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ORIGINAL ARTICLE

Big data analytics capabilities: a systematic literature review and research agenda

Patrick Mikalef1 • Ilias O. Pappas1 • John Krogstie1 •

Michail Giannakos1

Received: 15 November 2016 / Revised: 3 July 2017 / Accepted: 12 July 2017 /

Published online: 15 July 2017

� Springer-Verlag GmbH Germany 2017

Abstract With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they

can incorporate the shifts these technologies bring into their competitive strategies.

To date, emphasis has been on the technical aspects of big data, with limited

attention paid to the organizational changes they entail and how they should be

leveraged strategically. As with any novel technology, it is important to understand

the mechanisms and processes through which big data can add business value to

companies, and to have a clear picture of the different elements and their interde-

pendencies. To this end, the present paper aims to provide a systematic literature

review that can help to explain the mechanisms through which big data analytics

(BDA) lead to competitive performance gains. The research framework is grounded

on past empirical work on IT business value research, and builds on the resource-

based view and dynamic capabilities view of the firm. By identifying the main areas

of focus for BDA and explaining the mechanisms through which they should be

leveraged, this paper attempts to add to literature on how big data should be

examined as a source of competitive advantage. To this end, we identify gaps in the

extant literature and propose six future research themes.

Keywords Big data � Dynamic capabilities � Resource-based view � Competitive performance � IT strategy

& Patrick Mikalef [email protected]

1 Norwegian University of Science and Technology, Trondheim, Norway

123

Inf Syst E-Bus Manage (2018) 16:547–578

https://doi.org/10.1007/s10257-017-0362-y

1 Introduction

The application of big data in driving organizational decision making has attracted

much attention over the past few years. A growing number of firms are focusing

their investments on big data analytics (BDA) with the aim of deriving important

insights that can ultimately provide them with a competitive edge (Constantiou and

Kallinikos 2015). The need to leverage the full potential of the rapidly expanding

data volume, velocity, and variety has seen a significant evolution of techniques and

technologies for data storage, analysis, and visualization. However, there has been

considerably less research attention on how organizations need to change in order to

embrace these technological innovations, as well as on the business shifts they entail

(McAfee et al. 2012). Despite the hype surrounding big data, the issue of examining

whether, and under what conditions, big data investments produce business value,

remains underexplored, severely hampering their business and strategic potential

(McAfee et al. 2012). Most studies to date have primarily focused on infrastructure,

intelligence, and analytics tools, while other related resources, such as human skills

and knowledge, have been largely disregarded. Furthermore, orchestration of these

resources, the socio-technological developments that they precipitate, as well as

how they should be incorporated into strategy and operations thinking, remains an

underdeveloped area of research (Gupta and George 2016).

Over the past few years, several research commentaries have stressed the

importance of delving into the whole spectrum of aspects that surround BDA

(Constantiou and Kallinikos 2015; Markus 2015). Nevertheless, exploratory

empirical literature on the topic is still quite scarce (Gupta and George 2016;

Wamba et al. 2017). Past literature reviews on the broader information systems (IS)

domain have demonstrated that there are multiple aspects that should be considered

when examining the business potential of IT investments (Schryen 2013).

Furthermore, the particularities of each technological development need to be

thoroughly examined in order to fully capture the interdependencies that develop

between them, and how they produce value at a firm level. Past literature on IT

business value has predominantly used the notion of IT capabilities to refer to the

broader context of technology within firms, and the overall proficiency in leveraging

and mobilizing the different resources and capabilities (Bharadwaj 2000). It is

therefore important to identify and explore the domain-specific aspects that are

relevant to BDA within the business context (Kamioka and Tapanainen 2014).

While there is a growing stream of literature on the business potential of BDA,

there is still limited work grounded on established theories used in the IT-business

value domain (Gupta and George 2016). The lack of empirical work in this direction

significantly hinders research concerning the value of BDA, and leaves practitioners

in unchartered territories when faced with implementing such initiatives in their

firms. Hence, in order to derive meaningful theoretical and practical implications, as

well as to identify important areas of future research, it is critical to understand how

the core artifacts pertinent to BDA are shaped, and how they lead to business value

(Constantiou and Kallinikos 2015). Therefore, we employ a systematic literature

review grounded in the established resource-based view (RBV) of the firm, as well

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as the emerging dynamic capabilities view (DCV). We select these theoretical

groundings since the former provides a solid foundation upon which all relevant

resources can be identified and evaluated towards their importance, while the latter

enables examination of the organizational capabilities towards which these

resources should be directed in order to achieve competitive performance gains

(Mikalef et al. 2016a, b). As such, the DCV exerts complementarities in relation to

the RBV by providing an explanation of the rent-yielding properties of organiza-

tional capabilities that can be leveraged by means of BDA (Makadok 2001). Our

theoretical framework that guides the systematic literature review uncovers some

initial findings on the value of BDA, while also providing a roadmap on several

promising research streams.

The rest of the paper is structured as follows. In Sect. 2, we describe the research

methodology used to conduct the systematic literature review, and outline the main

steps followed. Next, in Sect. 3, we distinguish between the concepts of big data,

BDA, and BDA capability, and present some definitions as described in literature

for each. In Sect. 4, we proceed to describe the main theoretical foundations upon

which we build on and develop the proposed research framework. We then

summarize existing work on the business value of BDA according to the identified

themes. In Sect. 5, we outline a series of areas that are currently under-researched

and propose appropriate theoretical stances that could be utilized in their

examination. In closing, Sect. 6 presents some concluding remarks on the area of

BDA and their application to the strategic domain.

2 Research methodology

Following the established method of a systematic literature review (Kitchenham

2004, 2007; Kitchenham et al. 2009), we undertook the review in distinct stages.

First, we developed the review protocol. Second, we identified the inclusion and

exclusion criteria for relevant publications. Third, we performed an in-depth search

for studies, followed by critical appraisal, data extraction and a synthesis of past

findings. The next sub-sections describe in detail the previously mentioned stages

(Fig. 1).

2.1 Protocol development

The first step of the systematic literature review was to develop a protocol for the

next steps. In accordance with the guideline, procedures, and policies of the

Cochrance Handbook for Systematic Reviews of Intervention (Higgins and Green

2008), the protocol established the main research question that guided the selection

of papers, the search strategy, inclusion and quality criteria, as well as the method of

synthesis. The review process was driven by the following research question: What

are the definitional aspects, unique characteristics, challenges, organizational

transformations, and business value associated with big data? By focusing on these

elements of the research question, the subject areas and relevant publications and

materials were identified.

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2.2 Inclusion and exclusion criteria

Due to the importance of the selection phase in determining the overall validity of

the literature review, a number of inclusion and exclusion criteria were applied.

Studies were eligible for inclusion if they were focused on the topic of how big data

can provide business value. Publications were selected from 2010 onwards, since

that is when the term gained momentum in the academic and business communities.

The systematic review included research papers published in academic outlets, such

as journal articles and conference proceedings, as well as reports targeted at

business executives and a broader audience, such as scientific magazines. In-

progress research and dissertations were excluded from this review, as were studies

that were not written in English. Finally, given that our focus was on the business

transformation that big data entails, along with performance outcomes, we included

quantitative, qualitative, and case studies. Since the topic of interest is of an

interdisciplinary nature, a diversity of epistemological approaches was opted for.

2.3 Data sources and search strategy

The search strategy started by forming search strings that were then combined to

form keywords. In addition, during the search we employed wildcard symbols in

Fig. 1 Stages of the study selection process

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order to reduce the number of search strings. Combinations of two sets of keywords

were used, with the first term being ‘big data,’ and the second term being one of 12,

which were reviewed by a panel of five experts. These search terms included:

analytics capability, competitive performance, firm performance, organizational

performance, dynamic capabilities, resource-based view, human skills, managerial

skills, analytics ecosystems, data scientist, competencies, and resource management.

Keywords were searched within the title, abstract, and keyword sections of the

manuscripts. The search strategy included electronic databases such as Scopus,

Business Source Complete, Emerald, Taylor & Francis, Springer, Web of

Knowledge, ABI/inform Complete, IEEE Xplore, and the Association of Informa-

tion Systems (AIS) library. To further complement our search, we applied the search

terms in the search engine Google Scholar, as well as the AIS basket of eight

journals.

