Providing Clarity on Big Data: Discussing Its Definition and

the Most Relevant Data Characteristics

Matthias Volk

, Daniel Staegemann

and Klaus Turowski

Magdeburg Research and Competence Cluster VLBA, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

Keywords: Big Data, Definition, Data Characteristics, Overview.

Abstract: For many years now, the domain of big data has received lots of attention, as numerous studies, reports, and

research articles reveal. Up to this date, a multitude of different definitions, guidelines, technologies,

architectures, and best practices appeared that were supposed to provide clarity in the jungle of existing

solutions. Instead, it led to further confusion regarding the general nature and the applicability of big data. To

overcome those obstacles in detail, in the following, a thorough description of the term big data. To elucidate

those obstacles, in the following, some of the most important aspects regarding the terminology as well as the

essence of big data as a concept are discussed. Hereby, subsequent researchers as well as practitioners

interested in the domain are provided with a strong base that further considerations and future projects can be

built upon. This, in turn, hopefully helps to facilitate the progression of the domain as a whole as well as its

further proliferation.

1 INTRODUCTION

For many years now, the domain of big data has

received lots of attention, as numerous studies,

reports, and research articles reveal (Staegemann et al.

2019). Up to this date, a multitude of different

definitions, guidelines, technologies, architectures,

and best practices appeared that were supposed to

provide clarity in the jungle (Volk et al. 2019) of

existing solutions and help to harness the immense

potential of the concept (Müller et al. 2018). Instead,

this deluge led to further confusion regarding the

general nature and the applicability of big data. To

overcome those obstacles, in the following, some of

the most important aspects regarding the terminology

as well as the essence of big data as a concept are

discussed. First, a short historical outline is presented

at which existing definitions are discussed. Since the

underlying data characteristics are not only the

foundation for most of these approaches but also

essential to the overall understanding of the domain

itself, these are discussed afterward. Hereby,

subsequent researchers as well as practitioners

interested in the domain are provided with a strong

base that further considerations and future projects

https://orcid.org/0000-0002-4835-919X

https://orcid.org/0000-0001-9957-1003

can be built upon. This, in turn, hopefully helps to

facilitate the progression of the domain as a whole as

well as its further proliferation.

2 DEFINITION

In the yearly published hype cycle of the business

analytics company Gartner, the maturity and adoption

of recent technologies are graphically represented

according to the five key-phases that each of those

experience during their life cycle. Namely, those are

the innovation trigger, the peak of inflated

expectations, the trough of disillusionment, the slope

of enlightenment, as well as the plateau of

productivity (Gartner 2022b). Throughout those

stages, each depicts whether new technology can be

seen as hype or an ideal market solution that creates

revenue. An example of the hype cycle in 2011 is

provided in Figure 1.

In 2011 the term big data appeared for the first time

in that life cycle (Gartner 2011). After becoming a

hype topic quickly in the following years, in 2014 it

passed the peak of inflated expectations (Gartner 2014)

before it vanished completely in 2015 (Gartner 2015).

Volk, M., Staegemann, D. and Turowski, K.

Providing Clarity on Big Data: Discussing Its Deﬁnition and the Most Relevant Data Characteristics.

DOI: 10.5220/0011537500003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS, pages 141-148

ISBN: 978-989-758-614-9; ISSN: 2184-3228

141

Figure 1: The Gartner hype cycle 2011 (Gartner 2011).

One of the main reasons lies in the tremendous

effort put into researching and applying this topic in

academia and industry, especially in the first years of

its incorporation into the hype cycle. According to

Betsy Burton, a business analyst of Gartner, the term

big data “has become prevalent in our lives across

many hype cycles” (Woodie 2015). This statement

indicates the importance and interdisciplinary nature

that big data quickly achieved. Compared to other

technologies, it has become rapidly indispensable for

today's organizations.

The definition of the term itself also constitutes

this evolution. Since the initial mention (Diebold

2021) in a research article in (Cox and Ellsworth

1997), the definition and application of the term big

data tremendously changed. According to Cox and

Ellsworth (1997), big data can be distinguished in

collections and objects. While collections refer to the

data sets acquired and aggregated, the objects solely

focus on single data elements. In both cases, these can

sometimes be too big under the given context.

