Interactive Visualization and Big Data

A Management Perspective

Thomas Plank

and Markus Helfert

Controlling, Finance and Accounting Department, University of Applied Sciences Upper Austria,

Wehrgrabengasse 1-3, Steyr, Austria

School of Computing, Dublin City University, Glasnevin, Dublin, Ireland

Keywords: Interactive Visualization, Effective Decision Making, Big Data, Human-Information-Interaction.

Abstract: This position paper presents a systematic literature review that aims to identify research topics and future

research possibilities in the area of interactive visualizations of big data in a management perspective.

Therefore, the authors reviewed journals listed in the Index of Information Systems Journals and the

Computing Research and Education Association derived from the databases “EBSCO Business Source

Premier”, “Sage Premier” and “Science Direct” from 2005 to 2015. The authors reviewed 993 abstracts and

identified 122 peer-reviewed publications as relevant to the topic. Based on this interdisciplinary collection

of research papers, the authors will identify the key research topics and derive future research possibilities

that need to be undertaken.

1 INTRODUCTION

In recent years the amount of available data is

rapidly increasing and due to technological

improvements it will further expand dramatically in

the next years. This vast amounts of data

(summarised as “Big Data”) are the result of

innovation in the era of the so called “Internet of

Things” and investments to digitalize the value chain

of a company to gain deeper insights. Big data will,

according to Philip Chen and Zhang (2014) and

others, revolutionize many fields, including

business, public administration and also scientific

research.

There are various definitions of big data centre

upon the 3Vs and are lately extended to 4Vs. Doug

Laney uses volume, velocity and variety (Laney,

2001) as the 3Vs and Zikopoulos and Eaton (2011)

added the fourth V that can be interpreted as value,

variability or virtual. Philip Chen and Zhang (2014)

more commonly describe big data as a collection of

very huge data sets with a great diversity of types so

that there occur difficulties in processing these vast

amounts of data with state-of-the-art approaches.

This development sets the business intelligence and

analytics field for huge challenges. Nevertheless,

Chen et al. (2012) describe the importance of

business intelligence and analytics and the related

field of big data analytics for both researchers and

practitioners, to solve data-related problems in

contemporary business organizations.

The number of dedicated venues, initiatives and

publications regarding this topic reveal a continuous

and growing trend. Beside the academic interest,

practitioners in government, industry and business

have found that there arise enormous opportunities

through big data (Arave et al., 2014). Philip Chen

and Zhang (2014) and Schoenherr and Speier-Pero

(2015) also claim that big data is highly valuable to

increase productivity in business and produce

evolutionary breakthroughs in scientific disciplines.

There is a consensus among these scholars that the

future competitions in business will surely converge

into the big data explorations. However, they

conclude that with big data arise many challenges.

Other scholars support this point of view and claim

that the nascent issues reach from data capture, data

storage over data analysis to data visualization.

(Philip Chen and Zhang, 2014; Chen et al., 2012;

Arave et al., 2014; Lemieux et al., 2014)

Arave et al. (2014) and Howe et al. (2008) state

that the data, that is produced at great expense and

effort, are only as useful as the possibilities to

analyse and interpret them. Therefore, the cognition-

based perspective is an important aspect of

information visualization and impacts the quality of

Plank, T. and Helfert, M.

Interactive Visualization and Big Data - A Management Perspective.

In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 2, pages 42-47

ISBN: 978-989-758-186-1

decision- and sense-making based on this data (Al-

Kassab et al., 2014). The the human information

processing system cannot process these amounts of

information due to the limitation of the working

memory (Lemieux et al., 2014).

1.1 Data Visualization and Big Data

Card et al. state that “the goal of visualization is

insight and not pictures” (Card et al., 1999). Thus, to

provide a comprehensive insight into the enormous

amount and variety of data, the information

visualization discipline has to adjust to the changing

requirements and framework conditions that arise by

the vast growth and diversity of data in the last

decade.

Especially for big data applications, it is difficult

to conduct visualization arising from the large size

and variety of data. Philip Chen and Zhang (2014)

argue that current big data visualizations (Heer et al.,

2008; Keim et al., 2004) mostly have poor

performance and that the techniques to capture,

curate, analyse and visualize the data are far away

from meeting the variety of needs. They state that it

is necessary to rethink the way we visualize big data.

Cuzzocrea et al. (2011) support this argument by

claiming that actual analytics still does not go

beyond classical components like diagrams, plots

and dashboards, but complex business-intelligence

processes demand for more advanced tools. In their

opinion, visualization issues represent a leading

problem in current research. Card et al. (1999)

describe “visualization” as “the use of computer-

supported, interactive visual representation of data to

amplify cognition.” This definition outlines the

intersection of the research field information

visualization with the field of human-information-

interaction.

An essential influencing factor on the usage of

information is the purpose and the information-

seeking-behaviour. For example, Parson and Sedig

(2013) analyse the influencing properties of

interactive visualizations that tend to have an impact

on the cognitive processes and on visual reasoning.