The search was initiated on September 5, 2016 and ended on February 26, 2017.

At stage 1, 459 papers were identified and entered into the reference manager

EndNote. At stage 2, all authors went through the titles of the studies of stage 1 in

order to determine their relevance to the systematic review. At this stage, studies

that were clearly not about the business aspects of big data were excluded,

independently of whether they were empirical. In addition, articles that were

focused on big data for public administration were not included in the next stage.

The number of retained articles after the abovementioned process was 228. In the

third stage, all remaining articles were examined in terms of their abstracts and their

focus in relation to the research question we had defined. However, some abstracts

were of varying quality, some were lacking information about the content of the

article, while others had an apparent disconnect with their title and did not fit our

focus. At this stage, each papers’ abstract was reviewed independently by each

author. From the 228 abstracts assessed, 101 were omitted, leaving 127 papers to be

further analyzed.

2.4 Quality assessment

Each of the 127 papers that remained after stage 3 was assessed independently by

the authors in terms of several quality criteria. Studies were examined in terms of

scientific rigor, so that appropriate research methods had been applied; credibility,

to assess whether findings were well presented; and relevance, which was assessed

based on whether findings were useful for companies engaging in big data projects,

as well as the academic community. Taken together, these criteria provided a

measure of the extent to which a publication would make a valuable contribution to

the review. At this stage another 43 papers were excluded, leaving 84 papers for

data extraction and synthesis. These papers were then coded according to their area

of focus, allowing a categorization to be constructed. The derived categories were a

result of identifying the main research areas that papers aimed to contribute towards.

By categorizing papers, we were able to extract the details needed to answer each of

the posed research questions.

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2.5 Data extraction and synthesis of findings

In order to synthesize findings and categorize studies based on their scope, an

analysis of the different research streams was performed. The first step was to

identify the main concepts from each study, using the authors’ original terms. The

key concepts were then organized in a spreadsheet in order to enable comparison

across studies and translation of findings into higher-order interpretations. An

analysis was conducted based on the following areas of focus: organizational

performance outcomes of big data, human skills and knowledge, tangible and

intangible resources, team orchestration and project management, adoption and

diffusion of big data initiatives, governance in big data projects, as well as ethical

and moral issues related to big data within the business domain. For empirical

studies, the the authors also recorded the type of study conducted (e.g. qualitative,

quantitative, case study), the sample size, the instruments used (e.g. surveys,

interviews, observations), as well as contextual factors surrounding the study (e.g.

industry, country, firm size). Constant consensus meetings of all researchers

established the data extraction stage and the categorization of publications. The

remaining 84 papers were analyzed in detail in accordance with the coding scheme,

and relevant data were extracted, analyzed, and synthesized.

3 Defining big data in the business context

Big data is becoming an emerging topic of interest in IS, computer and information

sciences, management, and social sciences (Constantiou and Kallinikos 2015). This

phenomenon is largely attributed to the widespread adoption of social media,

mobile devices and sensors, integrated IS, and artifacts related to the Internet of

Things. The surging interest in big data is also reflected in the academic literature,

which spans multiple disciplinary domains (Chen et al. 2016). While the different

epistemological domains provide an alternative perspective on the notion of big

data, the definitions and key concepts put forth by each differ significantly (Wamba

et al. 2015). As such, the first step of the systematic literature review is to identify

the key concepts and develop integrative definitions of each. Notions such as big

data, BDA, and BDA capability are often used interchangeably in the literature.

However, their theoretical underpinnings reflect a different perspective in how they

are perceived and measured (Cao and Duan 2014a). Therefore, it is imperative to

clearly define the meaning of these concepts, and that aspects they encompass.

3.1 Big data

As a starting point, we provide an overview of how big data have been defined in

past studies, as well as what attributes are integral to the concept. Several definitions

of big data have been put forth to date in attempts to distinguish the phenomenon of

big data from conventional data-driven or business analytics approaches (Table 1).

Some scholars focus on the origin of the data, emphasizing the various channels

from which they are collected, such as enterprise IS, customer transactions,

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Table 1 Sample definitions of big data

Author(s) and date Definition

Russom (2011) Big data involves the data storage, management, analysis, and visualization

of very large and complex datasets

White (2011) Big data involves more than simply the ability to handle large volumes of

data; instead, it represents a wide range of new analytical technologies and

business possibilities. These new systems handle a wide variety of data,

from sensor data to Web and social media data, improved analytical

capabilities, operational business intelligence that improves business agility

by enabling automated real-time actions and intraday decision making,

faster hardware and cloud computing including on-demand software-as-a

service. Supporting big data involves combining these technologies to

enable new solutions that can bring significant benefits to the business

Beyer and Laney (2012) Big data: high-volume, high-velocity, and/or high-variety information assets

that require new forms of processing to enable enhanced decision making,

insight discovery, and process optimization

McAfee et al. (2012) Big data, like analytics before it, seeks to glean intelligence from data and

translate that into business advantage. However, there are three key

differences: Velocity, variety, volume

Gantz and Reinsel (2012) Big data focuses on three main characteristics: the data itself, the analytics of

the data, and presentation of the results of the analytics that allow the

creation of business value in terms of new products or services

Boyd and Crawford

(2012)

Big data: a cultural, technological, and scholarly phenomenon that rests on

the interplay of (1) Technology: maximizing computation power and

algorithmic accuracy to gather, analyze, link, and compare large datasets.

(2) Analysis: drawing on large datasets to identify patterns in order to make

economic, social, technical, and legal claims. (3) Mythology: the

widespread belief that large datasets offer a higher form of intelligence and

knowledge that can generate insights that were previously impossible, with

the aura of truth, objectivity, and accuracy

Schroeck et al. (2012) Big data is a combination of volume, variety, velocity and veracity that

creates an opportunity for organizations to gain competitive advantage in

today’s digitized marketplace

Bharadwaj et al. (2013) Big data refers to datasets with sizes beyond the ability of common software

tools to capture, curate, manage, and process the data within a specified

elapsed time

Kamioka and Tapanainen

(2014)

Big data is large-scale data with various sources and structures that cannot be

processed by conventional methods and that is intended for organizational

or societal problem solving

Bekmamedova and

Shanks (2014)

Big data involves the data storage, management, analysis, and visualization

of very large and complex datasets. It focuses on new data-management

techniques that supersede traditional relational systems, and are better

suited to the management of large volumes of social media data

Davis (2014) Big data consists of expansive collections of data (large volumes) that are

updated quickly and frequently (high velocity) and that exhibit a huge

range of different formats and content (wide variety)

Sun et al. (2015) Big data: the data-sets from heterogeneous and autonomous resources, with

diversity in dimensions, complex and dynamic relationships, by size that is

beyond the capacity of conventional processes or tools to effectively

capture, store, manage, analyze, and exploit them

Big data analytics capabilities: a systematic literature… 553

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machines or sensors, social media, cell phones or other networked devices, news

and network content, as well as GPS signals (Chen et al. 2016; Opresnik and Taisch

2015). The majority of scholars emphasize the ‘‘three Vs’’ that characterize big data:

volume, velocity, and variety (McAfee et al. 2012; Davis 2014; Sun et al. 2015).

Volume refers to the sheer size of the dataset due to the aggregation of a large

number of variables and an even larger set of observations for each variable.

(George et al. 2016). In addition, many definitions highlight the growing rate at

which the quantity of data increases, commonly expressed in petabytes or exabytes,

used by decision makers to aid strategic decisions (Akter et al. 2016a). Velocity

reflects the speed at which these data are collected, updated, and analyzed, as well as

the rate at which their value becomes obsolete (Davis 2014; George et al. 2016).

The ‘newness’ of data that decision makers are able to collect, as well as the

capacity to analyze these data-streams, is an important factor when it comes to

improving business agility and enabling real-time actions and intraday decision

making (White 2011; Boyd and Crawford 2012). Variety refers to the plurality of

structured and unstructured data sources, which, amongst others, include text, audio,

images, video, networks, and graphics (Constantiou and Kallinikos 2015; George

et al. 2016). While there are no universal benchmarks for defining the volume,

velocity, and variety of big data, the defining limits are contingent upon size, sector,

and location of the firm, and are subject to changing limits over time (Gandomi and

Haider 2015).

