The Role of Big Data Analytics in Corporate Decision-making

Darlan Arruda and Nazim H. Madhavji

Department of Computer Science, The University of Western Ontario, London, Canada

Keywords: Big Data, Data Analytics, Corporate Decision-making, Systematic Literature Review (SLR).

Abstract: Big Data Analytics results can play a major role in corporate decision-making allowing companies to achieve

competitive advantage and make improved decisions. This paper describes a systematic literature review

(SLR) on the role of the results of Big Data Analytics in corporate decisions. Initially, 1652 papers were

identified from various sources. Filtering through the 5-step process, 20 relevant studies were selected for

analysis in this SLR. The findings of this study are fourfold in the area of: (a) usage of the results of Big Data

Analytics in corporate decision-making; (b) the types of business functions where analytics has been fruitfully

utilised; (c) the impact of analytics on decision-making; and (d) the impediments to using Big Data Analytics

in corporate decision-making. Also, on the management front, two important issues identified are: (i) aligning

data-driven decision-making with business strategy and (ii) collaboration across business functions for

effective flow of Big Data and information. On the technical front, big data present some challenges due to

the lack of tools to process such properties of Big Data as variety, veracity, volume, and velocity. We observe

from this analysis that, thus far, little scientific research has focused on understanding how to address the

analytics results in corporate decision-making. This paper ends with some recommendations for further

research in this area.

1 INTRODUCTION

As stated by Johnson (2012), 15 out of 17 industry

sectors in the US have more data stored per company

than the US Library Congress, which alone collected

235 terabytes of data in April 2011. Big Data

Analytics examines a broad range of sources (Reddi,

2013), such as social media, public web, archives,

docs, business apps, and others. These sources of data

are used in analytics to address business aims: e.g.,

cost reduction, improving sales strategy, pricing,

development of new products and services, improved

risk management, and others.

Example decisions rooted in analytics results are

attributed to corporate-driven questions, such as: Is

there a need for certain new products and services in

specific geographical areas? How should we price our

products and services? What alternatives to consider?

(Davenport, 2014) How much inventory should be

held in the warehouse? What kinds of offers should

be given to customers with different profiles?

(Davenport, 2013).

Koscielniak and Puto (2015), noted that the use of

information for making decisions and the way in

which it is organized is becoming important. This

view is also supported by results of a survey

conducted by McKinsey in 2011 (McKinsey, 2011)

which suggests that the analysis of Big Data is

potentially becoming a key basis for competition,

productivity and innovation. The use of the results of

Big Data Analytics in business enables companies to

achieve competitive advantage (Kościelniak and

Puto, 2015). However, using traditional data to

support decision-making is not new: e.g., determining

favourite product features based on user preferences

(Ziora, 2015) the data for which are gathered during

the product evaluation phase to make decisions

concerning the new product (Barbacioru, 2014).

However, Big Data differs from traditional data,

principally in several accepted characteristics:

Volume, Velocity, Variety and Veracity (Chen at al,

2014). More recently, Value is also added to these

characteristics (Wegener and Sinha, 2013). Thus, new

and more in-depth information can potentially be

derived from Big Data for use in corporate decision-

making (Vahn, 2014). In addition, a survey conducted

by Capgemini in 2012 (Capgemini, 2012) shows that

the participants who have applied data analytics have

seen approx. 26% improvement in business

performance and they expect to improve this number

Arruda, D. and Madhavji, N.

The Role of Big Data Analytics in Corporate Decision-making.

DOI: 10.5220/0006402300280037

In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 28-37

ISBN: 978-989-758-255-4

to 41% in the foreseeable future. Furthermore, in a

study conducted by CSC in 2014 (Infochimps, 2013)

with more than 300 IT employees revealed that

approx. 52% of the respondents were involved in a

Big Data project, and approximately 59% of them

rated Big Data Analytics as top 5 priority items for

their company.

Yet, IDG Enterprise study presented by Columbus

(2015) reveals that 36% of the participated companies

have plans to increase their budgets for data-driven

initiatives within the organization. As the top priority,

61% of the respondents stated that the main goal in

investing in data-driven initiatives within the

organization is to improve the quality of the decision-

making as they considered Big Data Analytics as an

important tool to accelerate and gain important

business insight and value from data.

From aforementioned studies, it is clear that Big

Data Analytics and corporate decision-making are

fermenting in industry while no “tangible body of

knowledge” is readily visible in the scientific

literature. This situation motivated us to conduct a

systematic literature review (SLR) in order to obtain

some insight. We ask four key questions under the

general banner of the “role of Big Data Analytics in

corporate decision-making”, represented by the

following core points:

 RQ1 – use of Analytics results in decision-making;

 RQ2 – the business functions involved;

 RQ3 – impact of Analytics on aspects of decision-

making process; and

 RQ4 – impediments to using Analytics in

decision-making.

