledge Management ............................................................................................................................................6
2.3 Intellectual Capital – The Ultimate Goal? .............................................................................................................8
2.4 The Digital Revolution and Big Data ......................................................................................................................8
2.5 Infographics and Data Visualisations ....................................................................................................................10
2.6 Summary ........................................................................................................................................................................10
Chapter 3 – Methodology .....................................................................................................................................................12
3.1 Introduction ...................................................................................................................................................................12
3.2 Participant Demographics.........................................................................................................................................14
3.3 Data Collection .............................................................................................................................................................14
3.4 Data Analysis.................................................................................................................................................................15
3.5 Case Study Analysis .....................................................................................................................................................16
Chapter 4 – Findings..............................................................................................................................................................17
4.1 Response Rate................................................................................................................................................................17
4.2 Microsoft Excel Proficiency ......................................................................................................................................17
4.3 Statistical analysis ........................................................................................................................................................18
4.3.1 One Sample T-Test................................................................................................................................................18
4.4 Study Question 1 – Which Fish are OK to Eat? .................................................................................................20
4.5 Study Question 4 – UK Political Parties................................................................................................................22
4.6 Study Question 5 – The Tax Gap.............................................................................................................................23
4.7 Study Question 6 – International Number Ones ...............................................................................................25
4.8 Study Question 7 – Poll Dancing ............................................................................................................................27
4.9 Average response Time For Each Question........................................................................................................28
Chapter 5 – Analysis..............................................................................................................................................................29
5.1 Relating Findings to Previous Research ................................................................................................................29
5.2 Real-World Application .............................................................................................................................................31
5.2.1 Marketing, Advertising and Consumer Insight..............................................................................................31

IV
5.2.2 Storytelling..............................................................................................................................................................32
5.3 Good and Bad Infographics/Data Visualisations ................................................................................................33
Chapter 6 – Case Study Discussion ...................................................................................................................................35
6.1 IBM ...................................................................................................................................................................................35
6.2 Unilever .........................................................................................................................................................................38
6.3 Summary .......................................................................................................................................................................40
Chapter 7 – Conclusion ........................................................................................................................................................42
7.1 Limitations.....................................................................................................................................................................43
7.2 Infographic/Data Visualisation Framework........................................................................................................44
Bibliography ............................................................................................................................................................................47
Appendix ...................................................................................................................................................................................55

V
LIST OF FIGURES
Figure 1 - The Communication Process ..............................................................................................................................5
Figure 2 - Self Rating of Excel..............................................................................................................................................18
Figure 3 - Graph showing comparison between Participant Set A and Set B for Question 1 ..........................20
Figure 4 - Infographic used for Question 1 ......................................................................................................................21
Figure 6 - Infographic used for Question 4.....................................................................................................................22
Figure 8 - Data Visualisation used for Question 5 .......................................................................................................24
Figure 10 - Infographic used for Question 6 ..................................................................................................................26
Figure 11 - Graph showing comparison between Participant Set A and Set B for Question 7..........................27
Figure 12 - Data Visualisation used for Question 7 .......................................................................................................27
Figure 13 - Graph showing comparison between average time for Participant Set A and Set B.....................28
Figure 14 - Framework for Infographic/Data Visualisation Creation .....................................................................45
LIST OF TABLES
Table 1 - Participant Set A One Sample T-Test ..............................................................................................................19
Table 2 - Participant Set B One Sample T-Test..............................................................................................................19
LIST OF APPENDICES
Appendix A – Shannon and Weaver’s Model of Communication............................................................................55
Appendix B - Russell Ackoff’s DIKW Pyramid...............................................................................................................55
Appendix C – Nonaka and Takeuchi’s SECI Model .....................................................................................................56
Appendix D – Invitation for Study....................................................................................................................................56
Appendix E – Additional Infographics/Data Visualisations Used in Study...........................................................57
Appendix F – Snapshot of Raw Data Sets Used in Study ...........................................................................................62
Appendix G – Powerpoint Used for Study......................................................................................................................65
Appendix H – Answer Sheet for Participants ................................................................................................................70
Appendix I - Additional Graphs Showing the Comparisons Between Participant Set A and Set B:
Questions 2, 3 and 8 ...............................................................................................................................................................72
Appendix J - Gantt Chart ......................................................................................................................................................73

1
CHAPTER 1 – INTRODUCTION
1.1 BACKGROUND
While visual methods in sociology and anthropology today may rejoice in a growing
number of enthusiasts, along with a growing number of sceptics, most social scientists
are unaware of their existence or potential. Visual sociology and visual anthropology
are grounded in the idea that valid scientific insight in society can be acquired by
observing, analysing and theorising its visual manifestations.1
Moreover, in today’s society there is an increasing importance of Information and
Knowledge within organisations due to the ubiquity of information, as well as the
recognition of the value of intellectual capital not only for organisations, but for
national economies. Knowledge Management offers competitive advantages when it is
well managed and sustainable, by making the most out of tangible and intangible
resources available.
Combining Infographics and Data Visualisations with Information and Knowledge
Management further greatly amplifies the benefits realised, compared to utilising just
one of the tools.
1.2 AIMS AND OBJECTIVES
The aim of this project is to examine the relationship between Infographics/Data
Visualisations and Information and Knowledge Management by exploring the effects of
visually represented information on understanding and knowledge creation.
1. To design a controlled and comparative study that will involve two sets of
participant groups being exposed to a preselected list of information to see whether the
visual information is better or faster understood compared to the same information in
its raw, visually unaltered format.
2. To produce a review of appropriate literature critically evaluating relevant
theories and methodologies.
1 Margolis, E. and Pauwels, L. (2011). The SAGE handbook of visual research methods. Los Angeles: SAGE,
p.3.

2
3. To analyse examples of Infographic/Data Visualisation used in companies, and
how it has helped or hindered Information and Knowledge Management.
4. To reach a conclusion as to whether Infographics/Data Visualisations are useful
tools in the realm of Information and Knowledge Management and if so, at what stage
of the Information and Knowledge Management cycle they will best serve.
5. To create a framework that will help designers ensure their Infographic/Data
Visualisation meets the intended purpose for their audience.

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CHAPTER 2 – LITERATURE REVIEW
This chapter provides an analytical and critical review of a number of previous research
papers and publications related to the topic of Information and Knowledge
Management, as well as examining material related to the relatively new notion of
Infographics and Data Visualisations. Although not the focus of the research, the
Digital Revolution cannot be ignored. This particular phenomenon has played an
important role in the way in which organisations and individuals disseminate and
absorb in today’s information age.
2.1 INFORMATION MANAGEMENT
Information is derived from the word ‘inform’ which means to give shape to. It is an
aggregation of data that makes decision making easier involving facts and figures based
on reformatted or processed data. The focus of information is qualitative.2 Information
Management should be seen as the conscious process in which all information is
gathered and used to assist in the decision making process.3
The process of Information Management involves four steps:
Gathering
This part of the process includes all the activities one is engaged in to collect
information required. This can range from simply receiving information from people
who send it via a conversation for example, or having to actively seek the information
in the form of interviews or questionnaires. Choo describes this stage as ‘Scanning the
Environment’4. His publication addresses Information Management from a business
perspective, referring to companies who actively engage in the Information
Management cycle as ‘Intelligent Organisations’ as well as noting that managers are
depicted as ‘Information Seekers’ by seeking information not only from the external
2 Awad, E. and Ghaziri, H. (2004). Knowledge management. Upper Saddle River, N.J.: Prentice Hall,
pp.36-37.
3 Hinton, M. (2006). Introducing information management. Oxford: Elsevier Butterworth-Heinemann, p.2.
4 Choo, C. W. (2002). Information management for the intelligent organization: the art of scanning the
environment. Information Today, Inc.

4
business environment they operate in but also from the internal environment via key
stakeholders i.e. employees, suppliers and customers.
Analysing
Choo also describes managers as ‘Information Users’. This stage is crucial in forming
strategic aims, objectives and policies which steer a company in a particular direction.
The primary purpose of analysing information is to make it more useful for decision
making. This step of the process can be considered as transforming raw data into
meaningful information. This can involve a variety of manipulations that can take place
a number of times which can result in more meaningful information. The wrong type of
analysis can mean the wrong strategic aims and objectives are chased. Baets5
emphasises the need to align information with strategy and discusses the role that
Information Systems play in ensuring information helps rather than hinders company
success, predominately through integration. Baets acknowledges that different
organisations will have different information capabilities and different information
needs. The critical success factor in this step is for an organisation to identify effective
strategies within the information that is synthesised to attain competitive advantage.
Communicating
Baets discusses also the role of communication within Information Management.
Failure is imminent when there is a lack of clear understanding of corporate strategy
amongst managers within different functions and departments. This step is where most
problems arise within the process. Shannon and Weaver’s model of communication6 is
based on probability theory and has been applied to a wide range of fields including
engineering, psychology and social policy. However, communication is no longer a
linear process, so the model is less relevant today, but still a valid example of what the
communication step must achieve, which is to:
5 Baets, W. (1992). Aligning information systems with business strategy. The Journal of Strategic
Information Systems, 1(4), pp. 205-213.
6 See appendix A for Shannon and Weaver’s Model of Communication

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Figure 1 - The Communication Process
Storing
This is also a poorly performed step. It is often the case that a lot of information is
generated, used for a specific purpose and then forgotten about. Information
Documentation and storage is important. IFAD.org identifies this stage as crucial for
reasons of communication, transparency, consensus building and continuity of
consultative processes.7 Information warehouses, data centres and databases can all be
identified as a type of information storage. Institutions and bodies which specialise in
the field of Information Management have gone beyond the immediate scope of
information to identify systems such as Service Knowledge Management Systems which
serve as the central hubs for organisations that are heavily invested in Information and
Knowledge Management that do not only encompass the storing of IT systems and
software, but also integrate this with the storage of Information and Knowledge.
Existing literature concludes that information should be stored for three reasons – for a
possible future use, for higher management and for Information Auditing which is the
process by which systematic examination of information use, resources and flows, with
7 Ifad.org, (2015). Managing for Impact in Rural Development - A Guide for Project M&Es. [online]
Available at: http://www.ifad.org/evaluation/guide/index.htm [Accessed 7 Mar. 2015].
1
• Formulate a message by deciding what to say, who tosay it toand how
to say it.
2
• Transmit the message by an appropriate means of communication–
television, phone call, leafletetc.
3
• Reception’s success is affected by the two preceding points and
whether the recipient is at risk of information overload.
4
• Interpretation involves the issue of whetherthe recipient understands
the message in the way the senderintended.