Adding to the existing body of definitions, several scholars have included

different aspects of big data in their conceptualizations (Table 2). For instance, a

commonly acknowledged aspect of big data is its veracity (Akter et al. 2016a, b;

Table 1 continued

Author(s) and date Definition

Opresnik and Taisch

(2015)

Big data typically refers to the following types of data: (1) traditional

enterprise data, (2) machine-generated/sensor data (e.g. weblogs, smart

meters, manufacturing sensors, equipment logs), and (3) social data

Constantiou and

Kallinikos (2015)

Big data often represents miscellaneous records of the whereabouts of large

and shifting online crowds. It is frequently agnostic, in the sense of being

produced for generic purposes or purposes different from those sought by

big data crunching. It is based on varying formats and modes of

communication (e.g. text, image, and sound), raising severe problems of

semiotic translation and meaning compatibility. Big data is commonly

deployed to refer to large data volumes generated and made available on

the Internet and the current digital media ecosystems

Akter et al. (2016a) Big data is defined in terms of five ‘Vs:’ volume, velocity, variety, veracity,

and value. ‘Volume’ refers to the quantities of big data, which are

increasing exponentially. ‘Velocity’ is the speed of data collection,

processing and analyzing in the real time. ‘Variety’ refers to the different

types of data collected in big data environments. ‘Veracity’ represents the

reliability of data sources. Finally, ‘value’ represents the transactional,

strategic, and informational benefits of big data

Abbasi et al. (2016) Big data differs from ‘regular’ data along four dimensions, or ‘4 Vs’—

volume, velocity, variety, and veracity

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Abbasi et al. 2016). Veracity refers to the degree to which big data is trusted,

authentic, and protected from unauthorized access and modification (Demchenko

et al. 2013). Analyzing high-quality and reliable data is imperative in enabling

management to make cognizant decisions and derive business value (Akter et al.

2016b). Hence, big data used for business decisions should be authenticated and

have passed through strict quality-compliance procedures before being analyzed

(Dong and Srivastava 2013; Gandomi and Haider 2015). This vast amount of data is

argued to be an important enabler of creating value for organizations (Gandomi and

Haider 2015). Oracle introduced value as a defining aspect of big data. According to

Oracle’s (2012) definition, big data are frequently characterized by low value

density, meaning that the value of the processed data is proportionately low

compared to its volume. Seddon and Currie (2017) included two additional

dimensions in the definition of big data: variability and visualization. Variability

refers to the dynamic opportunities that are available by interpreting big data, while

visualization has to do with the representation of data in meaningful ways through

artificial intelligence methods that generate models (Seddon and Currie 2017).

3.2 Big data analytics

Some definitions of big data focus solely on the data and their defining

characteristics (Davis 2014; Akter et al. 2016a, b; Abbasi et al. 2016); others

extend and include the analytical procedures, tools, and techniques that are

Table 2 Defining characteristics of big data

Attribute Definition

Volume Volume represents the sheer size of the dataset due to the aggregation of a large number

of variables and an even larger set of observations for each variable. (George et al.

2016)

Velocity Velocity reflects the speed at which data are collected and analyzed, whether in real time

or near real time from sensors, sales transactions, social media posts, and sentiment data

for breaking news and social trends. (George et al. 2016)

Variety Variety in big data comes from the plurality of structured and unstructured data sources

such as text, videos, networks, and graphics among others. (George et al. 2016)

Veracity Veracity ensures that the data used are trusted, authentic, and protected from

unauthorized access and modification. (Demchenko et al. 2013)

Value Value represents the extent to which big data generates economically worthy insights

and/or benefits through extraction and transformation. (Wamba et al. 2015)

Variability Variability concerns how insight from media constantly changes as the same information

is interpreted in a different way, or new feeds from other sources help to shape a

different outcome. (Seddon and Currie 2017)

Visualization Visualization can be described as interpreting the patterns and trends that are present in

the data. (Seddon and Currie 2017)

3Vs: volume, velocity, variety (Chen and Zhang 2014)

4Vs: volume, velocity, variety, veracity (Zikopoulos and Eaton 2011; Schroeck et al. 2012; Abbasi et al.

2016)

5Vs: volume, velocity, variety, veracity, value (Oracle 2012; Sharda et al. 2013)

7Vs: volume, velocity, variety, veracity, value variability, visualization (Seddon and Currie 2017)

Big data analytics capabilities: a systematic literature… 555

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employed (Russom 2011; Bharadwaj et al. 2013); while some even go on to

describe the type of impact that the analysis and presentation of big data can yield in

terms of business value (White 2011; Beyer and Laney 2012; Schroeck et al. 2012;

De Mauro et al. 2015). This point is made very clear by the definition provided by

Gantz and Reinsel (2012), who state that BDA revolve around three main

characteristics: the data itself, the analytics applied to the data, and the presentation

of results in a way that allows the creation of business value. In this definition, the

process of analyzing the data is outlined without linking it to any tangible or

intangible business outcome. George et al. (2016) posit that big data refers to large

and varied data that can be collected and managed, whereas data science develops

models that capture, visualize, and analyze the underlying patterns in the data. To

make this distinction more apparent, some scholars use the term BDA to emphasize

the process and tools used in order to extract insights from big data. In essence,

BDA encompasses not only the entity upon which analysis in performed—i.e. the

data—but also elements of tools, infrastructure, and means of visualizing and

presenting insight. This distinction is quite eloquently put in the definitions of Kwon

et al. (2014), and Lamba and Dubey (2015). Nevertheless, while the definitions of

BDA encompass a wider spectrum of elements critical to the success of big data,

they do not include the organizational resources that are required to transform big

data into actionable insight. Becoming a data-driven organization is a complex and

multifaceted task, and necessitates attention at multiple levels from managers. To

address the transition to a data-driven era and provide practitioners with guidelines

on how to deploy their big data initiatives, scholars have begun utilizing the term

‘BDA capability’ to reference a company’s proficiency in leveraging big data to

gain strategic and operational insight (Table 3).

Table 3 Sample definitions of big data analytics

Authors and date Definition

Loebbecke and Picot

(2015)

Big data analytics: a means to analyze and interpret any kind of digital

information. Technical and analytical advancements in BDA, which—in large

part—determine the functional scope of today’s digital products and services,

are crucial for the development of sophisticated artificial intelligence,

cognitive computing capabilities, and business intelligence

Kwon et al. (2014) Big data analytics: technologies (e.g. database and data mining tools) and

techniques (e.g. analytical methods) that a company can employ to analyze

large-scale, complex data for various applications intended to augment firm

performance in various dimensions

Ghasemaghaei et al.

(2015)

Big data analytics, defined as tools and processes often applied to large and

disperse datasets for obtaining meaningful insights, has received much

attention in IS research given its capacity to improve organizational

performance

Lamba and Dubey

(2015)

Big data analytics is defined as the application of multiple analytic methods that

address the diversity of big data to provide actionable descriptive, predictive,

and prescriptive results

Müller et al. (2016) Big data analytics: the statistical modeling of large, diverse, and dynamic

datasets of user-generated content and digital traces

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3.3 Big data analytics capability

Despite the limited published research on big data, some studies have focused on the

challenges that companies face during the implementation of big data projects (Gupta

and George 2016; Vidgen et al. 2017). Particularly within the IS domain, researchers

recognize that the success of big data projects is not only a result of the data and the

analytical tools and processes, but includes a broader range of aspects (Garmaki et al.

2016). To address this issue, the notion of BDA capability has been proposed, which

is broadly defined as the ability of a firm to provide insights using data management,

infrastructure, and talent to transform business into a competitive force (Kiron et al.