The key findings relate to the following:

 RQ1: Approaches on how to use the results in

decision making;

 RQ2: Different business functions where the Big

Data Analytics results can be used;

 RQ3: the aspects of decision-making process

affected by Big Data Analytics; and

 RQ4: (i) Difficult in aligning data-driven decision-

making with business strategy and (ii)

collaboration across business functions.

Collectively, these findings add tangibly to the

current, meagre knowledge base on the role of Big

Data Analytics in corporate decision-making. The

paper also discusses the implications of these findings

on practice and research. Section 2 discusses research

methodology; Section 3 describes the results and

discussion; Section 4 describes limitations and threats;

and Section 5 concludes the paper.

2 METHODOLOGY

This section describes the research methodology used

in this study.

2.1 Systematic Literature Review

A systematic literature review (SLR) is a way of

identifying, evaluating and interpreting research

relevant to a particular research question, or topic

area, or phenomenon of interest, using a revised

research method that is reliable, accurate and

facilitates auditing (Kitchenham, 2004; Mafra and

Travassos, 2006). There are several reasons for

undertaking a SLR, for example (Kitchenham and

Charter, 2007):

 To summarise the existing evidence concerning a

specific area;

 To identify any gaps in current research in order

to suggest areas for further investigation;

 To provide a framework/background in order to

appropriately position new research activities;

 To examine the extent to which empirical

evidence supports/contradicts theoretical

hypotheses, or even to help yield new hypotheses.

In this paper, we followed the guidelines suggested

by Kitchenham and Charter (2007).

2.2 Research Questions

We ask following research questions. Collectively,

they address the role of Big Data Analytics in

corporate decision-making:

[RQ1] How are the results of Big Data Analytics

used by management in corporate decision-

making? This question is important for

characterizing corporate decision making with

respect to Big Data usage.

[RQ2] In which business functions (e.g.,

marketing, financial, manufacturing, project

management, etc.) are the Big Data Analytics

results used? This question helps in characterising

business functions and identifying others which could

possibly take advantage of Big Data Analytics.

[RQ3] Which aspects of the decision-making

process can be affected by Big Data Analytics?

Brown-Liburd et al., (2015) mention that the

decision-making process is composed of elements

such as accountability, outcome evaluation, the

number of people involved, and the quality of the

analysis. To this, we add some other elements, such

as information (e.g., inputs, contextual, constraints,

timing, deadline, know-how, etc.) on hand to be able

The Role of Big Data Analytics in Corporate Decision-making

to make the decision; (ii) authority to make the

decision; (iii) consequences and impact of making the

decision and executing it; etc. So, which elements of

the decision making process are affected by the use of

Big Data Analytics.

[RQ4] What are the impediments to using Big

Data Analytics in corporate decision-making?

Understanding the barriers posed by the use of Big

Data Analytics for decision making can help in

creating new technologies and work processes.

2.3 Search Strategy

This section describes the strategy used to conduct the

search for primary studies. We used both automatic

(electronic databases) and manual searches (business

journals and conferences proceedings).

2.3.1 Search Terms

In order to ensure that the literature review adheres to

the topic of Big Data Analytics and Decision-Making,

we limited our search string to the most relevant terms

(e.g., Big Data, Big Data Analytics, Corporate

Decision Making, corporate Decision, Decision

Making, Decision Making process, Decision-making

model, business functions, decision) that we extracted

from the defined research questions.

We then performed various tests using the

identified terms. Approximately five versions of the

search string were generated in order to finalise it.

The search strings composed by the following terms:

(Corporate Decision-Making, Corporate Decision,

Decision, Business Functions) returned less relevant

results as these terms are commonly used in the

business literature (not related to Big Data Analytics).

In order to restrict our research to the most

relevant results, we used the following terms and

logical operators: ("Big Data" OR "Big Data

Analytics") AND ("Decision-making Model" OR

"Decision-making Process" OR "Decision-

making")

Despite having the rationale for using the search

string above, the terms in it could have failed to

identify certain papers (e.g., “large-scale complex

systems”) that do not use our search terms. This is

thus a threat related to this study (see section 4) the

extent of which is not knowable without further

investigation.

2.3.2 Resources

The used resources are divided into: electronic

database, business and management, and software

engineering scientific journals and companies’

technical reports, also known as white papers. Details

regarding the used resources are provided below.

Electronic Databases: ACM Digital Library and

Science Direct and Business Source Complete.