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a verification by reference to both people and existing documents is done in order to
establish the extent to which they are contributing to an organisation’s objectives8.
Information Auditing also sheds light on the suggestion that the DIKW9 process is in
fact cyclical or an inwards moving spiral. Once an Information Audit has taken place, it
paves the start of the process i.e. gathering, and each time a cycle is completed it
generates deeper insights.
2.2 KNOWLEDGE MANAGEMENT
Knowledge can be described as actionable and relevant information available in the
right format, at the right time, and at the right place for decision-making.10
The strategic purpose of Knowledge Management activities is to increase intellectual
capital and enhance organisational performance;11 it involves a human dimension of
developing knowledge in individuals which occurs through different learning processes.
In an organisational context, Knowledge Management uses accessible knowledge from
external sources whilst embedding and storing knowledge in business processes,
products and services by representing knowledge in databases and documents,12 which
can relate back to Service Knowledge Management Systems discussed earlier.
Believing knowledge to be a ‘thing’ or an object which is open to being managed by a
‘subject’ or manager can lead to problems and challenges.13 The likeness is with
‘managing culture’ - seeing culture as an independent set of variables which become
embodied in organisations and which can be manipulated or managed suitably by the
appropriate people with the right skills. However it is now widely accepted that culture
is not an ‘add-on’ to organisations. Culture is what an organisation is rather than what
it has. Applying this same logic to Knowledge Management, knowledge is not an add-
8 Orna, E. (1999). Practical information policies. Aldershot, Hampshire, England: Gower, p.69.
9 See appendix B for Ackoff’s DIKW Pyramid
10 Tiwana, A. (2000). The knowledge management toolkit.Upper Saddle River, NJ: Prentice Hall PTR.
11 Jashapara, A. (2011). Knowledge management. Harlow, Essex: Pearson/Financial Times/Prentice Hall,
p.11.
12 Awad, E. and Ghaziri, H. (2004). Knowledge management. Upper Saddle River, N.J.: Prentice Hall, p.3.
13 Quintas, P., Lefrere, P., & Jones, G. (1997). Knowledge management: a strategic agenda. Long range
planning, 30(3), 385-391.

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on to organisations, i.e. an organisation cannot ‘get’ knowledge, and it is part of the
organisation but cannot always easily be identified. However, it is difficult to
confidently state whether knowledge is simply a less discrete resource in the same way
materials can be.
Further pitfalls in Knowledge Management stem from the basis that methods used to
adequately collate and manage knowledge arise from a bias of existing knowledge.
Known knowns and known unknowns are the fundamental building blocks for
Knowledge Management practices – many organisations are aware of what they do not
know, so they must manage their knowledge appropriately to cover these knowledge
gaps. But what about unknown unknowns i.e. the things that they do not know they do
not know? It is difficult for organisations to think outside the box and find solutions
that are uninfluenced by their standard way of thinking or knowledge boundaries i.e.
knowledge from new or unfamiliar disciplines, or knowledge about unfamiliar ways of
doing business.
There are many contributors in the field of Knowledge Management. Nonaka and
Takeuchi have identified a SECI (Socialisation, Externalisation, Combination and
Internalisation) model14 of Knowledge creation which recognises the role of Knowledge
Management in an organisational setting, with an organisation being identified as an
entity that creates knowledge continuously 15 , maintaining its creation whilst
simultaneously exploiting it. The SECI model coupled with the DIKW (Data,
Information, Knowledge and Wisdom) cycle identifies a purpose as to why
organisations are investing into the notion of Knowledge Management, distinguishing
between explicit and tacit knowledge in order to strategically align the aims and
objectives of their organisations to knowledge capacity.
14 See appendix C for Nonaka & Takeuchi 1995 SECI model
15 Nonaka, I., & Toyama, R. (2003). The knowledge-creating theory revisited: knowledge creation as a
synthesizing process. Knowledge management research & practice, 1(1), 2-10.

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2.3 INTELLECTUAL CAPITAL – THE ULTIMATE GOAL?
Intellectual Capital (IC) can be defined as knowledge that has been identified, captured,
and utilised to produce a high value asset16 and is the difference between the market
value of a publicly held company and its official net book value is the value of its
intangible assets.17 The UK Government recognises the competitive advantage IC brings
regarding the UK’s economic position.18 The white paper published in 1998 noted that a
policy for competitiveness should start from the premise that the UK's distinctive
capabilities are not raw materials, land or cheap labour - but rather knowledge, skills
and creativity. The Government identified its role as a body to foster a culture of
entrepreneurship and innovation and to empower firms and individuals to exploit the
potential of the digital age and the knowledge-driven economy.
Whilst this paper was published seventeen years ago, and could be considered out-
dated, the current situation regarding the UK economy and labour show that the
themes highlighted in this publication were indeed very correct. The tertiary or service
sector makes up 83.7 percent of all workforce jobs in England alone,19 and in particular
the finance sector, where looking away from the very obvious resource of financial
capital, Intellectual Capital is perhaps the most valuable resource.
Both Information Management and Knowledge Management can be seen as
component parts of the much broader concept of IC. In 1998, 82.3 percent of 1300 firms
named IC as the critical factor for future business success.20
2.4 THE DIGITAL REVOLUTION AND BIG DATA
"The Digital Revolution marked the beginning of the Information Age; it was the big
bang to a whole generation of techies, hackers, coders, and web-surfers of all ages and
16 Desouza, K. and Paquette, S. (2011). Knowledge management. New York: Neal-Schuman Publishers.
17 Sveiby, K. (1997). The new organizational wealth. San Francisco: Berrett-Koehler Publishers.
18 Pryor, A. (1999). Our competitive future: Building the knowledge-driven economy. Computer Law &
Security Review, 15(2), 115-116.
19 Ons.gov.uk, (2015). UK Statistics - The Economy - ONS. [online] Available at:
http://www.ons.gov.uk/ons/guide-method/compendiums/compendium-of-uk-
statistics/economy/index.html [Accessed 12 Feb. 2015].
20 Bertels, T. and Savage, C. (1998). Understanding Knowledge in Organizations. London: Sage.

9
social backgrounds, of every ethnicity and culture. The Information Age is not only an
era of humanity; it is a way of life. Imagine what is still to come!"21
The CEO of Information Age Technology LLC briefly highlights what the current
situation is regarding information and technology. It is very much a real part of life
today and whilst the way we interact with both data and information has radically
changed, current trends and patterns greatly suggest that we are still in a transitional
stage and that methods and practices are being put in place in order to transform us all
into Information Specialists and Knowledge Workers.
The notion of data is not a new phenomenon; people have been quantifying and
tabulating things for centuries22 however the quantity of data that is produced in the
world is growing at an exponential rate. For example, retailers such as Amazon, eBay,
Tesco and Sainsbury’s have built databases that capture customer data to provide more
tailored services. In addition, organisations that operate in logistics, health and finance
are also capturing data daily. Social media and its increased use in recent years are also
additional sources of available data. New and improved technologies have provided
additional channels for data collection. An example is visual recognition and ‘smart
technology’. As the technology we use becomes more interactive, the companies
behind these technologies have to collect data to ensure that customers are getting a
personalised product or service that meets their needs.
However this sharp rise in data availability has put many people at risk of data and
information overload as individuals and organisations become overwhelmed by the
plethora of information available. This can lead to a reduction in productivity and
performance as well as hindering learning and innovation.23
There are companies investing in ways to combat the implications of Big Data. A
notable example is IBM’s Watson – cognitive technology and a natural extension of
21 Information Age Technology, L. (2015). Information Age Technology, LLC. [online] Linkedin.com.
Available at: https://www.linkedin.com/company/information-age-technology-llc [Accessed 6 Mar. 2015].
22 Yau, N. (2011). Visualize this. Indianapolis, Ind.: Wiley Pub., p.1.
23 Jackson, T. and Farzaneh, P. (2012). Theory-based model of factors affecting information
overload. International Journal of Information Management, [online] 32(6), pp.523-532. Available at:
http://dx.doi.org/10.1016/j.ijinfomgt.2012.04.006 [Accessed 13 Oct. 2014].

