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A Review of Business Analytics: A Business Enabler or Another Passing Fad

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Business Analytics has emerged as a potential business enabler in both public and private sectors and is one of the fastest growing fields. By implementing Business Analytics initiatives in their organizations, decision makers can integrate disparate data sources, predict trends, improve performance, see key performance indicators, identify business opportunities, and make better and informed decisions. The purpose of this study is twofold: first, it provides a working definition, background, and a review of Business Analytics (BA) / Business Intelligence (BI) / Big Data (BD) theory and practice. Secondly, it discusses if BA/BI/BD is another passing fad or a business enabler.
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Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
Available online at www.sciencedirect.com
1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Istanbul Univeristy.
doi: 10.1016/j.sbspro.2015.06.354
ScienceDirect
World Conference on Technology, Innovation and Entrepreneurship
A Review of Business Analytics: A Business Enabler or Another
Passing Fad
Tuncay Bayrak
a*
a
Western New England University, 1215 Wilbraham Rd. Springfield, MA, 01119, USA
Abstract
Business Analytics has emerged as a potential business enabler in both public and private sectors and is one of the fastest
growing fields. By implementing Business Analytics initiatives in their organizations, decision makers can integrate disparate
data sources, predict trends, improve performance, see key performance indicators, identify business opportunities, and make
better and informed decisions. The purpose of this study is twofold: first, it provides a working definition, background, and a
review of Business Analytics (BA) / Business Intelligence (BI) / Big Data (BD) theory and practice. Secondly, it discusses if
BA/BI/BD is another passing fad or a business enabler.
© 2015 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of Istanbul University.
Keywords: Business Analytics, Business Intelligence, Big Data, Decision Making
1. Introduction
Across various public and private sectors, companies capture and maintain enormous amounts of data on their
customers, products, and services they provide. To leverage this technical data stored and maintained in various
digital platforms such as databases and data warehouses, and to translate it into actionable insights a new field called
Business Analytics (BA) also known as Business Intelligence (BI) or Big Data (BD) has emerged in recent years.
BA has evolved and become a part of every major business decision making process, and it has the potential to
transform businesses as it empowers decision makers with data and supports them to make strategic, operational,
and tactical decisions.
*
Corresponding author. 413-796 2304
E-mail address: tbayrak
@wne.edu
© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Istanbul Univeristy.
231
Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
BA has emerged as a potential business enabler in both public and private sectors and is one of the fastest
growing fields. Some companies have even built their entire business models around this concept and run their
businesses based on their ability to collect, analyse and act on data (Davenport, 2006). Given the fact that we live in
a world of interconnected people and computers generating and making more and more data accessible, BA holds
great promise for companies across various industries. BA can be critical to an organization’s operations and a
crucial part of decision making process. It may revolutionize the way companies harness their data generated
internally such as transaction data or gathered from external sources such as social networks, mobile devices, web-
sites, and data sensors. It
gives decision makers the power to integrate multiple data sources and discover more
insights in them, thus enabling them to gain a holistic view of their business and customers, improve operational
efficiency, move toward a data-driven decision making environment, and deliver business-critical solutions. BA can
help organizations capitalize on the value of historical and real-time data by harnessing the power of statistical and
mathematical models. Using such models, manager can monitor key metrics and operational data and measure and
manage corporate performance. By implementing BA initiatives in their organizations, decision makers can
integrate disparate data sources, predict trends, improve performance, see key performance indicators, identify
business opportunities, and make better and informed decisions. Further, by leveraging BA capabilities and models,
decision makers can identify business drivers of business success, align business goals and the company’s progress,
and develop value-based strategies and fact-based insights of business performance analysis. The purpose of this
study is twofold: first, it makes an attempt to provide a working definition, background, and a review of BA/BI/Big
Data theory and practice. Secondly, it discusses if BA/BI/BD is another passing fad or a business enabler.
2. Literature Review
Business Analytics (BA) may be defined as “a broad category of applications, technologies, and processes for
gathering, storing, accessing, and analyzing data to help business users make better decisions” (Watson, 2009).
Vendors and academics interchangeably use “Business Analytics (BA)”, “Business Intelligence (BI)” and “Big Data
(BD)” to refer to similar topics. For instance, the term “business intelligence” is used by the information technology
community, whereas “business analytics” is preferred by the business community (Sircar, 2009). In this study
however, the term “Business Analytics” is used to be consistent with the leading vendors and academia.