They argue that, performance of complex cognitive

activities involves active and goal-directed

information processing by human beings (Funke,

2010). This information processing consists of using

and interacting with given information to derive

insight (Knauff and Wolf, 2010). That implies that

humans interact with information to support their

thinking processes that are used for solving

problems and making decisions. As a consequence,

Parson and Sedig (2013) characterize “humans” in

their research as actors, because they focus on the

activity aspect of human-information-interaction.

Following this, they argue that static visualizations

tend to force the actor to exert a great deal of mental

effort in order to reason and think about the

information. The mental processes (e.g. abstractions,

comparison) take place over a span of time and

involve constant assimilation and reorganization of

information, which is not sufficient assisted by static

visualizations. Interaction, instead, can potentially

bridge this issue by encouraging the actor to grapple

with the provided information. (Parson and Sedig,

2013) Authors such as (Elmqvist et al., 2011; Green

and Fisher, 2010; Meyer et al., 2007; Sedig and

Parsons, 2013; Spink and Cole, 2006) state that the

process of stimulation and enabling reasoning with

the aid of interactive visualizations is still a highly

unexplored field. They also argue that current

research barely scratched the surface of this new line

of research and much work remains to be done.

Another relevant aspect in the concert of human-

information-interaction and visualization are the

different display opportunities such as smartphones,

tablets and smartwatches. Burford and Park (2014)

hypothesise that mobile computing devices are a

significant access point for information seeking

activities and current theories and models do not

consider the role of the individual devices in digital

information interactions. Alongside with the

interaction, the representation of information

depends on the individual purpose. The purpose is,

on a high level, separated into explanatory and

exploratory issues. Exploratory data analysis has a

high intersection with human-information-

interaction and data mining. Current visual data

mining techniques are of high value in exploratory

data analysis and are especially useful when little is

known about the data and the exploration goals are

vague (Keim, 2002). However, information

visualization tools and data mining algorithms

originate from two separate lines of research. On the

one hand, data mining researcher claim that

statistical algorithms and machine learning is the key

to find the interesting patterns. On the other hand,

information visualization researchers believe in the

importance of giving users an overview and insight

into the data distributions (Shneiderman, 2002).

Shneiderman (2002) argues that, the combination of

the advantages of data mining and information

visualization will lead to new insights and a more

efficient processing with high amounts of data.

The interaction with information can, for

explanatory purposes, be paraphrased as a story that

is told through data. Narrative visualizations

Interactive Visualization and Big Data - A Management Perspective

especially focus on this area of research. Segel and

Heer (2010) describe narrative visualization as the

melting pot of computer science, statistics, artistic

design and storytelling. Diagrams and charts

embedded in a larger body of text where used to tell

a story with static visualizations. However, in a big

data world these visualizations have to adapt to this

new frame conditions. In today’s business and rapid

changing environment, complex cognitive activities

cannot be based on a static visualization without

context or metadata. Decision maker need to know

the assumptions, source and quality of underlying

data and explanatory power to sufficient support

their thinking-process. Therefore, Segel and Heer

(2010) claim that visual storytelling is of critical

importance for providing intuitive and fast

exploration of very large amounts of data.

This overlap between different areas of research,

the mentioned research gaps and the impact on the

different areas through big data suggest that, a

comprehensive and cross-sectional view on the

topics big data, information visualization and

human-information-interaction and their connections

is essential.

1.2 Purpose

The purpose of this paper is to provide an

interdisciplinary foundation for future research and

therefore examines and clusters current literature on

big data, information visualization and human-

information-interaction. The paper therefore seeks to

address the following research questions:

1. Which relevant topics within the subject of

“interactive visualizations” can be

identified?

2. What are the research opportunities and

controversies identified by the authors?

These research questions aim to identify

and abstract future issues out of current literature for

empirical exploration. To unveil further research

relevance and topics, an interdisciplinary and

critical review of empirical research as well as

theoretical foundations is conducted. Therefore, a

systematic literature review was chosen by the

author above other alternatives to answer the stated

research questions (Okoli and Schabram, 2010;

Webster and Watson, 2002).

2 METHOD

2.1 Systematic Literature Review

Due to the recent increase in published papers

(Figure 2) and to structure this interdisciplinary area

of interest, we decided to apply a systematic

literature review and shed light on current streams

and future research possibilities. Our review of

articles in the area of interactive visualization of big

data in a management perspective is not just a

summary of available and relevant literature.

Therefore, it is interdisciplinary because it aims to

identify interferences, similarities and differences

among the various management perspectives and

show to what extent they have discussed this area of

interest. Additionally, this research should also help

readers to better understand the area of focus and

highlight possible purposes for their own work.