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what humans do best.24 Put simply, Watson is a solution to combat the complexity of
the information era that we currently live in with regard to big data as it becomes
increasingly difficult to capture, analyse and store data using traditional data analysis
methods. Systems and technologies similar to Watson play a part in the transformation
of Big Data to Information, leading to knowledge.
2.5 INFOGRAPHICS AND DATA VISUALISATIONS
For many an Infographic is just another word for a Data Visualisation. However, an
Infographic has characteristics not found in a Data Visualisation. Similarly Data
Visualisations have characteristics that can distinguish it from an Infographic.
Infographics are visual representations of information, data or knowledge often used to
support information, strengthen it and present it within a sensitive context. They are
specific and context-sensitive.
Data Visualisations are visual displays of measured quantities by means of the
combined use of a coordination system, points, lines, shapes, digits, letters quantified
by visual attributes. They are general, context-free and often created automatically.25
Both hopefully lead to the still more refined state of knowledge.26
2.6 SUMMARY
The literature review conducted as part of this project has shown a significant
knowledge and research gap with Big Data and Information and Knowledge
Management. There have been multiple discussions surrounding the use of
Infographics and Data Visualisations as well as the general advantages and
disadvantages for the purposes of quick and tidy information transfer. However, there
seems to be a lack of material regarding another key potential benefit – Information
24 Ibm.com, (2014). IBM Watson: What is Watson?. [online] Available at:
http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html [Accessed 8 Sep. 2014].
25 Quora.com, (2014). What is the difference between a data visualization and an infographic? - Quora.
[online] Available at: http://www.quora.com/What-is-the-difference-between-a-data-visualization-and-
an-infographic [Accessed 19 Oct. 2014].
26 Jackhagley.com, (2014). Jack Hagley / Infographic Designer / London. [online] Available at:
http://www.jackhagley.com/What-s-the-difference-between-an-Infographic-and-a-Data-Visualisation
[Accessed 8 Oct. 2014].

11
and Knowledge Management. Research has shown an indication that Infographics and
Data Visualisation provide a missing link between the concept of Big Data and
Information and Knowledge Management, primarily in the Communication Stage.

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CHAPTER 3 – METHODOLOGY
This chapter intends to outline and justify the research methods selected for this
project in order to investigate the relationship between Infographics/Data
Visualisations and Information and Knowledge Management.
3.1 INTRODUCTION
The predominant philosophy for this project takes an inductive reasoning approach
moving from specific observations that will generate quantitative data to form broad
generalisations and theories based on idiographic methods of research in order to
better understand the subjective focus which in this case will be the participants’ speed
of interpreting information. Idiographic research is concerned with using real-world
data, emphasising the analysis of subjective accounts to provide an overall picture of
the situation.27
The reasoning behind this method of research was due to the observational data that
the study would produce, which when combined with case study analysis can be used
to make assertions about the efficiency of Infographics and Data Visualisations for the
purposes of effective information and Knowledge Management.
Previous studies in this joint area of research are limited and have largely focused solely
on Information and Knowledge Management, Big Data or Infographics and Data
Visualisations, not all three, with the latter two topics featuring together in many
publications.
The approach to research in the field of Information and Knowledge Management has
predominately been appraisal, whereby studies have involved evaluation of a subject;
for example an organisation, before and after measures and strategies have been put in
place to improve Information and Knowledge Management within the organisation.
Techniques within this field include observation, interviews and focus groups.
27 Cornford, T. and Smithson, S. (2006). Project research in information systems. Basingstoke: Macmillan,
p.67.

13
The common approach for research in the field of Big Data has a predominately
exploratory approach which also encompasses an explanatory approach, most likely
due to the fact that Big data is still a relatively new area of research if comparing it to
Information and Knowledge Management. Exploratory research is defined as the initial
research into a hypothetical or theoretical idea, where an idea has been observed and
there is a need to understand more about it. Explanatory research is defined as an
attempt to connect ideas to understand cause and effect, in order to explain what is
going on.28
Earlier research surrounding Infographics and Data Visualisation takes a ‘best-practice’
approach. This type of research typically consists of the analysis of multiple
Infographics and Data Visualisations to find common aspects which should either be
embraced or omitted with Infographics and Data Visualisation design – the methods
used to conduct this research for this study incorporate elements of the best-practice
approach.29
In order to achieve the objectives set out in chapter one, the research was separated
into two phases. Phase one was concerned with achieving objective one and five
through the collection of data from observing two groups of participants; group one
who would be assessed to see how quickly the disseminated visually represented
information against group two, who would disseminate raw data and information in
the form on plain text and numbers. However given the very nature in this joint area of
[lack of] research, observation is merely not enough to draw satisfactory conclusions as
to whether Data Visualisations and Infographics are the missing link between Big Data
and Information and Knowledge Management.
Phase two set out to satisfy objective three through case study analysis of two
organisations who have embraced Infographics and Data Visualisation tools for the
28 Study.com, (2015). Purposes of Research: Exploratory, Descriptive & Explanatory - Video & Lesson
Transcript | Study.com. [online] Available at: http://study.com/academy/lesson/purposes-of-research-
exploratory-descriptive-explanatory.html [Accessed 12 Feb. 2015].
29 Bogan, C. and English,M. (1994). Benchmarking for best practices. New York: McGraw-Hill

14
purposes of decision making based on Information and Knowledge Management which
should lead to a more strategically competitiveand effective company.30
3.2 PARTICIPANT DEMOGRAPHICS
It has been identified that the beneficiaries of the outcome of this project will be those
who are somehow involved with higher education i.e. those who constantly need to
convey information to others in order to generate knowledge, predominately lecturers
and students. On the basis of this, students have been chosen as the primary subjects,
of which Loughborough University has over 16,000 enrolled31.
The participants were chosen randomly by a computer generated mechanism. Those
that expressed interest in the project had their details entered into a database and were
assigned a number. The computer then generated a random number relating to a
participant. This was then repeated a further fifteen times in order to generate a sample
size of sixteen using a scale of sample size to actual population of 1:1000.
3.3 DATA COLLECTION
An experimental study design was chosen to collect data for the purposes of internal
validity which is at the centre of all causal or cause-effect inferences.32 The aims and
objectives of this project were based on the hypothesis that if information and data are
represented visually, it will be better and faster understood compared to the same
information in plain numbers and text. An experimental study allowed for both
propositions to be tested i.e. when information is in plain text and numbers, it will be
harder and slower understood than visually represented information.
The experimental study created two participant groups that were equivalent to each
other. One group received visually represented information, and the other group, being
the control group did not. In other aspects, both groups were treated exactly the same
30 Bain.com, (2015). Decision Effectiveness / Decision Making - Bain & Company. [online] Available at:
http://www.bain.com/consulting-services/organization/decision-effectiveness.aspx [Accessed 12 Feb.
2015].
31 University.which.co.uk, (2014). Loughborough University (L79) - Which? University. [online] Available
at: http://university.which.co.uk/loughborough-university-l79 [Accessed 12 Oct. 2014].
32 Socialresearchmethods.net, (2015). Experimental Design. [online] Available at:
http://www.socialresearchmethods.net/kb/desexper.php [Accessed 12 Feb. 2015].

15
– the groups has similar backgrounds i.e. students at Loughborough University, they
were both given the same amount of time to assess the information before answering
questions, and the questions they answered regarding the information they were
exposed to were exactly the same for both participant groups. The only difference
identified was that one group received Infographics and Data Visualisations, whilst the
other group did not i.e. the control to be investigated. The critical success factor of the
study was random assignment, in order to attain two groups that were similar on the
basis of probabilistic equivalence i.e. the chance of both groups being exactly the same
was based on the notion of probability.33
The study consisted of the participant undertaking one task which consisted of eight
parts. Each part related to an Infographic/Data Visualisation or its equivalent data set
depending on which group the participant was in. The participant was then told to
study the information in front of them for ninety seconds before being presented with
one or two comprehension items. The participant was then timed to see how long it
took them to answer questions relating to the information they were presented with.
3.4 DATA ANALYSIS
Distribution Identification was first done on the data collected in order to select
appropriate methods of statistical analysis to perform on the data.
Given the small sample size, one statistical method of analysis was identified as
appropriate on the basis of two-group experimental study design in order to test the
validity, reliability and accuracy of the data.
The One Sample T Test was used to determine if the two sets of data were significantly
different from each other. It enabled inferences to be drawn about the participants in
the study. Ordinarily this test is used when the null hypothesis i.e. the mean of a
33 Elearnportal.com, (2015). Experimental Design. [online] Available at:
http://www.elearnportal.com/courses/sociology/research-procedures-ii/research-procedures-II-
experimental-design [Accessed 12 Feb. 2015].