2014; Akter et al. 2016a). Research in this area focuses on strategy-driven BDA

capabilities, and the mechanisms through which competitive performance gains are

realized (LaValle et al. 2011). Some definitions of BDA capabilities focus on the

processes that must be put in place in order to leverage big data (Cao and Duan

2014b; Olszak 2014), while others emphasize the investment of necessary resources

and their alignment with strategy (Xu and Kim 2014). In essence, the notion of BDA

capability extends the view of big data to include all related organizational resources

that are important in leveraging big data to their full strategic potential (Table 4).

Table 4 Sample definitions of big data analytics capability

Author(s) and date Definition

Davenport and Harris

(2007)

BDA capability is defined as the distinctive capability of firms in setting the

optimal price, detecting quality problems, deciding the lowest possible level

of inventory, or identifying loyal and profitable customers in big data

environments

Cao and Duan (2014a) Information processing capabilities: an organization’s capacity to capture,

integrate, and analyze big data, and utilize insights derived from that big data

to make informed decisions that generate real business value

Xu and Kim (2014) Business intelligence capabilities: a combination of a set of sub-capabilities.

Derived from IT capabilities, we define business intelligence capabilities from

the perspectives of infrastructures, skills, execution, and relationship

Olszak (2014) Dynamic business intelligence capability is the ability of an organization to

integrate, build, and reconfigure the information resources, as well as business

processes, to address rapidly changing environments

Kung et al. (2015) Big data competence: a firm’s ability to acquire, store, process, and analyze

large amounts of data in various forms, and deliver information to users that

allows organizations to extract value from big data in a timely fashion

Big data resources are defined as a combination of complementary IT resources

relevant to the utilization of big data to enhance firm performance

Garmaki et al. (2016) The BDA capability entails a firm’s ability to mobilize and deploy BDA

resources effectively, utilize BDA resources, and align BDA planning with

firm strategy to gain competitive advantage and enhance firm performance

Shuradze and Wagner

(2016)

A data analytics capability can be defined as an organization’s ability to

mobilize and deploy data analytics-related resources in combination with

marketing resources and capabilities, which constitutes an innovative IT

capability that can improve firm performance

Gupta and George

(2016)

BDA capability is defined as a firm’s ability to assemble, integrate, and deploy

its big data-specific resources

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To date, there is limited empirical research building on the notion of BDA

capability. Most studies are based on anecdotal evidence, particularly in relation to

the impact of a firm’s BDA capability on performance (Agarwal and Dhar 2014;

Akter et al. 2016a). Furthermore, there are diverging views about what constitutes

BDA capability, since different theoretical lenses are often employed. In this regard,

the purpose of the following section is to provide a theoretically driven synthesis of

past studies concerning the aspects that are important in order to develop BDA

capability. Thus, we seek to distinguish between the notion of developing a BDA

capability and leveraging the competence of a firm to enable or strengthen certain

organizational capabilities by means of BDA. We then discuss how the former is a

prerequisite for the latter, yet the existence of a BDA capability does not

automatically mean that the leveraged competence is actualized.

4 Toward the development of a big data analytics capability

4.1 Resource based theory

Developing and sustaining competitive advantage is the cornerstone of strategic

management literature, which draws on a number of interwoven yet distinct

elements and notions (Wernerfelt 1984; Amit and Schoemaker 1993). Resource-

based theory (RBT) has been widely acknowledged as one of the most prominent

and powerful theories to explain how firms achieve and sustain competitive

advantage as a result of the resources they own or have under their control (Barney

2001). According to the underlying philosophy of RBT, an organization is perceived

as a bundle of valuable tangible and intangible resources, which can be combined to

generate competitive advantage (Peteraf 1993). The original RBT defines resources

as rare, inimitable, and nonsubstitutable firm-specific assets that enable a firm to

implement a value-creating strategy to generate rents (Barney 1991). This concept

was later split to distinguish between resource-picking and capability-building, two

distinct facets that are central to RBT. Resource-picking encompasses activities of

identifying and purchasing or controlling resources that are perceived as being of

strategic value, while capability-building is concerned with the orchestration and

management of these resources into strategically useful assets (Makadok 2001).

Amit and Schoemaker (1993) define resources as tradable and nonspecific firm

assets, and capabilities as nontradable firm-specific abilities to integrate, deploy, and

utilize other resources within the firm. Makadok (2001) further elaborates on the

distinction between resource-picking and capability-building in his seminal work.

According to the author, resource-picking is an important aspect since it not only

helps the firm acquire good resources, but is also important for the economic impact

of the firm by avoiding potentially poor or unworthy resources. Capability-building,

on the other hand, is concerned with activities that relate to deploying these

resources in combination with other organizational processes for the creation of

intermediate goods, which can potentially provide enhanced productivity and

strategic flexibility. Thus, resources represent the input of the production process

while a capability is the capacity to deploy these resources in the most strategically

558 P. Mikalef et al.

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fit way. A firm’s resources and capabilities are commonly referred to as assets (Amit

and Schoemaker 1993).

Resources and capabilities are the core components of RBT, and have received a

great deal of attention in past empirical studies (Akter et al. 2016b). A characteristic

of resources is that they cannot generate any business value by themselves, but

require action to be leveraged strategically. This is indicated by Grant’s (1991)

description of resources as nouns, because they can lie dormant like an idle plant or

unused knowledge until they are needed, and can be identified independently of

their use (Wu et al. 2010). Hence, a resource is something that a firm has access to,

rather than something it can do (Größler and Grübner 2006). Several types of

resources have been suggested in the extant literature; nevertheless, one of the most

adhered-to classifications is that of Grant (1991). According to this categorization,

resources can be divided into tangible (e.g. financial and physical resources), human

skills (e.g. employees’ knowledge and skills), and intangible (e.g. organizational

culture and organizational learning) types (Grant 1991). This classification has been

predominantly followed in the IS capability literature (Bharadwaj 2000; Aral and

Weill 2007; Ravichandran and Lertwongsatien 2005).

Capabilities are described as high-level routines (or a collection of routines), with

routines consisting of learned behaviors that are highly patterned, repetitious or quasi-

repetitious, and founded in part in tacit knowledge (Winter 2003). Organizational

capabilities can be purposely built by focusing on the complex interactions between a

firm’s resources and competencies, and are therefore more complex and difficult to

imitate than just core resources (Grant 1996). According to Teece et al. (1997),

capabilities cannot easily be bought; they must be built. A basic premise of RBT is that

the capability-building process can only take place following acquisition of a resource;

therefore, developing capabilities is dependent on, and confined under, the types of

resources a firm decides to accumulate. The conversion of resources into potentially

strategic assets via the development of firm-specific capabilities has been the subject

of considerable scholarly attention (Sirmon et al. 2011). The resource orchestration

perspective attempts to explain the role of managers in terms of how resources are

transformed into capabilities, and what necessary actions are required to effectively

structure, bundle, and leverage them. This process-oriented view, which emphasizes

the conversion of resources into capabilities, is seldom addressed and is largely

affected by the heterogeneity of firms’ contexts (Barney et al. 2011).

In terms of the form that capabilities can take, previous research in the area of

strategic management has made great strides in developing and refining the different

types of capabilities. It is generally agreed that capabilities operate quite differently

from one another, and result in varying levels of competitive advantage and firm

performance based on a number of internal and external factors (Hoopes and

Madsen 2008). Grounded in the idea that firms must be both stable enough to

continue to deliver value in their own distinctive way, and agile and adaptive

enough to restructure their value proposition when circumstances demand it, there is

a well-documented distinction between operational (ordinary) and dynamic

capabilities (Drnevich and Kriauciunas 2011). Nevertheless, the resources owned

or controlled by the firm are imperative in determining what types of capabilities a

firm can develop, and of what value they will be (Wu 2007).

Big data analytics capabilities: a systematic literature… 559

123

RBT has been extensively applied to the IT context under the notion of IT

capabilities (Bharadwaj 2000). IT literature recognizes that competence in

leveraging IT-based resources in combination with other organizational resources

is a source of competitive and advantage (Pavlou and El Sawy 2006). Past empirical

studies have employed the notion of IT capabilities to demonstrate its direct (Bhatt

and Grover 2005) or indirect impact on performance outcomes (Wang et al. 2012).