Scientific Journals: Journal of Organizational Design,

Journal of Decision Systems, Software: Practice and

Experience, Strategy & Leadership Journal, Business

& Information Systems Engineering Journal, Journal

of Big Data and Harvard Business Review.

Others: Technical Reports published by well-

known companies such as IBM, McKinsey and

Capgemini.

Regarding the automatic searches, the search was

done by covering the meta-data in the case of the

Business Complete Source, a very comprehensive

scientific database in business, and full-text collection

(meta-data and text) of the literature in the case of

both ACM Digital Library and Science Direct. For

electronic databases that index different areas of

knowledge such as Science Direct, only results within

the fields of Computer, Business, Management and

Account, and Decision Sciences were taken into

account.

2.4 Selection Criteria

This section describes the inclusion and exclusion

criteria, the selection process as well as the quality

assessment process used to guarantee that only

relevant and significant literature results were

accepted for analysis in the SLR.

2.4.1 Inclusion Criteria

(IC1) Studies should be published between January

2005 and February 2016 as big data may not have

been known much earlier than 2005.

(IC2) Studies must be available in full version.

(IC3) Studies must be written in English.

(IC4) For duplicated works, the most complete one

was selected.

(IC5) Research was related to Big Data in a

managerial context (the focus of this SLR).

2.4.2 Exclusion Criteria

(EC1) The papers that did not meet the inclusion

criteria were excluded.

(EC2) Books, dissertations and theses (in part,

because of time constraints in going through this

voluminous literature and, in part, some of this work

would be expected in research publication form).

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

2.4.3 The Selection Process

The selection process is comprised of five steps

adapted from Biochini et al., (2005) and Kitchenham

and Charters (2007).

Step 1 - An automatic search was performed. The

results were initially assessed by their title and

abstract. The studies considered relevant to the

context of the research were selected and the first list

of selected studies was created.

Step 2 - A manual search was performed in

business and management journals as well as

conferences proceedings. The results were initially

assessed by their title and abstract. The studies

considered relevant to the context of the research

were selected and the second list of selected studies

was created. As commented by Kitchenham and

Charters (2007), initial electronic searches result in a

large number of irrelevant papers. Following this, the

irrelevant results were excluded from further analysis.

Step 3 - In this phase the two lists were merged

into a single one.

Step 4 - In this step the selected studies were

assessed by reading their introduction and conclusion

sections. The studies considered relevant to the

context of the research were potentially selected for

the next step. At the end of this phase, a fourth list of

results was defined.

Step 5 - The last step enhanced the selection,

mainly because the studies were completely read,

analysed, and criticized in view of the contextual

relevance and filter provided by the previous steps. In

this phase the quality assessment process was applied

and then a final list with selected results was created.

2.5 Data Extraction

In order to better organize the selected papers

included into the SLR, a template composed of the

following attributes was used: study id, title, authors,

source, year of publication, full reference and the

designated questions they address as well as

important statements to help to answer the defined

questions.

2.6 Quality Assessment

According to Kitchenham and Charters (2007), in

addition to the general inclusion and exclusion

criteria, it is usually considered important to assess

the quality of the primary studies. Kitchenham also

states that the quality assessment is important: i) to

provide more detailed inclusion/exclusion criteria, ii)

to guide the interpretation of findings iii) to determine

the strength of inferences and iv) to guide

recommendations for further research.

For this study, the following quality assessment

questions were defined:

(QA1): Are the aims of the research clearly stated?

(QA2): Are the research results and applications

described in detail?

(QA3): Does the paper deal with the use of Big Data

Analytics in decision making?

Based on the quality assessment, only papers that

addressed the quality assessment questions and at

least one of the research questions defined in this

study were selected to be used into this SLR.

2.7 Descriptive Data and Analysis

During the first step (automatic search) a total of 1652

results were identified. After applying the inclusion

and exclusion criteria and reading the title and

abstract, only 50 studies were considered relevant.

Moreover, a total number of 23 papers were selected

during the second step (manual search) based on the

inclusion and exclusion criteria as well. A third list

was defined by merging the results from the two

previous lists, totalling 73 papers.

After reading the introduction and conclusion of

each article, 49 were considered relevant and selected

for the next step, which consist of reading the full

paper and applying the quality assessment process.

No scoring method was used in this step.

The studies that did not meet the quality

assessment criteria and did not address at least one of

the research questions defined were excluded from

this review (29 out of 49). Furthermore, the excluded

studies addressed points not related to the main goal

of this SLR (see section 1). These points included: (i)

analytics techniques and algorithms, (ii) analytics

framework, (iii) implementation of tools for value

discovery, (iv) business intelligence and analytics, (v)

process for starting to use Big Data in companies, and

(vi) issues and policies for using Big Data Analytics

in government, to name a few.