Although some minor evidence was put on other

relevant data characteristics, these are only implicitly

mentioned, such as highlighting the relevance of

meta-data that sometimes comes with an extensive

collection or the origin out of heterogeneous

databases. This trend slowly continued until the

subsequent mention by Roger Mouglas from O’Reily

(Mishra et al. 2021), who emphasized that big data

“refers to a large set of data that is almost impossible

to manage and process using traditional business

intelligence tools” (Dontha 2017). Concurrently to

that, the first release of Apache Hadoop, which is

today known as one of the core technologies in the

domain of big data, emerged. Other approaches that

arose afterward didn’t exclusively consider the

volume of the data (Chen et al. 2014). Starting from

that, other data characteristics were recognized (Al-

Mekhlal and Ali Khwaja 2019; Grandhi and Wibowo

2018), and sometimes additional specificities are

highlighted, such as the need for scalable

technologies (Chang and Grady 2019) or the

challenges with established technologies (Manyika et

al. 2011). For that reason, it is not surprising that

many different research articles almost exclusively

deal with the exploration of an applicable definition,

such as (Ward and Barker 2013; Ylijoki and Porras

2016). Some of those definition approaches, over the

course of the years, are depicted in Table 1.

Those definitions showcase the maturation of the

term. However, at the same time, it becomes evident

that many discrepancies still exist, and the domain is

undergoing continuous transformations. Although the

data characteristics, or the nature of the data regarding

different dimensions, are still the central focus today,

it is above all the technologies, techniques, and

general paradigms that make big data appear

remarkable. Therefore, it serves today as a technical

foundation for many different data-intensive applica-

tion scenarios requiring sophisticated technological

concepts. The specificities, which are often differently

discussed, are presented in the following.

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

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Table 1: Selected definitions of the term big data.

Reference Yea

Definition

(Cox and Ellsworth

1997)

1997 „Big data objects are just that -- single data objects (or sets) that are too large to be

rocessed by standard algorithms and software on the hardware one has available.”

(Manyika et al. 2011) 2011 „Big data refers to datasets whose size is beyond the ability of typical database

software tools to ca

ture, store, mana

e, and anal

ze.“

(BITKOM 2012) 2012 „Big Data refers to the analysis of large amounts of data from diverse sources at

h s

eed with the aim of

eneratin

economic benefits“

(Chen et al. 2012) 2012 Big data summarizes technological developments in the area of data storage and

data processing that provide the possibility to handle exponential increases in data

volume

resented in an

e of format in steadil

decreasin

eriods of time”

(Mayer-Schönberger and

Cukier 2013)

2013 “Big data refers to things one can do at a large scale that cannot be done at a smaller

one, to extract new insights or create new forms of value, in ways that change

markets, organizations, the relationship between citizens and governments, and

more”

(Hashem et al. 2015) 2015 „Big data is a set of techniques and technologies that require new forms of

integration to uncover large hidden values from large datasets that are diverse,

com

lex, and of a massive scale“

(Chang and Grady 2019) 2015

„Big Data consists of extensive datasets primarily in the characteristics of volume,

velocity, variety, and/or variability that require a scalable architecture for efficient

stora

e, mani

ulation, and anal

sis“

(Grandhi and Wibowo

2018)

018

“Big data is defined as a large collection of multifaceted data sets, which can also

be described as being high volume, variety and velocity, making difficult to move

and process instantly with the traditional database management systems”

(Gartner 2022a)

022

“Big data is high-volume, high-velocity and/or high-variety information assets that

demand cost-effective, innovative forms of information processing that enable

enhanced insight, decision making, and process automation.”

3 DATA CHARACTERISTICS

The nature of the data plays a decisive role in this big

data domain and is often referred to by the term data

characteristics. As already highlighted by a multitude

of different definitions (cf. Table 1) and

contributions, as thoroughly investigated in (Volk et

al. 2016), the quantity (volume), variety (variety), and

speed (velocity) of the data are the most widely

acknowledged ones. Notably, all of those start with

the letter V, which is an implicitly followed property

of the data characteristics formulation in the domain

of big data. Presumably, this can be traced back to

their origin. The main characteristics, which are

commonly abbreviated as the 3V´s go back to the

former Gartner (formerly META) data analyst Doug

Laney and his report from 2001, titled 3D Data

Management: Controlling Data Volume, Velocity,

and Variety (Laney 2001). In this report, he discussed

the potentials and challenges associated with

considering these three dimensions. In the following

years, these characteristics were widely applied and

further developed in the context of a continuous data

increase. To this day, the 3V´s are probably the most

critical differentiators when it comes to the

consideration of a data-intensive endeavor. However,

even though a broad acceptance was achieved by

many researchers and practitioners, sometimes

different descriptions of each of them can be found.

Generally speaking, as highlighted by the given

definitions before, this circumstance is notable for

almost all specificities of the domain of big data.