The main premise adopted in these studies is that in order to develop a robust IT

capability, it is necessary for a firm to have invested in all the necessary resources

(Wade and Hulland 2004). In the context of big data, it is important to identify the

different types of resources, since the level of their infusion in various business

functions can be a source of competitive differentiation (Davenport 2006). A

conceptual framework of RBT posits that in order for a resource or capability to be a

source of competitive advantage, it must fulfill the criteria of value, rarity,

inimitability, and nonsubstitutability (i.e. so-called VRIN attributes) (Barney 1991).

When these resources and their related activity systems have complementarities,

they are more prone to lead to competitive advantage (Eisenhardt and Martin 2000).

4.2 The dynamic capabilities view of the firm

Over the past decade, the DCV of the firm has emerged as one of the most

influential theoretical perspectives in the study of strategic management (Schilke

2014). Extending the resource-based view of the firm, which posits that a firm may

achieve sustained competitive advantage based on the bundles of resources and

capabilities it has under its control, DCV attempts to explain how a firm maintains a

competitive advantage in changing environments (Priem and Butler 2001). This

shift has been ignited by commentaries from many researchers that RBT does not

adequately explain why certain firms attain a competitive advantage in situations of

rapid and unpredictable change where resources and capabilities are subject to

erosion (Eisenhardt and Martin 2000). Originating from the Schumpeterian logic of

creative destruction, dynamic capabilities enable firms to integrate, build, and

reconfigure their resources and capabilities in the face of changing conditions

(Teece et al. 1997). In essence, dynamic capabilities reformulate the way a firm

operates and competes in the market—a process referred to as evolutionary fitness

(Helfat and Peteraf 2009). Several alternative conceptualizations of dynamic

capabilities have subsequently been presented. Some follow an approach closer to

the resource-based view, which stresses the importance of strategic management

(Teece and Pisano 1994), while others approximate the logic of evolutionary

economics, which enunciates the role of routines, path dependencies, and

organizational learning (Barreto 2010).

Despite considerable variation in defining dynamic capabilities, a growing

consensus in the literature describes them as a set of identifiable and specific

routines that have often been the subject of extensive empirical research in their

own right (Eisenhardt and Martin 2000). This approach seems to be gaining

momentum in empirical studies, since it is feasible to identify and prescribe a set of

operating routines that jointly constitute firm-level dynamic capabilities (Zollo and

Winter 2002; Pavlou and El Sawy 2011). These routines are commonly recognized

560 P. Mikalef et al.

123

as learned, highly patterned, and repetitious, directed towards independent corporate

actions (Winter 2003). Consequently, to better understand dynamic capabilities it is

feasible to emphasize the set of routines that underpin them, commonly referred to

as capabilities. In the context of IS literature, several studies have examined how IT

infused in organizational capabilities can help firms renew or reconfigure their

existing mode of operating (Pavlou and El Sawy 2006; Wang et al. 2012; Mikalef

et al. 2016a, b; Mikalef and Pateli 2017). This perspective follows the logic

proposed by Henderson and Venkatraman (1993), who stressed that alignment as a

dynamic capability is not an ad-hoc event, but rather a process of continuous

adaption and change. As such, they argued that ‘no single IT application—however

sophisticated and state of the art it may be could deliver a sustained competitive

advantage.’ Rather, what is important is to infuse IT investments into the

organizational fabric (Kohli and Grover 2008; Kim et al. 2011) (Table 5).

4.3 Resources of a big data analytics capability

While the published research on BDA capability is limited, some studies have

focused on the resources necessary to develop such capability. Although resources

are of very limited value without the underlying ability to orchestrate and leverage

them, they are fundamental building blocks in the formation of a firm’s overall BDA

capability. It is therefore important to recognize the core resources and examine the

most important debate concerning each of these as described by empirical research.

By doing so, it is possible to provide a synthesis of findings that can guide practical

support in big data deployments, and also identify underexplored areas of research

that warrant further examination. The majority of studies to date have discussed the

resources and processes that need to be used to leverage big data strategically, but

have not offered much insight into how firms can develop a strong BDA capability

(Gupta and George 2016). Building on the foundations of RBT, and work in the

field of IT management that employs the theory, we present the main resources that

allow firms to develop a BDA capability. These are divided into three main

categories: tangible resources (e.g. infrastructure, IS, and data), intangible resources

(e.g. data-driven culture, governance, social IT/business alignment), and human

skills and knowledge (e.g. data analytics knowledge, and managerial skills).

4.3.1 Tangible resources

In the context of developing a BDA capability, perhaps the core resource is the data

itself. As previously mentioned, the defining characteristics of big data is their

volume, variety, and velocity (Chen and Zhang 2014). However, it is frequently

mentioned that IT strategists and data analysts are particularly concerned with the

quality of the data they analyze (Brinkhues et al. 2014). Although traditionally firms

analyzed enterprise-specific structured data, the diversity and breadth of data

sources that contemporary firms leverage render the aspect of quality highly

important (Ren et al. 2016). Data quality is regarded as a critical resource, and is

defined in terms of completeness, accuracy, format, timeliness, reliability, and

perceived value (Brinkhues et al. 2014; Ren et al. 2016). In a heavily data-oriented

Big data analytics capabilities: a systematic literature… 561

123

economy, data resources that present the previously mentioned characteristics have

been argued to be necessary for a firm to build competitive advantage (Kiron et al.

2014). Wamba et al. (2015) stress the importance of having availability and

integrating data from various sources, which traditionally may be siloed due to

existing IT architectures. The issue of availability of data is also mentioned by

Mikalef et al. (2017), who find that it is common for companies to purchase data in

order to complement their analytics and gain more insights into their customers and

Table 5 Key definitions

Concept Definition Author(s) and date

Asset Anything tangible or intangible the firm can use in its

processes for creating, producing, and/or offering its

products (goods or services) to a market

Wade and Hulland

(2004)

Resource Stocks of available factors that the organization owns or

controls

Amit and Schoemaker

(1993)

Capability A firm’s capacity to deploy resources, usually in

combination, using organizational processes, to effect a

desired end. They are information-based, tangible, or

intangible processes that are firm-specific and are

developed over time through complex interactions

among the firm’s resources

Amit and Schoemaker

(1993)

Operational

capability

Generally involves performing an activity, such as

manufacturing a particular product, using a collection of

routines to execute and coordinate the variety of tasks

required to perform the activity

Helfat and Peteraf

(2003)

Dynamic

capability

Can be disaggregated into the capacity (a) to sense and

shape opportunities and threats, (b) to seize

opportunities, and (c) to maintain competitiveness

through enhancing, combining, protecting, and, when

necessary, reconfiguring the business enterprise’s

intangible and tangible assets

Teece (2007)

Competitive

advantage

An enterprise has a competitive advantage if it is able to

create more economic value than the marginal

(breakeven) competitor in its product market

Peteraf and Barney

(2003)

Sustained

competitive

advantage

When a firm has a competitive advantage and other firms

are unable to duplicate the benefits of this strategy

Barney (1991)

VRIN VRIN resources are valuable, rare, inimitable, and

nonsubstitutable. VRIN-ness implies that resources are

heterogeneously distributed among firms. Valuable, rare

resources may be sources of competitive advantage, but

unless they are also inimitable and nonsubstitutable, that

competitive advantage will not be sustained

Barney (1991), Peteraf

and Barney (2003)

IT resources Commodity-like assets that are widely available and can

be purchased from the factor market

Cragg et al. (2011)

IT capability The ability to mobilize and deploy IT-based resources in

combination, or copresent, with other resources and

capabilities

Bharadwaj (2000)

562 P. Mikalef et al.

123

operations. A similar phenomenon is also noted in a recent report of MIT Sloan

Management Review (Ransbotham and Kiron 2017), in which it is highlighted that

firms that share data and form alliances based on such resources tend to be more

innovative. This aspect signifies the importance that the variety and diversity of data

sources have in order to derive any meaningful insights and direct strategic

initiatives.