At the end of the selection process, 20 papers were

considered relevant and selected for this SLR. There

were 11 studies (55%) published in scientific

journals, 4 (20%) published in conference

proceedings and 5 (25%) white papers published by

well-known companies. Approximately, 25% (5

studies) pointed out information to answer RQ1. 45%

(9 studies) covered RQ2. The RQ3 was addressed by

25% (5 studies) and finally, the majority, around 65%

(13 studies) of the papers addressed RQ4.

Table 1 presents the distribution of the studies in

terms of the questions they address. The numerical

The Role of Big Data Analytics in Corporate Decision-making

distribution of papers by research question can be

seen in Fig. 1. The distribution of studies by year of

publication is shown in Table 2. As can be clearly

seen from this table, the Big Data centric results

(pertaining to the focus of this SLR) appears to

surface in the literature around 2011 when this

research area began to gain traction in the community.

Table 1: Selected Primary Studies.

ID Authors Addressed RQ

Kościelniak and Puto (2015). RQ4

Henry and Venkatraman

(215)

RQ3

Phillips-Wren and Hoskisson

(2015)

RQ2, Q4

Fan et al (2015) RQ2, Q4

Xu et al (2015) RQ2

Way and See (2015) RQ3, Q4

Ziora (2015) RQ2, Q3

Brown-Liburd et al., (2015) RQ2, Q4

Colas et al (2014) RQ4

S10

Schermann et al., (2014) RQ4

S11

Galbraith (2014) RQ1, RQ2, RQ4

S12

Davenport (2014) RQ1

S13

Lukiæ (2014) RQ1, RQ3, RQ4

S14

Economist Intelligent unit

(2013)

RQ2, RQ4

S15

Probst et al (2013) RQ1

S16

Davenport (2013) RQ1, Q2

S17

McAfee et al., (2012) RQ4

S18

Capgemini (2012) RQ4

S19

McKinsey(2011) RQ3

S20

Lavalle et al., (2011) RQ2, Q4

Figure 1: Numerical Distribution of Papers by Research

Question.

Table 2: Number of Studies by Year.

Year 2011 2012 2013 2014 2015

Number of Papers

2 2 3 5 8

3 RESULTS AND DISCUSSION

This section discusses the results obtained for each

research question defined in section 2, subsection 2.2.

3.1 RQ1: How Are the Results of Big

Data Analytics Used by

Management in Corporate

Decision-making?

The SLR analysis demonstrated that despite the

recognition of the need to use and understand Big

Data in corporate decisions (Capgemini, 2012), there

has been little scientific research (5 papers since

2013) aimed at understanding how to use the

analytics results in the decision-making process of

organizations. Fourteen of 20 identified papers

address the advantages and benefits of using Big Data

Analytics to support decision-making, but not on an

understanding on how to use the results in decision-

making.

Galbraith (2014) states that the analytics results,

during the analysis phase, should be done in real-time

by fast-response teams. This can affect decision-

making. He suggests that the analytics results should

be used such that the team has to discuss the insights

and decide on responses based on real-time action.

However, an important factor to consider is how to

organize these activities so as to facilitate real time

decision-making. One recommendation is that

companies should have multi-functional teams that

are in constant contact with the data generated from

different sources to respond to real-time inputs. The

data from these different sources are then processed

by analytics tools and the results used to make real-

time decisions. This process allows companies to

influence the outcome and prevent bad outcomes

before they happen.

As an example on how big data analytics

facilitates real-time decisions in managing supply

chains is provided by Galbraith (2014): “At company

A, there are specially designed rooms with video

screens on the walls and computer access to various

databases. The rooms are designed to foster real-time,

cross-functional decision making. So when a paper

machine’s embedded sensors at the Pampers’ plant in

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

a specific location indicate that it requires

maintenance, a plant shutdown is scheduled. If it

looks like the machine will be down for a while, then

the decision is made to supply company A from the

Albany, Georgia plant. The analytics capabilities are

used to determine the best way to reroute trucks and

still meet other delivery commitments to customers.

In the study conducted by Probst (2013), within

various information technology companies that

develop analytics tools, an important aspect on how

to deal with the analytics results were pointed out:

analyse the data and generate fast and intuitive

reports, the called user friendly reports, which aims to

empower the decision-making and provide

companies with competitive advantages. The

availability of easy to read reports can help

companies to improve their decision-making

capability as it contributes with clear and important

information making it easier for the decision-maker

to understand.