Systematic reviews of pertinent literature are

conducted for a broad range of objectives. They

reach from providing a theoretical background for

subsequent research or answering practical questions

by understanding the existing research to constitute

an original and valuable work of research itself.

Therefore, this systematic literature review aims to

identify intersections between the different streams

and among disciplines. The outcome of this paper

should lead to a complete understanding of

information visualization of big data in a

management perspective and highlight future

research area. According to Okoli and Schabram

(2010) a rigorous stand-alone literature review has to

be systematic and guided by a methodological

approach. It is essential to explain the procedures by

which it is conducted, exhaustive in its scope and

including all pertinent literature to be reproducible

by others who want to follow the same approach to

review the topic.

In this research paper we follow the guide of

Okoli and Schabram (2010) and use a detailed

protocol for recording our approach. This should

provide a sound basis to retrace this systematic

literature review. In the first step, we gathered a first

list of key words to start the initial literature search.

After reading through the first papers, the relevant

keywords were marked and added to the list. This

list, originally based on Falschlunger et al. (2015)

and Falschlunger et al. (2016), expanded to a mind

map grouping all relevant keywords into topics and

serves as starting point for this systematic review of

relevant literature (Figure 1).

WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies

Figure 1: Relevant keywords grouped by topics.

This review consists of papers published in

peer reviewed journals listed in the Index of

Information Systems Journals and the Computing

Research and Education Association derived from

the databases “EBSCO Business Source Premier”,

“Sage Premier” and “Science Direct” from 2005 to

2015. To take the broad scope of

the interdisciplinary topic into account, it was

necessary to extend the focus on several databases

and journals. The used database allowed a keyword

search in the title or abstract with the keywords

derived from the authors initial literature search

(Figure 1) and combined by the Boolean operator

‘AND’. Applying this approach resulted in a list of

993 abstracts. After reviewing all these abstracts,

122 publications in 46 journals were identified as

relevant by the authors and form the basis of the

systematic literature review. The distribution of the

peer-reviewed articles arising from the keyword

search is as follows (Table 1, figure 2 and figure 3):

Table 1: Distribution by databasis.

Database Papers identified

EBSCO Business Source Premier 29

SAGE Journals 58

ScienceDirect 35

Total 122

Figure 2: Identified papers by year of publication.

Figure 3: Identified papers grouped by Scientific Journal

Ranking (SJR).

Levy and Ellis (2006) state that, theoretical

saturation can only be achieved by applying a

backward search beneath the keyword search. The

backward search will be based on the identified

publications and added to the collection of papers.

2.2 Limitations

In this research the authors applied the keyword

search as well as the backward search to ensure

theoretical saturation. However, the author cannot

guarantee that all relevant studies for answering the

research questions have been identified. By failing

to identify all relevant studies, important models or

theories might not be included in this review.

Furthermore, by only including studies published in

journals listed in the Index of Information Systems

Journals and the Computing Research and Education

Association, studies relevant to the subject not

published in listed journals have not been

considered. (Bryman and Bell, 2011).

3 CONCLUSION AND SUMMARY

According to the number of published peer-reviewed

publications, there has been an increasing interest in

this the field of interactive visualizations. This

recent development and the increasing involvement

of several major research areas seek for a critical,

systematic and transparent view of current literature.

The initial review of abstracts and identified papers

draws the conclusion, that interactive visualization

in a management perspective is a nascent field of

research. Traditionally, visualizations were

investigated in a scientific context and far too little

attention has been paid to the application

possibilities in management research. Additionally,

the implications of cognition processes and

0 - 0,500 0,501 -

1,000

1,001 -

1,500

1,501 -

2,000

> 2,000

Interactive Visualization and Big Data - A Management Perspective

information seeking behaviour on effective decision

making are also underrepresented in current

literature.

However, latest research starts to address

implications of cognition processes and information

seeking behaviour on sense-making and indicates a

growing interest in this area of research. To date

there has also been little evidence about

effectiveness measurement methods and associated

variables. This area of interest should also be

investigated in future research to prepare the ground

for quantitative analysis of interactive visualizations.

To examine this preliminary findings the authors

follow the instructions for a systematic literature

review provided by Okoli and Schabram (2010) and

continue by conducting a backward search to gain

theoretical saturation (Levy and Ellis, 2006). The

additional encountered publications will be added to

the previous gathering. This collection of peer-

reviewed publications will afterwards be reviewed

and classified into relevant research topics.

The several relevant research topics will then be

summarized and the quality of the included studies

will be appraised. For each topic the following

questions will be answered

• What are the key theories or concepts?

• What are the main questions and problems that

have been addressed to date?

• What are the major issues and debates about

the topic?

• Who are the main authors or research

institutions driving this research topic?

These findings will enhance our understanding of

interactive visualizations of big data in a

management context. Future research should

therefore concentrate on the investigation of the

influence of interactive visualizations on effective

management decision-making, and therefore

provides valuable insights into developing

innovative visualization tools.

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