The growing use of information technology (IT) in the business world has led to the development of large and
complex datasets for various organizational functions. Understanding their businesses and making decisions based
on very large datasets has become an important challenge for organizations. The IT industry refers to this
development as “Big Data” to indicate the complexity and size of data sets. Traditional database applications do not
have the capabilities to analyze such big data and address the decision-making needs of organizations. BA is the
current solution for analyzing big data by using advanced mathematical and statistical models, databases, and
interfaces to answer “what has happened” and “what will happen” questions (Wicom et al., 2011).
Having BA capabilities has already become an important goal for organizations. BA/BI ranks among the top five
search terms on Gartner’s website (Schlegel, 2011), recently published books are becoming hits (Wicom et al.,
2011), and leading companies such as Accenture, Deloitte Consulting, and IBM have launched analytics centers and
practices. The field of BA is experiencing enormous growth and the accelerated growth rate of
structured/unstructured data is fueling this growth. As argued by Davenport and Dyche (2013), no single business
trend in the last decade has as much potential impact on incumbent IT investments as BA. According to a study
done by International Data Corporation (IDC), business analytics is one of the top two IT priorities for large
enterprises (SAS-b, 2011). Manyka et al., suggest that by 2018 “The United States alone could face a shortage of
140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-
how to use the analysis of big data to make effective decisions.” Thus, recognizing its importance, numerous
companies have already implemented BA initiatives and technologies to gain a competitive edge.
232 Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
BA gives companies the ability to handle a new type of data such as voice, text, log files, images, and video
(Davenport and Dyche, 2013), and decision makers increasingly view this new type of data and the ability as an
important driver of innovation and a significant source of value creation and competitive advantage (Tan et. al.,
2015). Similarly, a study by (Gartner, 2011) suggests that the ability to manage big data will be a core competency
for enterprises. Companies are investing in BA initiatives for various reasons. For instance, Hagen et al., (2013)
argue that some of the drivers for which companies across various industries have implemented BA initiatives
include to make faster, better, and proactive decisions, improve capabilities, increase automation, eliminate
redundant tools, and streamline processes. A comprehensive study done by IBM (2012) asked a number of
companies to rank their top functional objectives for big data within their organizations. The report suggests that
they invest in BA to achieve customer centric outcomes (49%), optimize operations (18%), manage risk/finance
(15%), and develop new business models (14%). A similar study done by SAS-b (2011), points out that companies
are looking to analytics to help them solve a variety of critical issues; but the primary focus is on money. According
to the same study, the top three issues for analytics to solve are: reducing costs, improving the bottom line, and
managing risk.
Companies are for the most part employing BA initiates in areas where reliance on quantitative information is
typically more prevalent such as strategic planning, finance, and marketing. These functional areas address issues
that require analysis and prediction (SAS-b, 2011). Hagen et al., (2013) agree and suggest that building capabilities
in BA will not only improve performance in traditional segments and functions, but also create opportunities to
expand product and service offerings. A similar study done by SAS-c (2013) shows that organizations are looking to
analytics to improve the way they do business and to use technology, specifically analytics, to drive better decisions.
(2013). Like many new information technologies, BA can bring about dramatic cost reductions, substantial
improvements in the time required to perform a computing task, or new product and service offerings (Davenport
and Dyche, 2013). However, companies investing in BA technologies should address certain challenges and pitfalls
to fully realize the benefits their BA initiatives have to offer. For instance, Davenport (2006) argues that companies
should understand that to make optimal use of BA and the data they constantly collect and store, they should invest
in finding the right focus, building the right cultures, hiring the right people, and installing the right technology.
Similarly, a report published by SAS-a (2014) lists the top three big data challenges as lack of skills/expertise,
difficulty accessing all data, and not effectively using their most valuable data to drive decisions. Furthermore, the
complexities of dealing with big data, integrating technologies, finding analytical talent, and challenging corporate
culture are the main pitfalls to the successful use of analytics within organizations SAS-c (2013). Finally, a
company’s “data-driven mind-set” will be a key indicator of big data’s value to companies (Davenport and Dyche,
2013). Davenport (2006) identifies three key attributes of companies that want to compete on analytics: widespread
use of modeling and optimization tools, an enterprise approach, and senior executives’ advocates. In addition to
such characteristics, companies driving effective “analytics cultures” are reaping the rewards of business analytics
(SAS-b, 2011). IBM (2012) argues that companies that want to run a successful BA initiative must commit initial
efforts to customer-centric outcomes, develop an enterprise-wide big data blueprint, start with existing data to
achieve near-term results, build analytics capabilities based on business priorities, and finally, create a business case
based on measurable outcomes.