16
particular sample differs from the mean of a the population only by chance, is to be
tested34
3.5 CASE STUDY ANALYSIS
The decision to do an in depth analysis and critique of two organisations who had
incorporated Infographics and Data Visualisations into business practices was based
upon the desire to shed light on the role Infographics/Data Visualisations play in an
organisational context which would also confirm the findings in the study as well as
further tie in the aspect of Information and Knowledge Management.
In order to get a well-rounded view of the stance of Infographics/Data Visualisations
within organisations, two organisations were analysed from two different viewpoints in
order to gain broader insights.
IBM was the first company selected to critique as it was identified as the fore runner in
the realm of Big Data, and creation of tools for analytics and visualisations. Unilever
was chosen as a company to represent from the viewpoint of Infographics and Data
Visualisations being used as an aid to current business processes in an industry that
would not necessarily be the first or most obvious choice to use Infographics and Data
Visualisations.
34 Burns, R. and Burns, R. (2008). Business research methods and statistics using SPSS. Los Angeles: SAGE,
p.257.

17
CHAPTER 4 – FINDINGS
This chapter looks at the findings gathered from phase one of the research process as
detailed within the methodology of chapter three.
4.1 RESPONSE RATE
As mentioned in the methodology, the ratio of student to sample size decided was
1000:1 therefore the required number of participants was sixteen. Invitations were sent
out to fifty students and thirty-seven students expressed interest in participation. This
equated to a 74 percent response rate. Of those that expressed interest, twenty were
selected – four students were required in order to pilot the study and the remaining
sixteen had their responses included in data analysis. It was acknowledged that a
higher participant rate would have increased internal validity and that the original
agreed sample size could have been doubled given the response rate, on the
assumption that the students who expressed an interest would have committed to the
study.
The final sample was composed of 37.5 percent female and 62.5 percent male. In
addition, 25 percent were students in their first year, 31.25 percent were students in
their second year, and 37.5 percent were students in their final year of undergraduate
study. Furthermore, 6.25 percent identified as being a postgraduate student.
4.2 MICROSOFT EXCEL PROFICIENCY
For participants from Set B who would be exposed to raw data, they were asked to rate
their Microsoft Excel skills from 1 to 5, with one being a novice and five being an expert.
No participant rated their skill level as a 1 indicating that they were all comfortable and
familiar with Microsoft Excel.
This initial rating gave some indication as to how the times generated for each question
may range.

18
Figure 2 - Self Rating of Excel
Participants 2, 3 and 4 have identified themselves as being highly competent in
Microsoft Excel indicating of the eight participants, they are likely to have the fastest
times, as a participant highly proficient in Microsoft Excel is more likely to be able to
manipulate the data in order to answer the questions and gain insight quicker.
4.3 STATISTICAL ANALYSIS
Before statistical analysis was conducted on the data collected from the study, it was
converted from its original time format into standard numeric (labelled as ‘general’ in
Microsoft Excel) in order to make the data easier to analyse.
The data was identified as being normally distributed and there were no significant
outliers identified. In addition in the dependent variable i.e. response time, was
continuous and the independent variable i.e. study participants were categorical. As all
conditions were met, One Sample T-Test could be confidently calculated.
4.3.1 ONE SAMPLE T-TEST
The One Sample T-Test was run with both participant groups in order to ensure
internal validity within the results and determine whether response times calculated
from the participants differentiated from normal – the mean. The test value was
selected by a random number generator, choosing a participant from one to eight. The
test was conducted on each question in the study, the results of which are displayed in
the following tables, where “t” is the observed t-value, “df” relates to degrees of freedom
and the statistical significance is “sig. (two-tailed)”.
1
2
3
4
5
1 2 3 4 5 6 7 8
Participant Set B

19
Table 1 - Participant Set A One Sample T-Test
OneSampleT-TestSetA
Test
Value t df
Sig. (two-
tailed) Mean SD
1a 0.0002417 2.4233 7 0.0459 0.000264 2.658E-05
1b 0.0002208 3.3453 7 0.0123 0.00018 3.438E-05
2a 0.000258 3.8441 7 0.0063 0.000341 6.103E-05
2b 0.0002382 2.9165 7 0.0225 0.000212 2.572E-05
3a 0.001022 5.9217 7 0.0006 0.000882 6.705E-05
4a 0.0003216 5.0054 7 0.0016 0.000214 6.063E-05
4b 0.000479 2.6828 7 0.0314 0.000591 0.0001179
5a 0.0003595 5.7307 7 0.0007 0.000312 2.368E-05
5b 0.0003904 3.8314 7 0.0064 0.000448 4.219E-05
6a 6.227E-05 2.9365 7 0.0218 0.000176 0.00011
7a 0.0009094 4.6931 7 0.0022 0.00081 5.999E-05
7b 0.0014634 5.2436 7 0.0012 0.001325 7.455E-05
8a 0.0008885 5.008 7 0.0016 0.000892 9.277E-05
Table 2 - Participant Set B One Sample T-Test
OneSampleT-TestSetB
Test Value t df
Sig. (two-
tailed) Mean SD
1a 0.00060764 3.1703 7 0.0157 0.00051 8.71E-05
1b 0.00028414 3.1245 7 0.0167 0.000614 0.000299
2a 0.00048273 3.8467 7 0.0063 0.000528 3.34E-05
2b 0.00025185 4.6355 7 0.0024 0.000298 2.84E-05
3a 0.00104294 4.3058 7 0.0035 0.001275 0.000153
4a 0.00055602 3.3792 7 0.0118 0.000511 3.79E-05
4b 0.00068558 3.9417 7 0.0056 0.000731 3.24E-05
5a 0.0001984 3.4225 7 0.0001 0.000167 2.62E-05
5b 0.00041273 2.7153 7 0.0300 0.000537 0.000129
6a 0.00017199 5.2917 7 0.0011 0.000101 3.8E-05
7a 0.00087326 2.6078 7 0.0350 0.000908 3.82E-05
7b 0.00153213 2.5839 7 0.0363 0.001329 0.000222
8a 0.00052465 4.5613 7 0.0026 0.001077 0.000342
Both tables show that for every question used in the study, the participant means are
statistically different as the significance is below 0.05.

20
Taking the values for question 5a for Set A, it can be said that the response time was
significantly higher than the population normal response time, t(7) = 5.7307, p = 0.007.
Similarly for Set B the response time was also significantly higher (though not as high
as Set A) than the population response time, t(7) = 3.4225, p = 0.0001.
This means that the null hypothesis that there is no relationship how information is
presented and how quickly it is understood can be rejected. In other words, for
question 5a for Participant Set A the probability for achieving the t-value if the null
hypothesis is correct is 0.7 percent. For Set B it is 0.01 percent.
However, based on the actual statistical significance numbers, it indicates that the type
of Infographic/Data Visualisation plays a part in how quickly information is
disseminated, and in some cases, it is actually better to have certain types of
information in its raw format, compared to looking at it in an Infographic/Data
Visualisation.
4.4 STUDY QUESTION 1 – WHICH FISH ARE OK TO EAT?
Figure 3 - Graph showing response time comparison between Participant Set A and Set B for
Question 1
The Infographic used for question 1 was a simple classification graphic/visual snapshot
highlighting based on current consumption and fish farming levels, which fish are
acceptable to eat and which should be avoided for fear of over-consumption/over-
farming. The raw data accompanying this Infographic was in list form.
00:00.0
00:17.3
00:34.6
00:51.8
01:09.1
1
2
3
4
5
6
7
8
Question 1a
Set A Set B
00:00.0
00:17.3
00:34.6
00:51.8
01:09.1
01:26.4
1
2
3
4
5
6
7
8
Question 1b
Set A Set B

21
Figure 4 - Infographic used for Question 1
Question 1a followed the belief that when it comes to conveying information,
Infographics and Data Visualisations are better compared to raw data and both Set A
and Set B show a similar distribution regarding response time. For question 1b, whilst
the hypothesis still holds true i.e. Set B has a slower response time than Set A,
Participants B2, B3, B4 and B5 have shown to be much quicker in their responses
compared to the other participants in their group. Relating this back to their self-
assessment of Excel, the more proficient users were better able to assimilate the
information, with response times similar to Participant Set A.

22
4.5 STUDY QUESTION 4 – UK POLITICAL PARTIES
Question 4
The Infographic used for question 4 was based upon a Venn diagram used to compare
and contrast the three main UK political parties and their suggested proposals to
reduce the country’s deficit. The accompanying raw data was in a listed table form.
00:00.0
00:17.3
00:34.6
00:51.8
1
2
3
4
5
6
7
8
Question 4a
Set A Set B
00:00.0
00:17.3
00:34.6
00:51.8
01:09.1
1
2
3
4
5
6
7
8
Question 4b
Set A Set B

23
Question 4a confirmed the hypothesis surrounding this project, as every response time
in Participant Set A was faster than Set B. Question 4b does also confirm this, but
graph does highlight that Participant A5 had the longest response time in both sets.
Question 4b involved calculations which brings to light the issue of using information
to gain insights is not only dependent on how the information is presented, but a factor
also includes the capabilities of the audience or user.
4.6 STUDY QUESTION 5 – THE TAX GAP
Question 5
The Data Visualisation used for question 5 was based on a bar chart which compared
the official UK tax gap provided by the government against what the tax gap
unofficially is, taking into account other potential tax incomes which the government
has not included in its calculations.
00:00.0
00:08.6
00:17.3
00:25.9
00:34.6
1
2
3
4
5
6
7
8
Question 5a
Set A Set B
00:00.0
00:17.3
00:34.6
00:51.8
01:09.1
1
2
3
4
5
6
7
8
Question 5b
Set A Set B