As a result of the ongoing global digitalization, for

the year 2025 a volume of 175 Zettabytes (ZB) of data

is expected to be generated worldwide, compared to 33

Zettabytes in 2018 (Reinsel et al. 2018).

To give perspective: One zettabyte is equal to one

trillion gigabytes. If you would store the total amount

of 175 ZB on DVD blanks, you would be able to

create 23 stacks, each of which would reach from the

earth to the moon (Reinsel et al. 2018).

Volume, as the most prevailing data

characteristic, stands eponymous for the amount of

data that must be acquired, stored, and processed.

Within the literature, as most of the other

characteristics too, it is considered heterogeneously

and refers to the number of data elements and their

sheer size (Al-Mekhlal and Ali Khwaja 2019; Chang

and Grady 2019; Demchenko et al. 2013). However,

Providing Clarity on Big Data: Discussing Its Deﬁnition and the Most Relevant Data Characteristics

143

at what point in time one can speak of large volumes

of data has not yet been clearly defined (Assunção et

al. 2015). Individual studies repeatedly deal with

different definitions and metrics for the analysis and

assessment of the volume, as found out in previous

research (Volk et al. 2016). While many of them

attempt to provide generic definitions, such as too

huge to handle with established databases, others

indicate data in terabytes, like (Arockia et al. 2017).

However, those often don’t follow any processes that

provide enough evidence or a clear argumentation.

Variety, as another core characteristic, refers to

the diversity of the data in terms of structure. Usually

it can be distinguished into structured, semi-

structured and unstructured data (Erl 2016; Gani et al.

2016). The first type describes data sets with a fixed

scheme and can be stored, managed, and analyzed

without great effort. A common example is the

relational data model, in which information is stored

row-based in a tabular format (Erl 2016). The semi-

structured data partially contain information about the

underlying structure. Still, they are further modifiable

and extendable, for example, when using the

exchange format of the Extensible Markup Language

(XML) (Erl 2016). Contrary to the types above,

unstructured data can mostly be handled by special

procedures only (Gandomi and Haider 2015). This

circumstance is mainly due to the fact that, despite an

alleged similarity, great differences in the ability to

process the data may occur, for example, when

different file formats are present. Image files are a

good example of this. While common formats, such

as Joint Photographic Experts Group (JPEG),

Portable Network Graphics (PNG), or bitmap (BMP),

can be used to display the images, vector graphics, or

program-specific formats with additional meta-

information are also capable. Many authors

understand diversity not only in terms of pure

structure. For instance, in (Chang and Grady 2019),

the incorporation from various sources is meant.

Further factors such as the content of the data, the

underlying context, the used language, units, or

formatting rules are also addressed. In the case of the

combination of differently structured data, e.g.,

through the merging of internal and external holdings,

poly-structured data is also sometimes referred to

(BITKOM 2014).

Velocity, as the last of the three big data core

characteristics, refers to the speed of the data. As in

the case of the previous characteristics, there is no

universally accepted definition. While many experts

in this field address the speed of the pure data

processing (Arockia et al. 2017; Khan et al. 2019),

others also refer to the speed with which the data

arrives (Erl 2016; Gandomi and Haider 2015).

Notwithstanding that, velocity is usually expressed in

batch, (near) real-time processing as well as

streaming. While batch processing is a sequential and

complete processing of a certain amount of data

(BITKOM 2014; Ghavami 2021), (near) real-time

processing is described as an almost continuous

processing method. Especially when the speed

requirement increases, the processing becomes a non-

trivial task. Hence, various technologies,

frameworks, and architectures have been developed

to deal with this challenge. A graphical overview

containing the referred core data characteristics that

build the foundation of big data as well as some of the

arguably most common supplementary ones, is

depicted in Figure 2.

Figure 2: Overview of existing characteristics in the domain of big data, based on (Volk et al. 2020).

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

144

Table 2: Data characteristic definitions based on (Volk et al. 2019).

Characteristic Definition

Volume Volume indicates the amount of data that has to be handled.

Variety Variety refers to the heterogeneity of data and its sources.

Velocity Velocity denominates the speed at which data are incoming and the speed at which received

data mus

e processed.

Volatility Volatility refers to the tendency for data structures to change over time.

Variability Variability corresponds to the change of the other characteristics.

Veracity Veracity reflects the reliability and trustworthiness of the data.

Value Value refers to economic value that emerges out of the processing of the data.

Consistency Consistency refers to the data that flows among various sources and is shared by multiple

users. In doin

so the data needs to be in a consistent status all time.