2.1. Dimensions of BA/Big Data
BA/BD may be broken into four dimensions: Volume, Variety, Velocity, and Veracity (IBM, 2012). The first
three dimensions were first introduced by (Laney, 2001) in a Gartner research note entitled 3D Data Management.
Later, IBM (2012) introduced Veracity as the fourth dimension (Figure 1).
Volume refers to the magnitude of data (Gandomi and Haidar, 2014). The amount of data available to companies
has been growing enormously. Data scientist has to process massive amounts of data generated by and streamed
from internal and external data sources. This sheer amount of data poses technical challenges for data scientist
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(Gartner, 2011). Extracting relevant information from data and using it to make critical decisions becomes more
difficult as data volumes become too large and complex.
Variety means different types of data, which includes tabular data, hierarchical data, documents, e-mail, video,
still images, audio, stock ticker data, and financial transactions (Gartner, 2011). To have a much more complete
picture of their customers and operations companies are combining unstructured and structured data (Davenport and
Dyche, 2013), and using various raw data sources such as transactions, log data, events, emails, social media,
sensors, external feeds, RFID scans or POS data, free-form text, geospatial, audio, still images/videos (IBM, 2012).
However, it’s important to remember that the primary value from big data comes not from the data in its raw form,
but from the processing and analysis of it and the insights, products, and services that emerge from analysis
(Davenport and Dyche, 2013).
Velocity refers to the rate at which data are generated and the speed at which it should be analyzed and acted
upon (Gandomi and Haidar, 2014). Dealing quickly and in a timely manner with data velocity is a challenge for
most data scientist and decision makers.
Veracity is defined by IBM (2012) as data uncertainty. It refers to biases, noise, and abnormality in data
(Normandeau, 2013). In other words, veracity may be defined as uncertainty due to data inconsistency,
incompleteness, ambiguities, latency, deception, and model approximation (Corrigan, 2012).
Fig.1. Dimensions of Big Data
2.2. Types of Analytics
Three types of analytics are employed by organizations: descriptive, predictive, and prescriptive (Davenport and
Dyche, 2013).
Descriptive analytics uses business intelligence and data mining to provide trending information on past or
current events. Descriptive analytics drills down into data to uncover details such as the frequency of events, the cost
of operations, and the root cause of failures (IBM, 2013). Descriptive analytics provides significant insight into
business performance and enables users to better monitor and manage their business processes (Lustig et al., 2010).
Predictive analytics uses a variety of models and techniques to predict future outcomes based on historical and
current data (Gandomi and Haidar, 2014). In predictive modeling, data is collected, a statistical model is formulated,
predictions are made, and the model is validated as additional data becomes available (Gartner IT Glossary. n.d).
Predictive analytics is what translates big data into meaningful, usable business information (Abbott, 2014). It
unleashes the power of data, and allows decision makers to learn from data how to predict the future behavior of
individuals (Siegel, 2013).
234 Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
Prescriptive analytics may be defined as a set of mathematical techniques that computationally determine a set of
high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the
goal of improving business performance (Lustig et al., 2010). Prescriptive analytics enables decision-makers to not
only look into the future of their mission critical processes and see the opportunities, but it also presents the best
course of action to take advantage of that foresight in a timely manner (Basu, 2013).
3. Methodology
3.1. Research Method and Data Collection
This study was conducted utilizing publicly available data/information/publications maintained in well-known
databases such as sciencedirect.com. While the internet is full of web sites where a wealth of information can be
found on any subject, in this study we made use of the following major databases recognized and used by the
academic world across the globe (table 1).