24
Figure 8 - Data Visualisation used for Question 5

25
Based on response times, this particular Data Visualisation does not confirm the
hypothesis as Set B response times are faster than Set A for question 5a. For 5b the
response times confirm the hypothesis however, participants B2, B3 and B4 have
response times that are significantly different from the rest of participant Set B, and are
even quicker than the majority of Set A response times. These graphs bring to light two
themes found within the data. The first being that the type of visualisation used to
represent information can significantly impact how well an individual can comprehend
the information presented to them. The second is the ability to comprehend raw data is
based on an individual’s data analysis competency.
4.7 STUDY QUESTION 6 – INTERNATIONAL NUMBER ONES
Question 6
The Infographic used for question 6 was based upon a map of the world which depicted
what every country was best i.e. number one for.
The Infographic used for this question shows a consideration that was not previously
highlighted with other Infographics, which is the extent to which prior knowledge is
required to quickly assimilate information.
00:00.0
00:08.6
00:17.3
00:25.9
00:34.6
1
2
3
4
5
6
7
8
Question 6a
Set A
Set B

26
The question accompanying this Infographic was to find what ‘Estonia was number one
for.’ The raw data was listed alphabetically so Participant Set B simply had to scroll
down until they found Estonia in the list. This proved much more difficult for certain
participants from Set A despite being exposed to the Infographic for ninety seconds
prior to seeing the question. Some participants immediately knew where they had to
look, whilst the participants who were not very adept with geography took significantly
longer to answer the question.

27
4.8 STUDY QUESTION 7 – POLL DANCING
Question 7
The Data Visualisation for question 7 was based upon a line graph used to compare
how accurate independent opinion polls are regarding the three main UK political
parties and whether they reflect the actual state of UK politics.
Figure 12 - Data Visualisation used for Question 7
There are very little discrepancies between Participant Set A and Set B, though for
question 7a, Set A on average is marginally better whilst for 7b both participant groups
perform exactly the same on average.
00:00.0
00:17.3
00:34.6
00:51.8
01:09.1
01:26.4
1
2
3
4
5
6
7
8
Question 7a
Set A Set B
00:00.0
00:43.2
01:26.4
02:09.6
02:52.8
1
2
3
4
5
6
7
8
Question 7b
Set A Set B

28
4.9 AVERAGE RESPONSE TIME FOR EACH QUESTION
Figure 13 - Graph showing comparison between average time to answer each question for
Participant Set A and Set B
By comparing the average response times for each question for each participant group
it confirms the points highlighted when looking at each question individually.
Overall the spread of the data is similar. For example question 1a was answered faster
than question 3a for both participant groups. But on average Set A was faster than Set B
for both these questions. This confirms that both participant groups are similar in
ability.
In different circumstances, Participants B2, B3 and B4 would have their very fast
response times classed as outliers; however, they have been included in the averages
because the data collected in the study sample is representative of the actual
population. There are students who are highly proficient in Microsoft Excel and who
are naturally better at manipulating data in order to gain insight, just as there are
students who are not as confident in their Excel skills.
Finally, the graph confirms the hypothesis that information is better and faster
understood when represented visually compared to when it is in its raw format.
1a
1b
2a
2b
3a
4a
4b5a
5b
6a
7a
7b
8a
Average Response Time
Set A
Set B

29
CHAPTER 5 – ANALYSIS
This chapter presents a discussion of the findings, primarily in relation to previous
publication. In addition, this chapter will discuss real-world application of Infographics
and Data Visualisations, relating the findings of the study to Information and
Knowledge Management.
The purpose of this study was to examine to what extent that the way information and
data is presented has an impact on the speed at which it is understood, as well as
examining how the information is understood. Furthermore, whilst not the intent of
the study, it did also reveal that certain types of Infographics and Data Visualisations
are more suitable and appropriate at conveying information clearly than other types.
5.1 RELATING FINDINGS TO PREVIOUS RESEARCH
Infographics and Data Visualisations are classified as one-way communication. Viewers
are exposed to the material, and more often than not, the effect is unknown. As a result
studies that directly measure the relationship between Infographics/Data Visualisations
on cognitive effect do not [yet] exist.
However, the findings from this research do show some convergence with the results
from other insights generated on the broad topic of Infographics and Data
Visualisations.
There is some correlation between the results in the study and various themes
identified by Mol in her 2011 thesis.35 Infographics/Data Visualisations use various
design methods to present information in a way that is visual and easy for the human
brain to interpret. In addition to the information being easier to interpret, specific
pieces of information can be easily identified due to the shapes, symbols, and colours
that facilitate the display of information. Logically this would mean that information
represented visually would be faster understood compared to information and data in
plain text and numbers.
35 Mol, L. (2011). The potential role for Infographics in science communication. Master’s thesis,
Biomedical Sciences, Vrije Universiteit, Amsterdam, Netherlands.

30
Participant Set A had important aspects of the data and information highlighted to
them in the Infographics/Data Visualisations. For Participant Set B, at first glance, the
raw data did not particularly highlight any important trends or facts to draw quick
insight from. Essentially Participant Set B was exposed to more information and
according to Hick’s Law,36 response time is increased when an individual is presented
with more responses to choose from. Applying this logic to the results and looking at
the graph showing the comparison for average response time for both participants
groups, the results comply with Hick’s Law and also brings in the aspect of Information
Overload especially for the questions where there were significant difference in the
response times for the two participant groups, notably question 1 and 4. The greater the
volume of information an individual is presented with, the more likely it can affect the
ability to make quick and timely decisions. With Infographics/Data Visualisations, they
can highlight the important and relevant information within the raw data, which in
turn leads to faster decision making, confirmed by the results of the study.
Mayer’s37 research into eye-tracking software in relation to learning with graphics
shows some coherence with the results of the study. Mayer’s research of four principles
related to design of graphics, the most relevant being that people learn better (or
perform better) from graphics when relevant features are highlighted rather than not
highlighted, which is in coordination with the results of the study.
It also highlights a path in which the development of the insight generated from this
project could go in. Tying in Brain-Computer Interfaces which is a method of
communication based on neural activity generated by the brain and is independent of
its normal output pathways of peripheral nerves and muscles, with an
Infographics/Data Visualisations study similar to this would provide a new channel of
output for the brain that requires voluntary adaptive control by the participants,38
36 Msu.edu, (2015). Hick’s Law. [online] Available at: https://www.msu.edu/~malogian/hickslaw.html
[Accessed 29 Apr. 2015].
37 Mayer, R. (2010). Unique contributions of eye-tracking research to the study of learning with graphics.
Learning and Instruction (20), 167-171.
38 He, B. (2005). Neural engineering. New York: Kluwer Academic/Plenum, p.85.

31
which would give further credibility to the discussion surrounding the preference of
Infographics/Data Visualisation over plain text and numbers.
The general consensus based on multiple visualisation studies is that Infographics and
Data Visualisations are preferable to plain text and numbers because they are capable
of making complex processes and large amounts of data and information
understandable to audiences that may not have a background in data analysis, thus
making the practice of Information and Knowledge Management more accessible.
5.2 REAL-WORLD APPLICATION
The assertion that the findings of this study are somewhat representative of not only
Loughborough University’s student population, but the general population as a whole,
has a number of important implications. When applied to real world scenarios being
able to confidently convey information to an audience can have a significant impact in
many areas within organisations (which can also include not-for-profit organisations
such as universities), including marketing and advertising, generating consumer insight,
providing high level overviews to managers and board members and story-telling when
trying to express an important message.
5.2.1 MARKETING, ADVERTISING AND CONSUMER INSIGHT
Using Infographics/Data Visualisations in marketing/advertisement allow both
marketers and advertisers to keep up with the ever changing landscape of digital
companies, strategies, and social media opportunities. Smiciklas39 recognises that due
to their ‘easy to consume’ nature, Infographics are identified as an effective marketing
communication tool, best served under the umbrella of content marketing. This paves
the way for back-and-forth conversation and engagement, allowing communities that
have shared interests to be built, ultimately creating relationships with brands and the
organisation by providing the information that an audience needs with an Infographic.
Furthermore, with the increased use of social media and Web 2.0 domains, brands
whose marketing and advertising which embraces this rapidly increasing trend
39 Smiciklas, M. (2012). The power of Infographics. Indianapolis, Ind.: Que Pub., p.139.