Noteworthy, apart from these characteristics,

many more have emerged since the origin of the term

big data and the first mentioning of the data

characteristics. Some of these have become widely

accepted and used by researchers and practitioners

today.

Volatility, as another characteristic, “refers to the

tendency for data structures to change over time”

(Chang and Grady 2019). While this statement may

lead to the general idea of a data structure change, as

described by the variety, it rather focuses on the

overall changes of the data over time and its

useability. The analysis of the data that was collected,

stored, and processed over time may experience a

certain shift regarding its level of detail or use (Chang

and Grady 2019; Kapil et al. 2016). Hence, a holistic

consideration of novel and historical data is required.

Variability, as an additional characteristic, refers

to fluctuations that may occur in the context of the

processed data. While some authors, such as (Hussein

2020), focus on the changes of the data itself, others

understand by it the variations of the three core

characteristics either in an isolated or compound way

(Chang and Grady 2019; Gandomi and Haider 2015;

Katal et al. 2013). Because of that, an impact on the

overall data processing may occur that needs to be

counteracted. This includes the data flow rate,

structure, and volume. In doing so, sophisticated

approaches are required to overcome emerging

problems and increase the system's flexibility. One of

them is the scaling of related systems, horizontally

and vertically, for instance, by harnessing the

capabilities of cloud computing (Chang and Grady

2019).

Veracity provides information about the data

quality and thus the reliability of the raw data. It was

first coined by IBM report in 2013 (IBM 2013).

Especially when it comes to the necessity to utilize

unreliable data for big data projects, both the correct

context and the used analysis method are of utmost

importance (Gandomi and Haider 2015). In the case

of unreliable data, one measure could be to merge

several sources in order to increase the overall

quality, and thus the trustworthiness (IBM 2013;

Izadi et al. 2015).

With the value of data, Oracle (Dijcks 2013)

introduced a meta-data characteristic, rather than a

stand-alone data characteristic, as it is heavily

influenced by other characteristics, such as veracity,

velocity, volume, and variety (Erl 2016). It focuses on

the economic value of the data to be processed. It is

possible to extract information and gain previously

hidden knowledge, especially with semi- and

unstructured data, which differ from traditional data

structures. Often, however, the data in its pure form

do not contain any significant benefit, which means

that preceding processing steps and analyses are

necessary to generate such value (Gandomi and

Haider 2015).

Apart from those existing V’s, other concepts

were introduced in recent years, providing further

data characteristics that do not follow this pattern.

The 3C model addresses the cost, complexity, and

consistency in the context of the data to be processed.

While the first focuses on the overall monetary

expenditures required for a technological realization,

the second indicates the severity of connections and

relations between the data. The last C, the

consistency, refers to the “data that flows among

various sources and is shared by multiple users” (Bedi

et al. 2014). Generally speaking, due to the distributed

nature of data and related systems in big data, keeping

a consistent state can be demanding. In many cases,

simultaneous writes and reads have to be carefully

Providing Clarity on Big Data: Discussing Its Deﬁnition and the Most Relevant Data Characteristics

145

observed to avoid adhering problems (Bedi et al.

2014; Chang and Grady 2019).

While the characteristics mentioned above have

been widely recognized today, numerous other

papers, studies, and reports have attempted to

establish additional data characteristics that are less

widely used. As a result, reports emerged that present

10V’s (Kapil et al. 2016), 17V’s (Arockia et al. 2017),

42 V’s (Schafer 2017), 51V’s (Khan et al. 2019) or

even 56V’s (Hussein 2020).

However, as many authors already highlight by

themselves, not all of them are always important or

even applicable. An overview of the arguably most

essential definitions is given in Table 2.

4 CONCLUSION

Big data has become one of the most important

technological trends of our time. With its tremendous

influence on almost all aspects of today’s society, the

concept has sparked the interest of scientists and

practitioners alike. This not only led to numerous

publications dealing with the topic, but also to a

variety of definitions and explanations of the term

itself as well as the associated characteristics.

However, to facilitate a fruitful discourse, it is

necessary to have a common understanding of the

corresponding terminology. For this reason, the

publication at hand discusses the term, its origins as

well as the most relevant data characteristics that can

be found in the literature. Furthermore, some

exemplary works that extend the set of data

characteristics beyond the most common ones are

highlighted.

Hereby, subsequent researchers as well as

practitioners interested in the domain are provided

with a strong base that further considerations and

future projects can be built upon. This, in turn,

hopefully helps to facilitate the progression of the

domain as a whole as well as its further proliferation.

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