Table 1. Databases and their websites
Database/Publisher Website
Elsevier www.sciencedirect.com
Wiley www.wiley.com
Springer www.springer.com
Emerald www.emeraldinsight.com
Taylor & Francis www.tandfonline.com
INFORMS www.informs.org
ACM Digital Library dl.acm.org
We realize the aforementioned table is not exhaustive and understand that some other databases and publishers
may be added to the list. Nevertheless, the above list of the publishers represents a good number of publishers or a
significant portion of knowledgebase a majority of researchers, scientist, academicians would use when doing a
scientific literature review.
The above databases can be accessed through any college and university network. Thus, we logged in to each
database one by one and queried each one using the three key words/criteria: Business Analytics, Business
Intelligence, and Big Data. In this study, we reported on every published study in the area of Business Analytics,
Business Intelligence, and Big Data between the years 2000 and 2014. Every publication containing any of the three
key words in its abstract, title, or in the body of the publication was tallied.
Once the information was retrieved, we then refined and filtered our search within each database by topic,
content type, discipline, frequency of the key words in each publication, publication type, and subject. In the
following secti
ons we summarize our findings. The tables and figures presented in the following section are created
using Excel 2013, which itself can be used as a business intelligence application.
3.2. Analyses and Results
We first queried sciencedirect.com, which is maintained by Elsevier, one of the largest publishers in the world.
Figure 2 summarizes the total number of publications containing the three keywords of interest published between
the years 2000 and 2014. As seen in figure 2, there is an exponential increase in the number of publications covering
the three key words. While there was a few publications in the early 2000s, as of the end of 2014, in just one
database there were more than 2500 outlets containing the criteria BA, BI, or BD. Figure 2 indicates that BA/BI/BD
capabilities, applications, and tools have emerged as one of the fastest growing fields in recent years.
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0
500
1000
1500
2000
2500
3000
2000 2001 2002 2004 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
yearlyfrequency
Year
Total number of publications containing the keywords BA/BI/BD
Fig. 2. The number of publications containing the keywords BA/BI/BD
We then broke down the total numbers by BI, BA, and BD. As seen in figure 3, while in the early 2000s, there
was almost no publication containing the criteria BI, starting in 2013, however; BD is alluded to more frequently
than the other two keywords. This may be because the term “big data” is used more and more frequently by the
IT/IS community to communicate their findings to the general business community. The term BI is the second most
frequently cited keyword in the same database of numerous publications.
800
1000
1200
1400
1600
1800
2000
l
yfrequency
N
umber of publications containin
g
Fig. 3. Frequency of BA/BI/BD by year
236 Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
As summarized in figures 2 and 3, having analyzed the data in Sciencedirect.com, we summarized the content
type and the total number of publications listed in seven different databases containing the three key words. Table 2
is a pivot table which breaks data by the publisher and the three key words. As seen, between the years 2000 and
2014, the total number of outlets/publications making a reference to BA was 1878. Similarly, the same databases
contained a total of 17549 references made to the keyword BI. Finally, the key word BD was alluded to 13391 times
in the same databases. The database maintained by Springer contained the highest number of publications making
references to the three key words.
Table 2. Content type and summary of the number of publications in seven databases
Row Labels Sum of BA Sum of BI Sum of BD Total
ACM 350 2814 1288 4452
Article/Chapter 350 2814 1288
Emerald 55 834 268 1157
Article/Chapter 55 834 268
INFORMS 151 109 59 319
Book Review 1 4
Chapter 1 1 3
Miscellaneous 67 31 20
Notice Editorial 2 2 6
Primary Article 72 71 28
Primary Introduction 8 2
Science Direct 386 3998 3366 7750
Book 89 1134 697
Journal 296 2848 2656
Reference Book 1 16 13
Springer 551 7498 5863 13912
Article 131 1234 1790
Book 6 51 36
Chapter 400 6137 3978
Reference Work Entry 14 76 59
Taylor & Francis 117 928 820 1865
Article/Chapter 117 928 820
Wiley 268 1368 1727 3363
Books 130 550 417
Journals 138 818 1310
Grand Total 1878 17549 13391 32818
Figure 4 summarizes the frequency of the three key words vs. the publishers and databases. As seen, in terms of
the content type, the highest number of references are made to BI in various chapters published by Springer,
followed by Sciencedirect and ACM Digital Library. Looking at the keyword BD, we see a similar pattern. BD
appeared 5863 times in numerous outlets published by Springer. Similarly, Sciencedirect contains the second
highest number of publications containing the keyword Big Data.