While data itself is a core resource, it is also important for firms to possess an

infrastructure capable of storing, sharing, and analyzing data. Big data call for novel

technologies that are able to handle large amounts of diverse and fast-moving data

(Gupta and George 2016). One of the main characteristics of such data is that it is in

an unstructured format and requires sophisticated infrastructure investments in order

to derive meaningful and valuable information (Ren et al. 2016). Some scholars

examine the big data infrastructure of firms in terms of the investments made in

specific technologies (Kamioka and Tapanainen 2014), while others focus on

features of the technology itself (Akter et al. 2016a; Wamba et al. 2015; Gupta and

George 2016; Garmaki et al. 2016). In particular, scalability and connectivity are

cited as important, since the data accumulated and processes used fluctuate

continuously. Nevertheless, it is noted by many executives that infrastructure is not

a major issue for most firms, since the technology itself has extended beyond the

requirements of analytics (Mikalef et al. 2017).

With big data, novel software and IS have emerged that facilitate distributed

storage on nonrelational databases (e.g. Hadoop, Apache Cassandra, MongoDB,

Monet, and Hazelcat), parallel processing of massive unstructured datasets, and

visualization and decision aiding (Gupta and George 2016). These technologies

extend traditional ones that were built to process data in batches, enabling the

processing of continuous flows of information in real time (Wamba et al. 2015).

Despite these differences, the systems and software employed to analyze big data

follow the same principle as that of business intelligence (Lim et al. 2013). In

essence, the value of big data analysis and visualization tools is that they transform

raw data and provide business managers and analysts with appropriate information

to improve decision making (Wixom et al. 2011). Currently, there are multiple

software tools capable of facilitating requirements from very diverse data sources,

so trying to predict which ones will prevail is risky and relatively insubstantial

(Vidgen et al. 2017) (Table 6).

4.3.2 Intangible resources

Keeping up to date in terms of knowledge and skills, and effectively coordinating

activities, resources, and tasks, is highly dependent on the capacity to forge

networks internally and externally of the firm (Ravichandran and Lertwongsatien

2005). Intangible resources therefore reflect ties, structures, and roles established to

manage the different types of resources. Governance is one of the most frequent

terms used to encapsulate all the activities and decision-appropriation mechanisms

related to IT resources (Sambamurthy and Zmud 1999). Recognizing the growing

importance of managing large volumes of information, Tallon et al. (2013) proposed

a framework specifically for uncovering the structures and practices used to govern

Big data analytics capabilities: a systematic literature… 563

123

information artifacts. Their framework distinguishes governance practices into three

types: structural (assigning responsibilities, directing, and planning), procedural

(shaping user behaviors through value analysis, cost control, and resource

allocation), and relational (business–IT partnerships, idea exchange, and conflict

resolution). Espinosa and Armour (2016) define BDA governance as the approach

that analytic-based organizations use to define, prioritize, and track analytic

Table 6 Tangible resources

Resource type Characteristics Authors and date

Tangible

Data Accuracy

Timeliness

Reliability

Security

Confidentiality

Completeness

Currency

Volume

Variety

Velocity

Integration

Brinkhues et al. (2014), Chae et al. (2014), Erevelles et al.

(2016), Gupta and George (2016), Kamioka and Tapanainen

(2014), Olszak (2014), Ren et al. (2016), Wamba et al.

(2015), Phillips-Wren et al. (2015) and Vidgen et al. (2017)

Infrastructure Connectivity

Compatibility

Modularity

Agility

Large-scale,

unstructured

databases

Cloud services

Reliability

Adaptability

Integration

Accessibility

Response

Brinkhues et al. (2014), Akter et al. (2016a), Erevelles et al.

(2016), Garmaki et al. (2016), Gupta and George (2016),

Kamioka and Tapanainen (2014), Olszak (2014) and Ren

et al. (2016)

Software

and IS

Integrated analytics

systems

Security and risk-

management service

Data-management

service

Open software

Reporting and

visualization

systems

Bekmamedova and Shanks (2014), Erevelles et al. (2016),

Garmaki et al. (2016), Gupta and George (2016) and Olszak

(2014)

564 P. Mikalef et al.

123

initiatives, as well as to manage different types and categories of data related to

analytics. As such, BDA governance represents the rules and controls that

participants must comply with when performing relative tasks. The emphasis on big

data and information governance is largely attributed to the strategic importance

that it holds in contemporary enterprises. Similarly, numerous researchers note the

importance of establishing governance schemes for big data (Cao and Duan 2014b;

Garmaki et al. 2016), while others recognize it as one of the main reasons why firms

fail to leverage their data effectively (Posavec and Krajnovic 2016). A recurring

finding that concerns the effectiveness of governance, however, is that it must

follow a top-down approach, requiring commitment to data-driven decisions from

top management (Vidgen et al. 2017).

An additional intangible resource that is particularly important in driving the

adoption of big data and the development of firm-wide BDA capability is a data-

driven culture (Cao and Duan 2014a). The notion of a data-driven culture is adopted

from organizational culture, which is a highly complex concept to understand and

describe (Gupta and George 2016). In firms engaging in big data projects, a data-

driven culture has been noted as an important factor in determining their overall

success and continuation (LaValle et al. 2011). The main argument for the

importance of a data-driven culture is that although many companies implement big

data projects, the vast majority rely not on the information extracted from data

analysis, but rather on managerial experience or intuition (Provost and Fawcett

2013). This requires that organizational members, including top-level executives,

middle-level managers, and even lower-level employees, make decisions based on

information extracted from data (Gupta and George 2016). Aspects that contribute

towards a data-driven culture include prioritizing BDA investments, top manage-

ment support in formulating decisions based on BDA, and a fact-based operating

culture (Lamba and Dubey 2015; Olszak 2014; Kamioka and Tapanainen 2014). It

is then critical that the importance of data-driven decision making is imprinted in

the organization through specific practices (Mikalef et al. 2017). In fact, it is

frequently cited that organizations that are successful with BDA are those that have

managed to instill the importance of data-driven insights to a breadth of

departments. This alleviates siloed units and enables a greater depth and richness

of data to be analyzed, while also allowing for dispersed organizational units to

work collaboratively towards analytics-generated insights (Mikalef et al. 2017)

(Table 7).

4.3.3 Human skills and knowledge

The capacity to utilize big data technologies and tools such as those mentioned

above, and to make strategic decisions based on outcomes, is highly dependent on

the skills and knowledge of the human resources. These can be further divided into

technical knowledge (e.g. database management, data retrieval, programming

knowledge such as MapReduce, and cloud service management), business

knowledge (e.g. decision making heavily routed within the firm, strategic foresight

for big data deployments, and application of insights extracted), relational

knowledge (e.g. communication and collaboration skills between employees of

Big data analytics capabilities: a systematic literature… 565

123

different backgrounds), and business analytics knowledge (e.g. mathematical

modeling, simulation and scenario development, and interactive data visualization).

In a highly influential article, Davenport and Patil (2012) address the important role

that the emerging job of the data scientist will have in the context of big data. While

one of the most critical aspects of data science is the ability to think analytically

about data, such skill is not only important for the data scientist, but for employees

throughout the organization (Prescott 2014). In effect, the data scientist is capable of

understanding business problems and utilizing relevant data sources to generate

insights based on models and visualization tools.

Recognizing the importance of the data scientist in contemporary firms, some

studies have even proposed methods to redesign IS curriculums (Jacobi et al. 2014).

This lack of personnel with the appropriate skills is also noted in numerous studies,

and constitutes a major constraint in realizing the full potential of these technologies

(Tambe 2014). A report by McKinsey Global Institute concludes that by 2018 there

will be a shortage of talent necessary for organizations to take advantage of big data,

with an estimate of 140,000–190,000 positions for which no trained personnel will

be available (Domingue et al. 2014). While it is still vague and unclear what the

critical skills that a data scientist must have are, some definitions help to clarify this.

According to Mohanty et al. (2013), data scientists are practitioners of the analytics

models solving business problems. They incorporate advanced analytical

approaches using sophisticated analytics and data visualization tools to discover

patterns in data. The core attributes of the data scientist have been distilled to having

Table 7 Intangible resources

Resource

type

Characteristics Authors and date

Intangible

Governance Control

Coordination and

monitoring

Business–IT alignment

Decision-rights

appropriation

Big data solution

assessment and

validation

Business vision and

planning

Policy and rule structures

Olszak (2014), Garmaki et al. (2016), Akter et al. (2016a),

Cao and Duan (2014b), Erevelles et al. (2016), Tallon et al.