Davenport (2013) presents a 6-step approach to

decision making in the context of Big Data. The steps

are:

1. Problem Recognition;

2. Review Previous Findings;

3. Model the solution and select the variables;

4. Collect the data;

5. Analyse the data; and

6. Present and act on the results.

In this approach, for qualitative decision-making, the

focus is on the first, second and last steps of the

process which consist of: (1) frame the problem,

identify it and (2) understand how others might have

solved it in the past and (6) present the Analytics

results and act on the results. The intermediate steps

focus on (3) modelling the solution, (4) collecting the

data and (5) analysing it.

For qualitative decision-making, following the 6

steps in order is important making necessary to

formulate detailed hypotheses, get primary and

secondary data on the hypothesized variables and

finally run statistical models to check the usefulness

of the data. Given the size of the big data, in his

approach, the Big Data Analytics results are used in a

way where they tell a story to decision makers and

stakeholders, and based on that story, the decision

will be made and actions will be taken upon it. In this

approach the judgement and expertise of the decision-

maker are also important as they are used for final

decision.

In order to use the results of the Big Data

Analytics in decision making, P&G (described by

Colas et al., 2014) developed an initiative called

“Decisions Cockpits” which can be defined as

dashboards that provide executives with visual

displays of information on business performance and

market trends. The idea is to provide a single source

of truth for the information across geographies and

business units. They can be customized and provide

real-time automated information notifications. In this

way, Big Data Analytics results helps to speed up

decision making and reduce time to market (Colas et

al., 2014).

3.2 RQ2: In Which Business Functions

Are the Big Data Analytics Results

Used?

In general, any business function can potentially use

Big Data Analytics to make informed decisions.

Examples are scattered in the literature. We identify

nine business functions in this SLR, and add

examples for each function in terms of how Big Data

Analytics can be used in decision-making.

Supply Chain (Lavalle et al., 2011; Phillips-

Wren and Hoskisson, 2015). In supply chain,

companies use big data to describe the efficiency of

its supply chain as well as to measure and monitor

supply chain risks making decisions about location,

product, etc. Questions such as What quality control

measures will be used? Can be easily addressed by

using Big Data Analytics.

Product Research and Development (Lavalle et

al., 2011; Xu et al., 2015; Ziora, 2015). Companies

can use Big Data to make new product decisions;

Speed up the product development process: e.g., How

can we improve the product design? What features

should the product have to meet the costumer’s

preferences?

Marketing Management (Davenport, 2013;

Galbraith, 2014; Phillips-Wren and Hoskisson,

2015). Marketing Decisions such as customer

opinions toward a product, service or company and

marketing promotions: e.g., how to define the product

marketing strategies? Where and when to release the

product? What kind of visual aids should we use in a

product campaign?

Sales and Productivity (Lavalle et al., 2011;

Economist Intelligence Unit, 2013; Davenport,

2013). Optimize sales resource assignments, define

sales strategy: e.g., Do our current sales staff need

training to increase sales? How effective is our

current sales strategy? What can we do to improve our

current sales strategy? How much should we invest in

training? Is there a way to lower that cost with a more

effective strategy?

The Role of Big Data Analytics in Corporate Decision-making

Operations/Manufacturing (Lavalle et al.,

2011; Economist Intelligence Unit, 2013).

Understanding variances that might be indicators of

quality issues. Decisions might include: e.g., How to

automate our operations? How to define future

strategy to enhance our day-to-day operation?

Human resources (Economist Intelligence Unit,

2013; Ziora, 2015). Identify characteristics of most

successful employees. Using predictive modelling to

understand the workforce. The decisions involve

improvements in the hiring and retention process. The

use of cross-functional information combined

promotion process, etc. Decisions involve process

improvements, which personal to hire, retain and fire

with the data in the human resources sector can be

used to create a bigger context. This can be used to

evaluate employees’ performance.

Risk Management (Lavalle et al., 2011). Have an

understanding of market risk, credit risk and

operational risk: e.g., what are the vulnerabilities of

our business? What actions should we take in order to

reduce the risks?

Investment Decisions/Financial Management

(The Economist, 2013). Have a better insight on how

to spend money and how to borrow money: e.g.,

which investment strategy to use in? What can we do

to best allocate capital to maximize its value? How

much to borrow?

Audit (Brown-Liburd and Lombardi, 2015).

Improving the efficiency of the audit procedures such

as analyse external data in the assessment of client

business risk, fraud risk, internal control.

Intuitively, it appears that practice is taking hold

more widely in terms of diversity of business

functions where Big Data Analytics results are being

used to make decisions than what might appear from

the SLR findings. Further empirical studies are

clearly needed to uncover more facts.

3.3 RQ3: Which Aspects of the

Decision-making Process Can Be

Affected by Big Data Analytics?