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0
1000
2000
3000
4000
5000
6000
7000
Article/Chapter
Article/Chapter
BookReview
Chapter
Miscellaneous
Notice Editorial
PrimaryArticle
PrimaryIntroduction
Book
Journal
ReferenceBook
Article
Book
Chapter
ReferenceWorkEntry
Article/Chapter
Books
Journals
ACMEmerald INFORMS ScienceDi rect Springer Taylor&FrancisWiley
SumofBA
SumofBI
SumofBD
Fig.4. BA, BI, and BD vs. Publishers/Databases
Since Springer has the highest number of publications pertaining to BA/BI/BD, we also looked at the topics and
subjects in which the three key words are covered in the database maintained by Springer. Table 3 summarizes the
first 15 topics/fields utilizing BA/BI/BD applications. As expected, the computer science field is where the there key
words are cited more frequently. Followed by the Computer Science field is the Business and Management field in
which BA/BI/BD tools and applications are employed to address various business and management challenges.
Surprisingly, Engineering is the third field where BA/BI/BD applications and tools are employed.
Table 3. Topic/subject and BA/BI/BD
Topic/Subject Frequency
Computer Science 5899
Business & Management 3547
Engineering 1192
Economics 600
Life Sciences 535
Mathematics 343
Statistics 341
Physics 318
Big Data 244
Biomedical Sciences 194
Social Sciences 179
Medicine 176
Education & Language 158
Earth Sciences & Geography 140
Public Health 123
238 Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
As seen in table 3, in addition to such fields as Computer Science, and Business and Management, where one
would be expected to utilize BA/BI/BD capabilities, it appears that BA/BI/BD applications and tools are employed
across various fields such as Economics, Life Sciences, Mathematics, Biomedical Sciences, and Public Health.
BA/BI/BD capabilities appear to hold great future for various public and private sectors.
To get a more holistic view of the frequency of the keywords BA/BI/BD and the discipline in which they are
alluded to, we explored each of the seven databases. Table 4 lists the first 15 disciplines and the frequency of the
three key words within each discipline. As seen in table 4, a similar pattern emerges. As expected, the three key
words appear 10842 time in the publications pertaining to the Computer Science field. The same three key words
appear 4571 times in a title, an abstract, or the body of the publications pertaining to the Business & Management
discipline.
Table 4 Frequency of BA/BI/BD by Discipline
Discipline
Freque
ncy
Computer Science 10842
Business & Management 4571
Engineering 2108
Economics 740
Mathematics 664
Medicine 439
Life Sciences 423
Statistics 418
Social Sciences 417
Physics 387
Biomedical Sciences 328
Earth Sciences &
Geography 217
Education & Language 198
Public Health 169
In addition to the number of well-known disciplines such as computer science where one would utilize BA
applications, table 4 shows that BA capabilities and tools are employed in various disciplines including Medicine,
Life Sciences, Social Sciences, Geography, and Public Health. These findings suggest that one should not assume
that BA/BI/BG capabilities can only be made use of in IT//IS/Computer disciplines. Contrary to the common
perception, advances in information and communication technologies and data visualization have made it possible
for decision makers to employ BA capabilities and tools in numerous disciplines.
4. Conclusion
More and more companies recognize the vital role BA plays in addressing their challenges, predicting future
outcomes, and capitalizing on the value of data. A growing number of companies rely on BA to plan and optimize
their business operations, forecast their business outcomes, improve efficiency, make better decisions, offer new
products and services, and capture new market opportunities. In addition, advanced analytics capabilities can help
decision makers find more novel uses of data, build their organizations around data, and transform their business
models.
Every industry is faced with a different set of challenges and business analytics presents new opportunities for
decision makers to deal with such challenges. Numerous studies alluded to in the literature review section suggest
that BA has already become a business enabler in various organizations. It can be concluded that with all the tools,
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Tuncay Bayrak / Procedia - Social and Behavioral Sciences 195 ( 2015 ) 230 – 239
models, technologies, opportunities, and capabilities it presents, BA is not a passing fad, rather it’s a much
promising paradigm shifter.