32
amongst consumers are the ones most likely to reap the rewards of increased awareness.
Embedding Infographics and Data Visualisations into Social Media further enhances
the benefits realised with such a strategy. When used effectively, it can increase website
traffic by at least 12%,40 which can be tracked using analytics.
Every time an Infographic is clicked, viewed, shared, as well as other relevant
information like how long an online user viewed a particular Infographic/Data
Visualisation can be tracked and measured using analytics. This can present an
organisation with greater insight and a deeper understanding of the information
regarding what makes their targeted customers behave and think in a certain way. As a
result an organisation can adjust their marketing and advertisement campaigns
accordingly, by creating even more interesting and relevant Infographics/Data
Visualisations, whilst simultaneously increasing followers and subscribers of
communities surrounding the brand and company by engagement. This can be likened
to an organisation, taking the information gained from these insights and transforming
it into knowledge which would guide these marketing and advertising campaigns.
5.2.2 STORYTELLING
“The aim of the poet is to inform or delight, or to combine together, in what he says, both pleasure and
applicability to life.
In instructing, be brief in what you say in order that your readers may grasp it quickly and retain it
faithfully.
Superfluous words simply spill out when the mind is already full.”
- Horace (19 BCE)41
In order to build and engage audiences, a number of organisations are following the
practices of publishers and are finding success by presenting content with the aim of
entertaining and informing readers. Infographics and Data Visualisations combine
aspects that utilise attractive visuals that not only appeal to an audience that is eager
for information, but also supports in the retention and comprehension of that material,
40 Advertising and Marketing Blog | Marketing News and Trends, (2015). It’s All About the Images
[Infographic] - Advertising and Marketing Blog | Marketing News and Trends. [online] Available at:
http://www.mdgadvertising.com/blog/its-all-about-the-images-infographic/ [Accessed 11 Apr. 2015].
41 Horace. (1843). Epistola ad Pisones De Ars Poetica. Rivington, London.

33
which is crucial in this age as it becomes more challenging to catch and hold onto the
attention of viewers due to the ever increasing content being produced and dispersed
on the web.
Al Gore is one individual who has used Infographics/Data Visualisations to
communicate information through visual storytelling.42 The documentary film, ‘An
Inconvenient Truth’ is based upon a climate change lecture that he has delivered over
one thousand times since 1989;43 however there are a number of differences between
the content depicted in the first deliveries and in the documentary. Most notably, the
use of compelling data alongside interactive Infographics/Data Visualisations to convey
that humans are incrementally, but rapidly damaging planet-scale forces. By combining
information visualisations with rhetorical figures; the most notable example of a before
and after graphic of a glacier seventy years apart, resulted in juxtaposition and invoked
emotion, whilst creating engagement and involvement with the information and
content by stimulating the audience to imagine what the third image would be.
5.3 GOOD AND BAD INFOGRAPHICS/DATA VISUALISATIONS
It is evident from the results of the study that the type of Infographic/Data
Visualisation used impacted response times, which leads to the thought that there is a
right way and there is a wrong way regarding the use of Infographics/Data
Visualisations to convey information. One can therefore make the assumption that the
Infographics/Data Visualisations used in this study that struggled to clearly and
accurately depict information to the participants had longer response times compared
to the same information in its raw format.
This begs the question, what makes a good Infographic/Data Visualisation? Quite
simply, it makes sense. In the study, after exposure for ninety seconds, the good
Infographics/Data Visualisations that were clearly either explorative or narrative left
the participants in the words of Horace, informed and delighted. Information that is
42 An Inconvenient Truth. (2006). [DVD] Los Angeles: Davis Guggenheim.
43 Spiritualityandpractice.com, (2015). Spirituality& Practice: Film Review: An Inconvenient Truth, directed
by Davis Guggenheim. [online] Available at:
http://www.spiritualityandpractice.com/films/films.php?id=15647 [Accessed 12 Mar. 2015].

34
represented visually in a good way facilitates the DIKW process, allowing users to
combine their own knowledge about a topic with the information presented to them
via an Infographic or Data Visualisation to further enhance their knowledge.
If an Infographic/Data Visualisation leaves the audience with a lot of unanswered
questions, or they have to spend a long time analysing the content in order to make
sense of it, the chances are that it is likely to be depicted as poor in quality by the
audience and the intended message will not be conveyed well, if conveyed at all.
Existing literature surrounding both Infographics and Data Visualisations relate
primarily to its formation and has highlighted that there is clearly a right and a wrong
way to visually represent information and data.

35
CHAPTER 6 – CASE STUDY DISCUSSION
This chapter critically evaluates the use of Infographics and Data Visualisations in two
organisations, and from two different viewpoints - the first company being a creator of
insight software, technology and techniques, and the second company being a user of
Infographics and Data Visualisations.
The first company is IBM who have identified and marketed themselves as an
international company heavily invested within big data and analytics. As the world’s
largest technology company, it can be expected that the multitude of technologies and
capabilities available to IBM influences and is a major asset when using Infographics
and Data Visualisations to gain competitive advantage. The second company is
Unilever, one of the world’s top four FMCG (fast moving consumer good) companies.44
This particular case is of interest because the benefits of using Infographics and Data
Visualisations and incorporating them into the business cannot be seen as easily as
they can in a firm like IBM. Unilever’s approach can be considered to be much more
strategic and aligned in order to maximise potential benefit.
6.1 IBM
The 2013 Annual Report published by IBM45 gave investors insight into the vision the
board of directors saw for the company’s future. Data was a significant topic for the
report – it made up 1/3 of IBM’s strategy. IBM acknowledged that data was at the
forefront of every decision made, regardless of whether it was about technology, people
or business. IBM also acknowledged the rapidly increasing velocity, variety and volume
of data and concluded that data in the 21st century promises to be what steam was for
the 18th century and electricity for the 19th.
IBM believes that data is the new basis for competitive advantage and the top leaders
will drive business outcomes via analytics, capture the time value of data developing
44 Leading 25 FMCG companies worldwide in 2013, b. (2015). Leading FMCG companies worldwide based
on sales, 2013 | Statistic. [online] Statista. Available at:
http://www.statista.com/statistics/260963/leading-fmcg-companies-worldwide-based-on-sales/
[Accessed 12 Feb. 2015].
45 2013 IBM Annual Report, (2014). IBM Annual Report 2013. [online] Available at:
http://www.ibm.com/annualreport/2013/ [Accessed 15 May 2014].

36
speeds of insights and speeds of action and change the environment of the industry
with cognitive capability. Essentially, the best leaders will not just collect large amounts
of data. Data on its own serves no real purpose or benefit to an organisation as there is
no meaning or use for it. IBM’s belief is that the real market leaders will apply
methodologies, processes and techniques to Big Data in order to turn it to meaningful
information which will lead to detailed insights. These insights should then be used to
construct strategies that align to current business process and successful execution of
data and information focused strategies will lead to competitive advantage on the basis
that a company possesses Intellectual Capital, which can only be sustained if a
company is committed to Big Data and its analysis and transformation to information
in a cyclical fashion. This continued and repeated cycle ties Knowledge Management
into the process; each cycle will bring to light key learnings which should be taken on
board in order to have an impact on future strategy creation.
As mentioned at the start of this chapter, IBM as a technology company takes a more
data science approach when it comes to realising the benefit of Infographics and Data
Visualisations within organisations.
65% of all people are visual learners46 which is not to say that 65% of all people
disregard other types of learning (i.e. auditory and kinaesthetic) however for purposes
of information recall, it is significantly enhanced when tied to visual imagery at speeds
of 100 MB/s. Compared to computers, the human mind is weak at performing
calculations but much stronger at recognising patterns. As data sets get larger and
more complicated only the very skilled are able to derive meaning. Visualising data
presents an opportunity to break down these barriers and translate data into
judgement.
Visualisations show interrelationships and trends that may not have been previously
picked up on. Because of this, Data Visualisation is a powerful tool for making complex
environments easier to understand.
46 McCue, T. (2013). Why Infographics Rule. [online] Forbes. Available at:
http://www.forbes.com/sites/tjmccue/2013/01/08/what-is-an-infographic-and-ways-to-make-it-go-viral/
[Accessed 2 Mar. 2015].

37
IBM recognises that visual information can help gain insight from the myriad of data
that their company generates.47 The ability to understand is increased from the
underlying numbers in data and has designed technology which simplifies the
visualisation creation process - by simplifying the data and information.
IBM’s Watson is at the forefront of a new era of cognitive computing which is in a
different realm to the programmable systems that preceded it, despite these being
radically different to the tabulating machines that existed a century ago. Conventional
computing solutions are based on rules and logic and are designed to derive
mathematically precise answers, following a rigid decision tree approach. But in today’s
world for big data and the need for more complex, evidence-based decisions, these
rigid approaches can often break or fail to keep up with today’s relevant information.
Watson bears some of the key cognitive elements of human expertise, namely the four
step process our brains go through in order to make a decision: 1) Observation 2)
Interpretation 3) Evaluation 4) Decision.
IBM benefits greatly from the fact that as a creator of software and products relating to
analytics, Infographics and Data Visualisations, they are able to use their own products
to generate insights for their own company.
Insights generated by Data Visualisations and Infographics have reduced risk and the
likelihood of risk, improved the detection of fraud and improved the efficiency of data
security and privacy by monitoring cyber security in real time. It has modernised data
warehousing with new technology which includes in-memory computing, social data
and telematics while building confidence in existing data and reducing the cost to store
and process it. By incorporating additional internal and external information sources,
customer views are extended, allowing IBM to attain a 360-degree view of their
customers meaning that customer attrition has declined. Which in turn, has led to a
reduction in the cost of marketing campaigns as they know their customers better as
their campaigns are targeting appropriately at customer needs.
47 Ibm.com, (2015). IBM Advanced visualization. [online] Available at: http://www-
01.ibm.com/software/analytics/many-eyes/index.html [Accessed 7 Feb. 2015].