In traditional decision-making processes, decisions

involved human agents’ skills, experience and

judgement. In the Big Data context, operational

decisions are partly or wholly replaced by automated

algorithms as data-driven analysis is used instead of

intuition (Way and See, 2015; Henry and

Venkatraman, 2015).

This implies that any of the steps of a human

decision-making process (such as: Identify the

problem, gather the information, analyse the

situation, develop and evaluate alternatives, select the

preferred alternative and action upon the problem) are

eliminated or changed in some way, possibly

introducing new steps for the human agent. In this

context, Henry and Venkatraman (2015) state that

there is less reliance on subjective managerial inputs

due to the availability of real time insights from big

data in order to make quality data-driven decisions.

Finally, the effort, time and cost involved in

making a decision as well as to implement it may be

affected by the use of Big Data Analytics (Ziora,

2015).

3.4 RQ4: What Are the Impediments

to using Big Data Analytics Results

for Effective Decision-making?

While Big Data Analytics offers many advantages

and benefits to the users, it also brings challenges. In

a Big Data Analytics context, companies face several

impediments, both at the managerial and technical

levels. At the technical level, the Big Data paradigm

imposes numerous challenges due to possibly

inconsistent and unstructured data (The Economist,

2013 and Madhavji et al., 2015) for which tools are

still in the infancy (Capgemini, 2012).

At the management level, any organizational silos

can make the decision-making suboptimal if data is

not pooled together, across the silos, for the benefit of

the organization at large. Of course, silos are an

impediment to efficiently and effectively moving data

and information across the organisation contributing

to the potential for inconsistent reporting among the

possibly geographically distributed business

divisions (Capgemini, 2012). The silos can also lead

companies to the issue of not having timely or

relevant data across the business functions for

effective decision-making.

A study conducted by MIT’s Sloan School of

Business shows that companies that engage in data-

driven decision-making see a 5 to 6% increase in

output and productivity over the ones that do not

(Economist Intelligence Unit, 2013). However, the

difficulty in aligning data-driven decision-making

with the business strategy (McAfee et al., 2012;

Phillips-Wren and Hoskisson, 2015) is considered as

one of the most common problems companies face in

the Big Data Analytics era as it requires an

organizational culture change that involves

intellectual, technological and social alignment.

Also, leveraging Big Bata often means working

across functions such as IT, engineering, and finance,

for instance (Colas et al., 2014). However, where

cross-functional processes are of low maturity,

organisations are not able to take advantage of the

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

Table 3: Impediments in using Big Data Analytics Results in corporations.

Decision-Making Related Impediments References

 Aligning data-driven decision-making with business strategy and development of the

analytics strategy;

 Leadership of Analytics initiatives;

 Absence of clear business goals;

 Managerial behaviour/Culture (Resistance to change within the organization)

 Talent Management;

 Organizational Silos;

 Timely or relevant data across the company;

 Cost of specific tools;

 Centralization or Decentralization tendencies;

 Inconsistent reporting of information among business units, geographies and functional

operations;

 Difficulty in integrate their own data sources within the organization;

 Speed decision-making; and

 Time to analyse the datasets.

Kościelniak and Puto (2015);

Phillips-Wren and Hoskisson (2015);

Fan et al., (2015);

Way and See (2015);

Brown-Liburd et al., (2015);

Colas et al (2014);

Schermann et al., (2014);

Galbraith (2014);

Li (2014);

Economist Intelligent unit (2013);

McAfee et al (2012);

Capgemini (2012);

McKinsey(2011);

Lavalle et al., (2011)

power of Big Data. Table 3 lists the impediments to

using the results of Big Data Analytics in

corporations.

4 LIMITATIONS AND THREATS

We first discuss some limitations of this SLR study,

followed by some threats to validity.

One obvious limitation is that the intersection of

Big Data and corporate decisions is a relatively new

topic, with much focus on Big Data currently being

on data analytics. Thus, the difficulty in finding

timely and relevant studies could be considered a

limitation. Also, not all the electronic databases offer

the same kinds of features to use the defined search

strings. Thus, the search strings needed slight

adjustments so that they could be used successfully.

Concerning the threats to validity, some threats

were considered when analysing the results of the

SLR.

Construct Validity: Regarding the search string

used in this SLR (see subsection 2.3, sub-subsection

2.3.1), we used the terms considered most suitable in

order to make the string as comprehensive as possible

to capture the most relevant literature. We performed

various tests using the identified terms and,

approximately, five versions of the search string were

generated in order to decide upon the final version.

Thus, this threat was reasonably contained.

Conclusion Validity: All the conclusions drawn

are shown to have been rooted in specific core

sections of this paper – thus there is traceability.

However, there can be an argument that data

extraction from the selected papers could be biased.