(2013), Espinosa and Armour (2016), Phillips-Wren et al.

(2015), Mikalef et al. (2017), and Vidgen et al. (2017)

Data-driven

culture

Prioritizing BA

investments

Top management support

Fact-based and learning

culture

Davenport et al. (2001), Davenport (2013), Kiron et al.

(2014), Kiron and Shockley (2011), Gupta and George

(2016), Lamba and Dubey (2015), Olszak (2014),

Kamioka and Tapanainen (2014) and Mikalef et al. (2017)

566 P. Mikalef et al.

123

entrepreneurial and business domain knowledge, computer science skills, effective

communication skills, ability to create valuable and actionable insights, inquisi-

tiveness and curiosity, and knowledge of statistics and modeling (Chatfield et al.

2014).

Nevertheless, while the center of attention has been placed on the data scientist

primarily due to the novelty of the role, other skills and knowledge sets are

necessary in employees of firms engaging in BDA. Of particular relevance are

technical skills such as those of the big data engineers, who are able to acquire store,

cleanse, and code data from multiple sources and of various formats (Mikalef et al.

2017). Similarly, big data architects accommodate such technical knowledge by

being responsible for developing blueprint plans of the data sources, as well as the

appropriate technologies to leverage their potential. Due to the fusion of business

and IT departments in BDA firms, the importance of a liaison person has emerged;

that is, a person capable of bridging the siloed departments and making them work

collaboratively (Akter et al. 2016a). The necessary skills for such employees include

a good understanding of what each department/unit is doing, as well as an ability to

communicate with each and build fused teams (Mikalef et al. 2017). Finally, having

a good understanding of the goals and directions of the firm, as well as knowing

how to measure and improve critical key performance indicators (KPIs), is of

paramount importance since, in most cases, BDA are grounded on an existing

problem. Therefore, an ability to identify this problem and improve by means of big

data-generated insight is an important aspect of the knowledge that business

executives and data analysts should have (Gupta and George 2016) (Table 8).

4.4 Areas of big data analytics

Apart from the core resources that are required to develop a BDA capability, several

studies have examined the areas in which big data initiatives can be leveraged, as

well as overall firm performance gains (Akter and Wamba 2016). The main premise

is that although a BDA capability comprises mostly similar aspects that need to be

taken into account independently of context, the way in which this capability is

applied has considerable diversity. For instance, several studies note that companies

that belong in the media and news industry apply their BDA capabilities towards

personalizing content toward their customers and delivering tailored-made news and

suggestions, while in the oil and gas industry there are several applications geared

towards risk assessment and maintenance (Mikalef et al. 2017). Furthermore,

considerable heterogeneity of BDA applications has been observed within

industries, which can potentially lead to differentiated business value outcomes

(Akter et al. 2016a). To this end, several recent studies attempt to examine the

influence of BDA capabilities on enabling various forms of organizational

capabilities (Xu et al. 2016; Pappas et al. 2016; Wamba et al. 2017).

These studies show that a BDA capability can be directed towards strengthening

both operational (Chae et al. 2014) and dynamic (Erevelles et al. 2016) capabilities

of a firm. Wamba et al. (2015) demonstrate that the types of value creation for big

data initiatives can be divided into creating transparency (Meredith et al. 2012;

Bärenfänger et al. 2014), enabling experimentation to discover needs and improve

Big data analytics capabilities: a systematic literature… 567

123

T a b le

8 H u m a n re so u rc e s

R e so u rc e ty p e

C h a ra c te ri st ic s

A u th o rs

a n d d a te

H u m a n sk il ls a n d k n o w le d g e

T e c h n ic a l k n o w le d g e

P ro g ra m m in g

T e c h n ic a l in fr a st ru c tu re

m a n a g e m e n t

M a p R e d u c e

U n st ru c tu re d d a ta

m a n a g e m e n t

D a ta

c o ll e c ti o n /i n te g ra ti o n

A k te r e t a l. (2 0 1 6 a ), E lb a sh ir e t a l. (2 0 1 3 ),

G a rm

a k i e t a l. (2 0 1 6 ), G u p ta

a n d G e o rg e

(2 0 1 6 ), K a m io k a a n d T a p a n a in e n (2 0 1 4 ),

O ls z a k , (2 0 1 4 ), a n d M ik a le f e t a l. (2 0 1 7 )

B u si n e ss

k n o w le d g e

B u si n e ss

st ra te g y

K P Is

B u si n e ss

p ro c e ss e s

C h a n g e m a n a g e m e n t

A k te r e t a l. (2 0 1 6 a ), E re v e ll e s e t a l. (2 0 1 6 ),

E lb a sh ir e t a l. (2 0 1 3 ), G u p ta

a n d G e o rg e

(2 0 1 6 ), O ls z a k (2 0 1 4 ), G a rm

a k i e t a l.

(2 0 1 6 ), a n d O ls z a k (2 0 1 4 )

R e la ti o n a l k n o w le d g e

C o m m u n ic a ti o n sk il ls

T e a m

b u il d in g

A k te r e t a l. (2 0 1 6 a ), G a rm

a k i e t a l. (2 0 1 6 )

a n d M ik a le f e t a l. (2 0 1 7 )

B u si n e ss

a n a ly ti c s

S ta ti st ic a l a n a ly si s

F o re c a st in g

Q u e ry

a n d a n a ly si s

P re d ic ti v e m o d e li n g

O p ti m iz a ti o n

M o d e l m a n a g e m e n t

S im

u la ti o n a n d sc e n a ri o d e v e lo p m e n t

B u si n e ss

re p o rt in g /K P Is /d a sh b o a rd s

W e b a n a ly ti c s

S o c ia l m e d ia

a n a ly ti c s

In te ra c ti v e d a ta

v is u a li z a ti o n

T e x t, a u d io , v id e o a n a ly ti c s

D a ta

a n d te x t m in in g

D a v e n p o rt e t a l. (2 0 0 1 ), L a V a ll e e t a l.

(2 0 1 1 ), C h e n e t a l. (2 0 1 6 ), C a o a n d D u a n

(2 0 1 4 b ), C h a e e t a l. (2 0 1 4 ), E re v e ll e s e t a l.

(2 0 1 6 ), G a lb ra it h (2 0 1 4 ), G a rm

a k i e t a l.

(2 0 1 6 ), L a m b a a n d D u b e y (2 0 1 5 ), O ls z a k

(2 0 1 4 ) a n d V id g e n e t a l. (2 0 1 7 )

568 P. Mikalef et al.

123

performance (Brinkhues et al. 2014; Bärenfänger et al. 2014), segmenting

populations (Kowalczyk and Buxmann 2014), enhancing or replacing human

decision making (Meredith et al. 2012; Cao et al. 2015; Brinkhues et al. 2014;

Bärenfänger et al. 2014), and innovating new business models, products, and

services (Jelinek and Bergey 2013). Hence, it is important when considering the

potential of BDA capabilities to take into account their area of application. Utilizing

BDA to improve process efficiency is most likely to have significantly less impact

on a firm’s competitive position compared to utilizing them to detect new customer

segments or come up with new business models. Nevertheless, even if BDA

capabilities are leveraged in core strategic areas, their value is likely to be

dependent and contingent upon multiple factors, which will be further elaborated in

the subsequent section.

5 Discussion

Despite the hype that surrounds big data, the business potential and mechanisms

through which it results in competitive performance gains have remained

largely underexplored to date in empirical studies. By conducting a systematic

literature review and documenting what is known to date, it is possible to

identify prominent themes of research that are of high relevance. We do so by

defining six thematic areas of research, as depicted in the research framework in

Fig. 2, and provide some suggestions on how scholars could approach these

problems.