38
Quantifying both Information Management and Intellectual Capital has been discussed
widely in literature, with many researchers noting the difficulty in quantifying or
valuing information. However Forrester have managed to conclude that IBM’s
Information Management solutions have impacted the company positively from a
finance perspective resulting in 148% return on investment and a total benefit amount
of $31.2m.48
6.2 UNILEVER
In 2010, the company set itself the target of doubling its revenues in a decade or less –
without doubling its costs. 49 According to the Vice President for Business Intelligence
at Unilever, Information Management was the critical success factor in achieving this
strategy. In order to support its employees to make better decisions, Unilever decided
to incorporate data into the majority of its business processes and make effective use of
analytics and in particular, Data Visualisations.
In order to maintain global leader status in the consumer goods industry, Unilever, like
its competitors needs to continuously keep an eye on market trends, respond rapidly to
changing consumer trends, whilst searching for new opportunities to improve the lives
of its consumers. The ability to analyse the colossal amounts of data that Unilever is
privy to is critical to successful running of Unilever in present and being reactive to
changes in the marketplace.50
The human brain finds it challenging to comprehend plain numbers and text, but when
these same numbers are visualised, it brings the story to life. To solve their data
questions, an increasing number of businesses are as a result taking the Data
48Ibm.com, (2015). IBM Management Solutions. [online] Available at:
http://www.ibmbigdatahub.com/sites/default/files/infographic_file/TEI%20Infographic%20IBM%20Info
rmation%20management%20solutions_Final2.pdf [Accessed 19 Feb. 2015].
49 FusionBrew - The FusionCharts Blog, (2014). How Data Visualization and Effective Information
Management help Unilever employees make better decisions? - FusionBrew - The FusionCharts Blog.
[online] Available at: http://blog.fusioncharts.com/2014/08/how-data-visualization-and-effective-
information-management-help-unilever-employees-make-better-decisions/ [Accessed 28 Feb. 2015].
50 Fusion Charts, (2014). Towards Effective Decision-Making Through Data Visualization: Six World-Class
Enterprises Show The Way. [online] Available at:
http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-
Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf [Accessed 28 Feb. 2015].

39
Visualisation route in order to gain actionable insights from their data. Also due to the
developments in technology, the interactivity of visualisations has progressed
enormously. The major benefit is that users are no longer required to be experts in data
analysis in order to gain insights from them. Their visualisations do the job of helping
them identify patterns, trends, gaps and outliers in the data.
Earlier complex mathematical modelling processes at Unilever were designed to
process data and find relevant patterns in them. With Data Visualisation, all employees
within the company could become more analytically minded without needing the
expert statistical skills.
Data Visualisation has enabled global managers at Unilever to delve into the level of
detail they require for effective decision-making. Managers are using Data
Visualisations to group products or competitors as they see fit, comprehend the
momentum in the business in terms of its smaller integral parts and get a better
understanding into both consumer behaviour and buying trends. Incorporating Data
Visualisations into business practices has bridged the gap between the global
perspective and the local perspective by helping to connect the local and small
consumer-based trends to a global sort of speed and direction-defining trend for the
business.
Data Visualisation has helped Unilever employees comprehend vast amounts of
information that would have been otherwise unmanageable if it were in an Excel sheet.
According to a ComputerWeekly study,51 the project was launched in the third quarter
of 2011 and has gone live in 45 operating units, with 6,000 commonly used reports.
Eighty-five per cent of business users now say they have improved access to reporting
and information. Meanwhile, the analytics side of their objectives is also seeing success.
One system which collects retail point of sale data, has lowered costs whilst also
improving business performance. Their customer teams’ feedback the insight they have
got through these tools they have been able to increase revenue with retailers.
51 ComputerWeekly.com, (2015). Unilever enterprise data warehouse locked to business programmes.
[online] Available at: http://www.computerweekly.com/feature/Unilever-enterprise-data-warehouse-
locked-to-business-programmes [Accessed 5 Mar. 2015].

40
The VP was interviewed in the study as saying, “Striving for better information is a
journey in itself rather than a specific destination. Needs will continue to evolve, but at
the same time, technology will allow us to continue to develop better ways to process
data and drive insights; this continuous evolution and development has the potential to
enhance our relationships with consumers and customers all over the world.
The frontiers of insight and simplification are expanding all the time. So what we look
for is measurable success for each phase of the journey — markers that can help us
quickly understand what works and what doesn’t and which allow us to react quickly.
And of course, we also place a strong emphasis on feedback from our users — because
in the end, creating solutions for them is at the heart of what we do.” 52
By removing the delay of manually collecting and amassing data, Unilever’s Data
Visualisation and analytics systems have seen improved efficiency and collaboration,
streamlined work processes; they have reduced the decision-making cycle time and
also enabled Unilever to focus on product innovation for the consumer. Unilever has
recognised that data exists both inside and outside product and geographic silos and
they have made this data coherent and manageable. By doing this, it has given Unilever
the power to radically change perceptions and bring to light opportunities that could
otherwise be missed.
6.3 SUMMARY
Both IBM and Unilever are aware and have identified various degrees of complexity and
difficulty in their respective business environments regarding data and Information and
Knowledge Management.
Today, the challenges of economic environment require businesses to take an
integrative approach to their problem solving, whilst at a more comprehensive level,
take a more holistic approach in order to gain insights and generate knowledge.
Without integration, understanding, communication and action, it is quite often the
52 Blogs.forrester.com, (2013). Kyle McNabb's Blog. [online] Available at:
http://blogs.forrester.com/kyle_mcnabb/13-06-05-
qa_with_greg_swimer_vp_it_business_intelligence_unilever [Accessed 12 Mar. 2015].

41
case that businesses simply cannot generate the relevant information and knowledge
they require in order to truly serve the needs of their customers.
Data Visualisations and Infographics allow organisations to take a different approach to
see their business from another perspective as long as they are conceived as a
transformational process within the DIKW cycle and not just seen as an end product.

42
CHAPTER 7 – CONCLUSION
Infographics and Data Visualisations make use of both words and visuals, and strike the
ideal balance of where linguistic and non-linguistic systems converge.
Infographics and Data Visualisation have been around for a while but have only
recently started to come to the attention of data specialists as an alternate way of
displaying information. The increased popularity of Infographics and Data
Visualisation could be attributed to the rapid increase of social media and electronic
communication which have revolutionised the way businesses communicate.
The findings from the analysis and the case study discussions have produced
encouraging results, proposing to varying extents, Infographics and Data Visualisations
can indeed contribute to effective Information and Knowledge Management, when
used as an aid to existing processes and strategies within an organisation. Moreover,
there is confirmation from the findings that data and information that is represented
visually is better or faster understood, compared to the same information in its raw
format, thus attaining the first objective detailed in chapter one.
However, perhaps the most fascinating development from this assertion are the
implications that suggest such research of this nature has an application in the ‘real-
world’. As both chapter five and chapter six discusses, there are a number of ways in
which such research can play an important role within organisations and society in
general, by outlining various areas where there are opportunities in developing reliable
tools to effectively utilise the insight generated by Infographics/Data Visualisations
demonstrated by the case study discussion, consequently achieving objective three.
This project has shown that when dealing with large data sets, the best way to explore
and understand it is through Data Visualisation. When there is a need to condense
copious amounts of information and data into a digestible format, Infographics are an
ideal solution. This also highlights the need for designers to be familiar with their data
when creating visual information. Designers should obtain sufficient information or
knowledge about a data set they wish to visualise, which in turn will lead to obtaining

43
the most appropriate illustration about the data, in order to assist others in the
information and knowledge acquisition process53.
From the analysis, the conclusion is that Infographics and Data Visualisations best
serve the communicating stage of the Information and Knowledge Management cycle,
whilst Data Visualisations can further be used in the analysing stage, which realises
objective four.
7.1 LIMITATIONS
Whilst the results and findings of this project were promising, there were some aspects
of the comparative study within the project that could have been improved upon.
Firstly, the sample size could have been improved by increasing the number of
participants and also broadening the demographics to accurately reflect
Loughborough’s student population in terms of age, and not just year group.
One of the main hurdles with phase one of the research was interpreting the
participants’ understanding of the data and information presented to them. There was
the likelihood that two participants could get two different answers depending on
which participant group they were in, which was expected due to the nature of
Infographics/Data Visualisations being used to explain particular points or aspects
within data and information, so naturally the raw data sets would contain a lot more
data and information. However, if participants within the same group got different
answers from what was identified as the correct answer for a particular question, there
was the issue of aggregating results so that no participant was depicted as better or
worse.
The limitations surrounding Infographics and Data Visualisations pertain primarily to
their design. A well-designed Infographic/Data Visualisation will not always amount to
clear communication. There is an additional requirement to understand the
53 Chen, M., Ebert, D., Hagen, H., Laramee, R., van Liere, R., Ma, K., Ribarsky, W., Scheuermann, G. and
Silver, D. (2009). Data, Information, and Knowledge in Visualization. IEEE Computer Graphics and
Applications, 29(1), pp.12-19.