This was addressed in two ways. One is the use of two

researchers (primary and secondary), and making

adjustments in interpretations as necessary. The

second is the use of the data extraction form (see

section 2, subsection 2.5) used to collect the

information needed to be used into this SLR. The

findings and outcomes of this study are based on

consensus and organised data.

Internal Validity: Specifically, the selection

process (see section 2, subsection 2.4, sub-subsection

2.4.3) in this SLR was conducted by two researchers

simultaneously and any possible disagreement was

discussed to reach a consensus from both researchers.

By doing this, we tried to minimise any threats to

internal validity. Regarding the manual search, it is

important to note that it was performed only in a

limited set of sources (e.g., magazines, journals, etc.).

The rest of the data sources were searched using

automatic search functions.

External Validity: This threat relates to whether

the findings are applicable in contexts other than

those presented in the study. An SLR study is not like

a case study or a scientific experiment where this

threat is of core importance because there are

environment scopes (e.g., projects) outside the

conducted study where one may wish to apply the

case study or experiment results. Since the scope of

the data (selected papers) in the SLR study is the

universe to be considered, this threat is not considered

relevant here.

However, despite care and collaborative effort, it

is difficult to guarantee that all relevant published

works and concepts related to the topic were included

into this SLR.

The Role of Big Data Analytics in Corporate Decision-making

5 CONCLUSIONS

A systematic literature was conducted in order to

answer fours research questions (RQ1-RQ4 – see

Section 2.2) in the intersection between Big Data

Analytics and decision-making process of

enterprises. This topic is relatively new and, to our

knowledge, no prior SLR studies on this topic have

been conducted. The selection process for choosing

the studies for analysis is composed of five steps (see

section 2.4). After applying the inclusion and

exclusion criteria, as well as the quality assessment

process, twenty studies were considered relevant and

selected to be used into this SLR (see Section 2/Table

1 for the list of studies selected).

This SLR study yields four main contributions:

(1) presentation of the state-of-the-art on the

intersection between Big Data Analytics and

decision-making process (see section 3, subsections

3.1 to 3.4); (2) the understanding on how the Big Data

Analytics results can contribute to the decision-

making process (see section 3, subsection 3.1); (3) the

identification of the business functions where Big

Data Analytics has been applied (see section 3,

subsection 3.2); and (4) the list of impediments for

using the analytics in decision-making (see section 3,

subsection 3.4 - Table 2). Collectively, these

contributions add to the emerging knowledge base on

Big Data Analytics and decision-making. Based on

this SLR study, we conclude that Big Data Analytics

results plays an important, multi-faceted, role in

corporate decision-making.

On the management front, two important issues

identified are: (i) aligning data-driven decision-

making with business strategy and (ii) collaboration

across business functions (See Section 3, subsection

3.4). Also, on the technical front, big data present

some challenges due to lack of tools to process

multiple properties of Big Data (such as variety,

veracity, volume, and velocity).

Finally, the SLR results also demonstrates that

there has been little scientific research aimed at

understanding how to use the analytics results in the

decision-making process of organizations. Most of

the relevant studies address the advantages and

benefits of using big data analytics to support the

decision-making process. However, an understanding

on how to use the results to make better decisions is

still in its infancy.

ACKNOWLEDGEMENTS

The current study was conducted with a grant support

to the first author from CNPq, The National Council

of Technological and Scientific Development –

Brazil. Process number 200218/2015-8. The authors

would like to thank Jie Lan for his help and effort in

the initial steps of this SLR.

REFERENCES

Barbacioru, I.C. 2014. “An Illustrative Example Of

Application Decision Making Process For Production

Consumer Goods.”

Biolchini, J; P. G. Mian, A. Candida, and C. Natali. 2005.

“Systematic Review in Software Engineering,”

Engineering, vol. 679, no. May, pp. 165–176.

Brown-Liburd, H., Issa, H. & Lombardi, D. 2015.

Behavioral implications of big data’s impact on audit

judgment and decision making and future research

directions. Accounting Horizons, 29(2), pp.451–468.

Capgemini, 2012. The deciding factor: big data & decision

making. Capgemini and the Ecomomist Web.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey.

Mobile Networks and Applications, 19(2), 171–209.

Colas, M. et al., 2014. Cracking the Data Conundrum : How

Successful Companies Make Big Data Operational. ,

pp.1–17. Available at: https://goo.gl/MlDmFT.

Columbus, Louis. Data Analytics Dominates Enterprises'

Spending Plans For 2015. 2015. Available

at:<http://www.forbes.com/sites/louiscolumbus/2015/

03/15/data-analytics-dominates-enterprises-spending-

plans-for-2015/#32355a293eb4. Last Access: August,

7 2016.