5.1 Theme 1: resource orchestration of big data analytics

While considerable effort has been made to define the building blocks of a firm’s

BDA capability, little is known so far about the processes and structures

necessary to orchestrate these resources into a firm-wide capability. In other

words, literature has been very detailed on the resource-picking aspect of BDA,

but less so on the activities that need to be put into place to develop the

capability. Prior literature on IT–business value has shown that competence in

orchestrating and managing such resources is a prerequisite to developing the

capacity so as to leverage these resources strategically (Cragg et al. 2011; Wang

et al. 2012). According to the framework of resource orchestration, structuring

resources towards the building of a capability consists of acquiring, accumu-

lating, and divesting resources (Sirmon et al. 2011). Therefore, it is important

for researchers to examine the capability-building process, since it is likely that

firms with similar resources will exert highly varied levels of BDA capabilities.

Similarly, firms with same levels of BDA capabilities may develop them in

dissimilar ways, since their value may be contingent upon several internal and

external factors (Mikalef et al. 2015).

Big data analytics capabilities: a systematic literature… 569

123

5.2 Theme 2: decoupling big data analytics capability from big data- enabled capabilities

Although it is clear that a BDA capability refers to a firm’s proficiency in

orchestrating and managing its big data-related resources, it is important to

differentiate between the firm’s capacity to utilize its BDA capability towards

insight generation of organizational-level capabilities. As such, a firm can have

developed a strong BDA capability but only utilize it towards a specific type of

operational capability (e.g. marketing). Therefore, the assumption that a BDA

capability will enhance several organizational capabilities simultaneously is

misleading. It is highly likely that industry and other contextual factors influence

firms’ decisions to leverage their BDA capabilities in order to gain insight in

relation to different organizational capabilities. Again, the means by which they

choose to leverage their BDA capabilities could differ significantly and could

possibly result in variation in terms of performance gains. Consequently, it is critical

to gain a deeper understanding of the specifics of each capability, since the high-

level abstractions noted in strategic management literature may conceal the reality

of how the capability is leveraged by means of BDA (Mikalef and Pateli 2016).

5.3 Theme 3: bounded rationality of big data analytics

One of the assumptions of BDA, which is not discussed very frequently, has to do

with the limitations of insights that can be derived from big data itself. While BDA

may enable a more data-driven decision-making approach, the types of insights that

can be derived are bounded by the amount, variety, and quality of available data.

Consequently, a frequently observed phenomenon is that firms try to access a

diverse set of data from open sources. In some cases, it is even noted that firms are

forming strategic alliances in which the exchanged resources are datasets and

customer information (Ransbotham and Kiron 2017). Hence, one of the conditions

that should be taken into account when examining the strategic potential of big data

Fig. 2 Big data analytics research framework

570 P. Mikalef et al.

123

is the availability of data. In a similar vein, the different forms of collaborative

agreements with regards to data exchange and their resulting business value are

posited to be an area of increased interest. It is highly probable that the boundaries

of the insight that firms can develop, and, subsequently, the types of competitive

actions that they launch, are restricted by the availability of data.

5.4 Theme 4: turning big data analytics insights into action

To realize the value of BDA, it is necessary not only to put them into action in the

generation of data-driven information for specific organizational capabilities, but

also to take action to harness the insights. While some studies assume that

leveraging BDA capabilities is sufficient to provide business value, it is important to

examine the mechanisms of inertia that act in inhibiting their value. In a recent

literature review, Besson and Rowe (2012) indicate that when it comes to

organizational transformation there are five main types of inertia that hinder the

value of IS. These include negative psychology inertia, socio-cognitive inertia,

socio-technical inertia, economic inertia, and political inertia. The aspects of inertia

can work at multiple stages within the development of a BDA capability, and also

after they have resulted in insights. Nevertheless, turning BDA into action, and

subsequently business value, may also be dependent upon the external environment.

In highly dynamic and turbulent environments, the value of insight may be

diminished by scarce resources or competitors launching competitive actions in

short cycles. Hence, it is important to examine the confluence of the competitive

environment when considering the business value of BDA-derived insight.

5.5 Theme 5: trust of top managers in big data analytics insights

One specific form of distrust in the value and accuracy of BDA can be detected on

the top management level. While managers may be positive about investing in BDA

capabilities, when it comes to decision making they may feel that their intuition is

more accurate than the analysis performed on big datasets. This phenomenon has

been studied under the prism of dual process theory in the organizational context

(Hodgkinson and Healey 2011). The main premise is that managers’ emotional and

cognitive responses may override the insights of BDA, thereby reducing their

potential value. This dichotomy between reflexive systems inherent in top

managers, such as implicit stereotyping and automatic categorization, and reflective

systems, such as those present in BDA that allow logical reasoning and planning,

are argued to be important when measuring the impact of such investments.

5.6 Theme 6: business value measurement

The issue of determining the right measurement indices by which to assess the value

of IT is prevalent throughout IS research (Schryen 2013). Similarly, the hype

surrounding BDA may cause managers and academics to overestimate the potential

business value of BDA, or develop biased performance metrics to benchmark BDA

effectiveness. It is therefore important to construct specific performance measures

Big data analytics capabilities: a systematic literature… 571

123

depending on a number of contextual factors, as well as on the area in which BDA

are deployed. Furthermore, it is critical to identify metrics that take into account the

competitive landscape, since in highly uncertain and dynamic markets the value of

BDA may be reduced due to competitors following similar strategies or scarce

resources inhibiting response formation. As such, we highly encourage researchers

to examine a multitude of not only objective, but also subjective, value measures.

6 Conclusion

An ever-increasing number of companies are attempting to use big data and

business analytics in order to analyze available data and aid decision making. For

these companies, it is important to leverage the full potential that big data and

business analytics can offer with the aim of gaining competitive advantage.

Nevertheless, since big data and business analytics are a relatively new technolog-

ical and business paradigm, there is little research on how to effectively manage

them and leverage them. While early studies have shown the benefits of using big

data in different contexts, there is a lack of theoretically driven research on how to

utilize these solutions in order to gain a competitive advantage. This work identifies

the need to examine BDA through a holistic lens. We thereby focus on summarizing

what we already know and pinpoint themes on which we still have limited empirical

understanding.

To this end, this study proposes a research framework that is based on prior

literature in the area of IT–business value, as well as on concepts from strategic

management and management IS literature. The framework provides a reference for

the broader implementation of big data in the business context. While the elements

present in the research framework are on a high level and can be interpreted as quite

abstract, they are purposefully described in such a manner that they can be adapted

depending on the company at hand. This poses a novel perspective on big data

literature, since the vast majority focuses on tools, technical methods (e.g. data

mining, textual analysis, and sentiment analysis), network analytics, and infras-

tructure. Hence, the proposed framework contributes to big data and business

strategy literature by covering the aforementioned gap. It is more important for

managers and decision makers to learn how to implement big data and business

analytics in their competitive strategies than to simply perform raw data analysis on

large datasets without a clear direction of where this contributes to the overall

business strategy.

Furthermore, this study argues that the main source of competitive edge,

especially in highly dynamic and turbulent environments, will stem from companies

being able to reinforce their organizational capabilities through targeted use of big

data and business analytics. This of course does not lessen the importance of big

data resources and capabilities, since their availability and VRIN characteristics can

determine the strength of the associated insights that augment organizational

capabilities (Bowman and Ambrosini 2003; Meyer-Waarden 2016). The concepts

used in the proposed framework may help managers to better understand, plan, and

organize the process of implementing BDA within a business strategy. In addition,

572 P. Mikalef et al.

123

the framework can be used as a roadmap for gradually maturing the BDA capability

of a firm and deriving increased value from such initiatives.

This paper offers a theoretical framework on how to increase business value and

competitive performance through targeted application of big data. Future studies

should empirically test and evaluate this framework using surveys, interviews,

observation, focus groups with experts (e.g. managers, decision makers) and with

customers, as well as case studies from the industry. In addition, both qualitative

and quantitative methods of data collection should be employed. For each different

type of data, more than one method of analysis should be used (e.g. structural

equation modeling, qualitative comparative analysis). The main argument of this

systematic literature review is that the value of big data does not solely rely on the

technologies used to enable them, but is apparent through a large nexus of

associations that are eventually infused with organizational capabilities. Strength-

ening these capabilities by virtue of big data is what will lead to competitive

performance gains, and is contingent upon multiple internal and external factors.

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