44
information needs of the intended audience so that they will appreciate what is being
communicated to them as discussed in chapter five.
7.2 INFOGRAPHIC/DATA VISUALISATION FRAMEWORK
Many creators of Infographics/Data Visualisations have left it to chance when it comes
to ensuring whether their material can truly be understood.
The analysis of the results in the study has shown that there are good and bad
Infographics/Data Visualisations and as such there are good and bad practices when it
comes to designing Infographics/Data Visualisations.
When Infographics/Data Visualisations are done incorrectly and are not finished to a
professional standard they can create a sense of distrust in the media and the
information source. It can also form opinions based on incorrect information which
leads to a warped sense of knowledge and bias is brought into decision making aspects.
Infographics/Data Visualisations should be used for conveying information in an
aesthetically and visually pleasing manner; however, they are a medium in which
information can be very easily distorted and misrepresented.
Many data visualists and graphic designers are designing based on what others are
doing rather than whether the context of the information fits what these designers are
trying to make, which makes the end products repetitive and unfit for purpose as the
contexts are not properly being thought through.
There is not an explicitly correct way for designing Infographics/Data Visualisations,
however, there are common aspects that are evident in visual information that is
depicted as good or fit for purpose.
The fundamentals of Infographics/Data Visualisations are based upon a message that
the designer wants to communicate to an audience. Encompassing the message is the
familiarity of the audience that is to be targeted with the information visual. Once the
designer has confidently identified both the message to be sent and the audience that
will be targeted with the information, a six step process ensues regarding the

45
amalgamation of data and information and the creation of the Infographic/Data
Visualisation.
This process involves data and information gathering, identifying patterns,
relationships and differences within the data which will allow the designer to
conceptualise the best way to present the data and information visually. After
refinements of a first draft and subsequent drafts, a designer should have an
Infographic/Data Visualisation that will convey their message appropriately. Naturally,
over time information and data becomes dated and the message contained in an
Infographic/Data Visualisation is no longer accurate. Therefore the process of
designing is not linear but cyclical as new data and information becomes available
allowing for updates to be made to information that is represented visually, as depicted
by the following framework; meeting the final objective outlined in chapter one.
Figure 14 - Framework for Infographic/Data Visualisation Creation
Finally encompassing the main design process is the need for an Infographic/Data
Visualisation to inform an audience as well as engage them. A designer that keeps the
need to create engaging and informing Infographics/Data Visualisations in mind
throughout the design process can usually be confident of creating material that is fit
for purpose.

46
When Infographics and Data Visualisations are used as a medium instead of ground-
breaking tools that will completely revolutionise the way data and information is seen,
they become much more flexible, as they are then able to be applied to many more
areas. They also become more exciting. Ultimately, at the core of both Infographics and
Data Visualisations is the data. Representing it visually allows insight that may not be
found in a table. There are stories within the data, Infographics and Data Visualisations
can help tell them.
Word Count: 10, 962

47
BIBLIOGRAPHY
2013 IBM Annual Report, (2014). IBM Annual Report 2013. [online] Available at:
http://www.ibm.com/annualreport/2013/ [Accessed 15 May 2014].
Advertising and Marketing Blog | Marketing News and Trends, (2015). It’s All About the
Images [Infographic] - Advertising and Marketing Blog | Marketing News and Trends.
[online] Available at: http://www.mdgadvertising.com/blog/its-all-about-the-images-
infographic/ [Accessed 11 Apr. 2015].
An Inconvenient Truth. (2006). [DVD] Los Angeles: Davis Guggenheim.
Anand, V., Manz, C. and Glick, W. (1998). AN ORGANIZATIONAL MEMORY
APPROACH TO INFORMATION MANAGEMENT. Academy of Management Review,
23(4), pp.796-809.
Awad, E. and Ghaziri, H. (2004). Knowledge management. Upper Saddle River, N.J.:
Prentice Hall, pp.3, 36-37.
Baets, W. (1992). Aligning information systems with business strategy. The Journal of
Strategic Information Systems, 1(4), pp.205-213.
Bain.com, (2015). Decision Effectiveness / Decision Making - Bain & Company. [online]
Available at: http://www.bain.com/consulting-services/organization/decision-
effectiveness.aspx [Accessed 12 Feb. 2015].
Bertels, T. and Savage, C. (1998). Understanding Knowledge in Organizations. London:
Sage.
Blogs.forrester.com, (2013). Kyle McNabb's Blog. [online] Available at:
http://blogs.forrester.com/kyle_mcnabb/13-06-05-
qa_with_greg_swimer_vp_it_business_intelligence_unilever [Accessed 12 Mar. 2015].
Bogan, C. and English, M. (1994). Benchmarking for best practices. New York: McGraw-
Hill.

48
Bryman, A. and Bell, E. (2003). Business research methods. Oxford: Oxford University
Press.
Burns, R. and Burns, R. (2008). Business research methods and statistics using SPSS. Los
Angeles: SAGE, p.257.
Capgemini, (2015). Business Information Management and IBM | Article. [online]
Available at: http://www.uk.capgemini.com/business-information-
management/business-information-management-and-ibm [Accessed 8 Mar. 2015].
Chen, M., Ebert, D., Hagen, H., Laramee, R., van Liere, R., Ma, K., Ribarsky, W.,
Scheuermann, G. and Silver, D. (2009). Data, Information, and Knowledge in
Visualization. IEEE Computer Graphics and Applications, 29(1), pp.12-19.
Choo, C. (2002). Information management for the intelligent organization. Medford, NJ:
Information Today.
Community.watsonanalytics.com, (2015). IBM Watson Analytics Community. [online]
Available at: https://community.watsonanalytics.com/expert-blog/ [Accessed 8 Mar.
2015].
ComputerWeekly.com, (2015). Unilever enterprise data warehouse locked to business
programmes. [online] Availableat: http://www.computerweekly.com/feature/Unilever-
enterprise-data-warehouse-locked-to-business-programmes [Accessed 5 Mar. 2015].
Cornford, T. and Smithson, S. (2006). Project research in information systems.
Basingstoke: Macmillan, p.67.
Cramer, J., Khalil, F. and Rochet, J. (1998). Contracts and Productive Information
Gathering. Games and Economic Behavior, 25(2), pp.174-193.
Desouza, K. and Paquette, S. (2011). Knowledge management. New York: Neal-Schuman
Publishers.

49
Elearnportal.com, (2015). Experimental Design. [online] Availableat:
http://www.elearnportal.com/courses/sociology/research-procedures-ii/research-
procedures-II-experimental-design [Accessed 12 Feb. 2015].
Franklin, M., Halevy, A. and Maier, D. (2005). From databases to dataspaces. ACM
SIGMOD Record, 34(4), pp.27-33.
Fusion Charts, (2014). Towards Effective Decision-Making Through Data Visualization:
Six World-Class Enterprises Show The Way. [online] Availableat:
http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-
Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf
[Accessed 28 Feb. 2015].
FusionBrew - The FusionCharts Blog, (2014). How Data Visualization and Effective
Information Management help Unilever employees make better decisions? - FusionBrew -
The FusionCharts Blog. [online] Availableat:
http://blog.fusioncharts.com/2014/08/how-data-visualization-and-effective-
information-management-help-unilever-employees-make-better-decisions/ [Accessed
28 Feb. 2015].
Gold, A., Malhotra, A. and Segars, A. (2001). Knowledge Management: An
Organizational Capabilities Perspective. Journal of Management Information Systems,
18(1), pp.185-214.
Haight, J. (2014). Data Visualization, Pattern Recognition, and How Us Humans Actually
Make Decisions. [online] LinkedIn Pulse. Available at:
https://www.linkedin.com/pulse/20141018191340-58600475-data-visualization-pattern-
recognition-and-how-us-humans-actually-make-decisions [Accessed 8 Mar. 2015].
He, B. (2005). Neural engineering. New York: Kluwer Academic/Plenum, p.85.
Heimbigner, D. and McLeod, D. (1985). A federated architecture for information
management. TOIS, 3(3), pp.253-278.

50
Hinton, M. (2006). Introducing information management. Oxford: Elsevier Butterworth-
Heinemann, p.2.
Horace. (1843). Epistola ad Pisones De Ars Poetica. Rivington, London.
Hough, J. and White, M. (2004). Scanning actions and environmental dynamism.
Management Decision, 42(6), pp.781-793.
Ibm.com, (2015). IBM Advanced visualization. [online] Available at: http://www-
01.ibm.com/software/analytics/many-eyes/index.html [Accessed 7 Feb. 2015].
Ibm.com, (2015). IBM Management Solutions. [online] Available at:
http://www.ibmbigdatahub.com/sites/default/files/infographic_file/TEI%20Infographi
c%20IBM%20Information%20management%20solutions_Final2.pdf [Accessed 19 Feb.
2015].
IBM, (2015). IBM PureFlex System and IBM Flex System resources: Infographics. [online]
Available at: http://www-03.ibm.com/systems/pureflex/Infographics/data-everywhere-
003.html [Accessed 8 Mar. 2015].
IBM, (2015). IBM System x Infographics. [online] Available at: http://www-
03.ibm.com/systems/x/resources/Infographics/ [Accessed 8 Mar. 2015].
Ibm.com, (2014). IBM Watson: What is Watson?. [online] Available at:
http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html [Accessed
8 Sep. 2014].
IBM, (2015). Infographics & Animations | The Big Data Hub. [online] Available at:
http://www.ibmbigdatahub.com/Infographics [Accessed 8 Mar. 2015].
Ifad.org, (2015). Managing for Impact in Rural Development - A Guide for Project M&E -
Table of Contents. [online] Availableat:
http://www.ifad.org/evaluation/guide/index.htm [Accessed 7 Mar. 2015].

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