Davenport, T.H., 2013. Keep up with your quants. Harvard

Business Review, 91(7–8).

Davenport, T. H, 2014. How Strategists use “big data” to

Support Internal Business Decisions, Discovery and

Production. Strategy & Leadership, 42(4), pp.45–50.

Economist Intelligence Unit, 2013. The evolving role of

data in decision making.

Fan, S., Lau, R.Y.K. & Zhao, J.L., 2015. Demystifying Big

Data Analytics for Business Intelligence Through the

Lens of Marketing Mix. Big Data Research, 2(1),

pp.28–32. Available at:

http://dx.doi.org/10.1016/j.bdr.2015.02.006.

Feinleib, “What Managers Need to Know to Profit from the

Big Data” p. 236, 2014.

Galbraith, J.R., 2014. Organization Design Challenges

Resulting From Big Data. Journal of Organization

Design, 3(1), pp.2–13.

Gartner IT Glossary. Big Data. Available at

http://www.gartner.com/it-glossary/big-data/. Last

Access: 20-02-2016.

Glenys Vahn, G.-Y. (2014). Business Analytics in the Age

of Big Data. Business Strategy Review, 25(3), 8–9.

Henry, R. & Venkatraman, S., 2015. Big Data Analytics the

Next Big Learning Opportunity. Journal of

Management Information and Decision Sciences,

18(2), pp.17–30.

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

Infochimps, “Cios & Big Data: What Your It Team Wants

You To Know,” Infochimps Whitepaper, 2013.

Kościelniak, H. & Puto, A., 2015. BIG DATA in Decision

Making Processes of Enterprises. Procedia Computer

Science, 65(Iccmit), pp.1052–1058.

Johnson, J. E. . 2012. Big Data + Big Analytics + Big

Opportunity. Financlial Executive, (July/August), 50–

53.

Kitchenham, B. 2004. Procedures for performing

systematic reviews. Keele, UK, Keele University,

33(TR/SE-0401), 28.

Kitchenham, B., & Charters, S. 2007. Guidelines for

performing Systematic Literature reviews in Software

Engineering Version 2.3. Engineering, 45(4ve), 1051.

Lavalle, S. et al., 2011. Big Data , Analytics and the Path

From Insights to Value. MIT Slogan Management

Review, (52205).

Lukiæ, J. (2014) ‘The impact of information and

communication technology on relation-based

governance systems’, Information Technology for

Development, 11(2), pp. 105–122. doi:

10.1002/itdj.20010.

Madhavji, N. H., Miranskyy, A., & Kontogiannis, K.

(2015). Big Picture of Big Data Software Engineering,

11–14. International Workshop of Big Data Software

Engineering.

Mafra, S. N.; Travassos, G. H. Estudos Primários e

Secundários apoiando a busca por Evidência em

Engenharia de Software. 2006. Relatório Técnico RT-

ES-687/06,Programa de Engenharia de Sistemas e

Computação (PESC), COPPE/UFRJ.

McAfee, A. et al., 2012. Big Data: The Management

Revolution. Harvard Business Review, 90(10), pp.60–

68.

McKinsey & Company, 2011. Big data: The next frontier

for innovation, competition, and productivity.

McKinsey Global Institute, (June), p.156.

Phillips-Wren, G. & Hoskisson, A., 2015. An analytical

journey towards big data. Journal of Decision Systems,

125(June), pp.1–16.

Probst, L. et al., 2013. Big Data - Analytics & Decision

Making. European Commission, Case study.

Reddi Sashi. 2013. 4 ways Big Data Will Transform

Business. Available at:

http://www.csc.com/big_data/publications/89362/9647

7-4_ways_big_data_will_transform_business. Last

Access 21-02-2016.

Schermann, M. et al., 2014. An interdisciplinary

opportunity for information systems research. Business

and Information Systems Engineering, 6(5), pp.261–

266.

Wegener, R. and V. Sinha. 2015. “The value of Big Data:

How analytics differentiates winners is a partner with

Bain & Company in Atlanta. Global Technology

practice.”

Way, F. & See, S.L., 2015. Big Data Applications:

Adaptive User Interfaces to Enhance Managerial

Decision Making. , pp.3–5.

Xu, Z., Frankwick, G.L. & Ramirez, E., 2015. Effects of big

data analytics and traditional marketing analytics on

new product success: A knowledge fusion perspective.

Journal of Business Research.

Ziora, A.C.L., 2015. The Role of Big Data Solutions in the

Management of Organizations. Review of Selected

Practical Examples. Procedia Computer Science,

65(Iccmit), pp.1006–1012.

The Role of Big Data Analytics in Corporate Decision-making