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... BA has evolved in the past few years and continues to gain popularity (Delen & Ram, 2018). It is considered by many to be a major part of decision making in many organisations (Bayrak, 2015). As a key business driver, BA methods are being used in many different ways to empower decision makers in the business world with data that supports them to make strategic and operational decisions (Delen & Ram, 2018;Duan et al., 2018). ...
... BA gives organisations the power to integrate multiple data sources and discover insights that help them develop new or improved products, respond to changing customer demands, react swiftly to changing market conditions and gain competitive advantage (Basole, 2014;Duan et al., 2018;Wamba et al., 2017). New technologies are enabling BA to provide descriptive, predictive and prescriptive analysis to aid decision makers (Bayrak, 2015;Duan et al., 2018;Holsapple, Lee-Post, & Pakath, 2014) with advances in data processing technologies playing a role (Delen & Ram, 2018). While BA introduces many opportunities for organisations, it has been met with many challenges in its successful implementation. ...
... Similarities can exist between HEIs and commercial organisations in their adoption of analytics. In both business and education, analytics are used to derive insights in order to make more informed decisions (Bayrak, 2015;Duan et al., 2018;Lester et al., 2017). Decision makers in industry use predictive analytics in BA to identify future trends, and prescriptive analytics to determine the most optimal route to take in order to gain competitive advantage and meet constantly changing customer demands (Bayrak, 2015;Delen & Ram, 2018). ...
Chapter
Developments in educational technologies as well as the need by higher education institutions (HEI) to improve both the teaching and learning environment are contributing to the growth of learning analytics (LA). Research in LA has predominantly been exploratory in nature and a shift is needed for evaluative research that explores the impact and outcomes of LA. Moreover, there is limited research on the use of LA at HEI from a South African context with most research focusing on the Global North and Australia. The aim of this article is to respond to that research gap by presenting findings from an empirical study investigating the use of LA in SA. This article draws technology-organization-environment (TOE) framework and DeLone and McLean’s IS success model to develop a theoretical integrated model. The outcome of the study seeks to help HEIs better recognize opportunities available to them through the use of LA and to guide future empirical research on exploratory studies of adoption of LA.
... Las mejoras en la comunicación introducidas por la cuarta revolución industrial impactaron positivamente a la industria en general en los últimos años; estas se han convertido en facilitadoras, activadoras de la digitalización y el intercambio de datos (Bayrak, 2015). En este sentido, se pueden listar numerosas ventajas competitivas empresariales, entre las que destacan el ahorro del tiempo, trámites, eficiencia en operaciones (Antunez et al., 2020) y la multicanalidad, que han implementado las organizaciones y la innovación en la comunicación con los clientes (Kannan y Li, 2016). ...
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La digitalización ha dado lugar a la aparición de conceptos como la omnicanalidad. Así, esta investigación tuvo como objetivo desarrollar una revisión teórica, a través de un análisis de mapa de redes temáticas y entrevistas con expertos, para describir las áreas empresariales críticas del proceso y sus dimensiones. Las áreas empresariales importantes en las que se presenta la estrategia son la cadena de suministro e inventarios, el marketing y los modelos de negocio y las tecnologías digitales. Entre las dimensiones encontradas se encuentran el mercadeo digital y los sistemas de fidelización y empoderamiento de los clientes en canales en línea y fuera de línea; sistemas de información digital, integrados en tiempo real a la cadena de suministro; y plataformas para la captura, analítica e intercambio de grandes volúmenes de datos en interacción con los clientes. En la revisión realizada, se encontró que la omnicanalidad es más rentable que otras estrategias, optimiza entregas, rotación de inventarios y costos.
... No business analytics activity is ever performed in vain. They all seek to increase business value for the enterprise [4,5]. ...
... BA is a mixture of techniques, technologies and applications used to scrutinize a corporation's data and performance to transpire data-driven decision-making analytics for the corporation's future direction and investment plans (Bayrak, 2015;Kristoffersen et al., 2021). Data-driven corporations will manage their data as their corporate assets and actively look for ways to turn it into a competitive advantage against their competitors (Bawack and Ahmad, 2021). ...
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Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications
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The digitalization of industry, the growth and development of artificial intelligence are attributed to the advent of big data and hence there has been increased interest in research into business analytics, business intelligence, and big data analytics. Business analytics is seen as a complex continuum of data analytics, statistical analytics, and the ability to gain information and knowledge for decision-making. Business analytics is generally understood to support the information society, knowledge-based economy, and digital innovation. The research publications in business analytics have by and large been systematic reviews of literature with a limited number of empirical studies in business analytics. Hence, this paper seeks to review some of the most significant extant literature on business analytics while emphasizing on important theoretical and empirical contributions relating to business analytics in industry, education, and professional training. Second, this paper proposes future issues relating to business intelligence, advanced data mining, applied analytics, and reporting.KeywordsBusiness analyticsBusiness intelligenceKnowledge managementPredictive analyticsBig dataData scienceData miningData visualizationProfessional developmentIndustry
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Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is "How can big data analytics support people-centred and integrated health services?" Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.
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Financial inclusion becomes a global agenda to achieve sustainable development goals (SDGs). In Asia, many countries are engaging financial inclusion as a strategy to reach inclusive growth. Digitalization now brings digital financial inclusion. Peer-to-peer (P2P) lending and philanthropy platforms become a new face of digital financial inclusion in Asia. As the third-largest population in Asia and Southeast Asia’s biggest economy, Indonesia has remarkable untapped financial technology prospects in P2P lending and philanthropy platforms. Indonesia represents a major global economy in Asia, where financial inclusion works and the digital economy has begun to arise and has become the most generous country through Islamic philanthropy. Therefore, by using Indonesia as a case study, this chapter describes P2P lending and philanthropy platform by focusing on three issues: (1) entry barriers; (2) developing digital financial inclusion ecosystems; and (3) social and economic impacts. Overall, this chapter has a mission to invite the public to be involved in financial inclusion as a form of shared social responsibility through digital financial inclusion. The aim is not oriented for commercial financing to the people who are the target of financial inclusion, but as social financing for empowering them to reach a better living standard.
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Current trends suggest that academia may be behind the curve in delivering effective Business Intelligence programs and course offerings to students. In December 2009 and 2010, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congresses and conducted surveys to improve the understanding of the state of BI in academia. This panel report describes the key findings and best practices that were identified. The article also serves as a "call to action" for universities regarding the need to close a widening gap between the BI skills of university graduates in Information Systems and other fields and BI market needs. The IS field is well positioned to be the leader in creating the next generation BI workforce. To do so, it is important for IS to begin moving on this opportunity now. We believe the necessary first step is for BI and IS leaders to advance the BI curriculum.
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Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
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We all know the power of the killer app. It's not just a support tool; it's a strategic weapon. Companies questing for killer apps generally focus all their firepower on the one area that promises to create the greatest competitive advantage. But a new breed of organization has upped the stakes: Amazon, Harrah's, Capital One, and the Boston Red Sox have all dominated their fields by deploying industrial-strength analytics across a wide variety of activities. At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the few remaining points of differentiation--and analytics competitors wring every last drop of value from those processes. Employees hired for their expertise with numbers or trained to recognize their importance are armed with the best evidence and the best quantitative tools. As a result, they make the best decisions. In companies that compete on analytics, senior executives make it clear--from the top down--that analytics is central to strategy. Such organizations launch multiple initiatives involving complex data and statistical analysis, and quantitative activity is managed atthe enterprise (not departmental) level. In this article, professor Thomas H. Davenport lays out the characteristics and practices of these statistical masters and describes some of the very substantial changes other companies must undergo in order to compete on quantitative turf. As one would expect, the transformation requires a significant investment in technology, the accumulation of massive stores of data, and the formulation of company-wide strategies for managing the data. But, at least as important, it also requires executives' vocal, unswerving commitment and willingness to change the way employees think, work, and are treated.
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There has been widespread investment in business intelligence/business analytics within industry because of the potential for improved managerial decision-making through mining the vast quantities of data collected by modern corporations; however, despite major recent curriculum changes in business schools There has been very little attention given to this field. This has been true of both research and teaching and is compounded by inadequate quantitative literacy possessed by U.S. students and antipathy towards quantitative literacy among faculty. This paper documents the importance of business intelligence within business and the programs offered by the 50 leading business schools. A pioneering minor in the field offered by one school is described.
Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst
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Abbott, D. (2014). Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, Wiley.
Five Pillars of Prescriptive Analytics Success
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Basu, A. (2013). Five Pillars of Prescriptive Analytics Success, Analytics Magazine, March-April, 8-12.