Education and training for successful
career in Big Data and Business Analytics
Rameshwar Dubey and Angappa Gunasekaran
Rameshwar Dubey is Professor
at Symbiosis Institute of
Operations Management,
Constituent of Symbiosis
International University, CIDCO,
New Nashik, India.
Angappa Gunasekaran is
Professor at Charlton College
of Business, University of
Massachusetts Dartmouth,
North Dartmouth,
Massachusetts, USA.
Abstract
Purpose – The purpose of this paper is to identify Big Data and Business Analytics (BDBA) skills and further
propose an education and training framework for a successful career in BDBA.
Design/methodology/approach – The present study adopts a review of extant literature and appreciative
enquiry (AI) which is a quasi-ethnographic approach to identify the skills required for BDBA.
Findings – The study helps to identify skills for BDBA and based on extant literature and AI, proposes
a theoretical framework for education and training for a successful career in BDBA. Further research
directions are outlined which can help take the present research to the next level.
Research limitations/implications – The paper presents a theoretical framework, but it has to be validated
through empirical data. This research will generate a lot of interest to develop a more practical framework and
conduct empirical and case study research.
Practical implications – The present study has outlined skills for BDBA. The authors have also proposed a
theoretical framework which can further help an educational or training institute to embrace the framework
to train young undergraduates or graduates to acquire BDBA skills. It may also motivate an institution to
structure their curriculum for a BDBA program.
Social implications – This research is a timely one to develop necessary skills for being successful in BDBA
career and in turn contribute to the well-being of business community and society.
Originality/value – This research is a novel one as there is no research done earlier on this new and
emerging areas of research, namely, education and training for BDBA.
Keywords Appreciative inquiry, Framework, Big data and business analytics, Education and training
Paper type Research paper
1. Introduction
Received 16 August 2014
Revised 27 August 2014
Accepted 27 August 2014
PAGE 174
j
In recent years “Big Data” and “Business Analytics” (BDBA) have attracted burgeoning interest
among practitioners and academia due their immense potential (Waller and Fawcett, 2013).
Chen et al. (2012) explored how the possible impacts of “Big Data” extend to all facets of
business. A study conducted by IBM Institute for Business Value in collaboration with MIT Sloan
Review revealed the importance of data to business leaders (LaValle et al., 2011). The subject
has been embraced in special issues in leading journals including Harvard Business Review, MIT
Sloan Review, MIS Quarterly, with more to follow from International Journal of Production
Economics, International Journal of Production Research, International Journal of Operations
and Production Management and many others. However, despite increasing contributions, the
field is still in its infancy. Waller and Fawcett (2013), in their seminal paper, identified the need for
blending Data Science, Predictive Analytics and Big Data (DPB) in supply chain network design.
The power of “Big Data” has also found immense application in the Banking, Financial Services,
and Insurance (BFSI) domain, health, sports and may other sectors (LaValle et al., 2011). Bean
(2014) recently commented on corporate trends (“Big Data Fatigue?”; http://sloanreview.mit.
edu/article/big-data-fatigue/) and the lack of proper understanding of “Big Data” among senior
professionals who are gearing up for massive investment in “Big Data” analytics capability.
INDUSTRIAL AND COMMERCIAL TRAINING
j
VOL. 47 NO. 4 2015, pp. 174-181, © Emerald Group Publishing Limited, ISSN 0019-7858
DOI 10.1108/ICT-08-2014-0059
Shah et al. (2012) questioned the investment policies of companies expecting significant returns
on investment in Big Data, as this can be useless without appropriate training (Shah et al., 2012;
Provost and Fawcett, 2013; Waller and Fawcett, 2013). However, most past studies revolved
around applications of “Big Data” and its usefulness in various fields, with few examining the need
for training to create a pool of talent who can work with “Big Data” (for an exception, see Waller
and Fawcett, 2013).
The objectives of our present study, derived from the discussions above, are:
1.
to identify desired skills for “Big Data and Business Analytics”;
2.
to propose a theoretical framework for education and training for BDBA; and
3.
to outline further research directions.
To address these, the next section of the paper discusses the importance of BDBA. The third
section focusses on the need for education and training in BDBA. The fourth section presents a
framework for education and training for BDBA. Section 5 discusses the framework and makes
further recommendations. In our last section we conclude our study and outline our unique
contribution, the managerial implications, limitations of our present research and further research
directions.
2. Importance of BDBA
BDBA is defined as “an integration of data and technology that accesses, integrates, and reports
all available data by filtering, correlating, and reporting insights not attainable with past data
technologies” (APICS, 2012). According to the Department of Business-Innovation and Skills
(2013), BDBA is an emerging phenomenon which reflects higher dependence on data in terms
of growing volume, variety and velocity. BDBA has been identified as one of the critical
differentiators for any organization (LaValle et al., 2011) and much attention has been paid to its
impact on organizations’ values (Mithas et al., 2013; Sharma et al., 2014). Waller and Fawcett
(2013) outlined possible opportunities in the field of supply chain management, and Chen et al.
(2012) similarly outlined the possible applications of BDBA in predicting market behavior and
further leveraging the associated opportunities. Other studies confirmed a positive relationship
between BDBA and organizational performance (Anderson-Lehman et al., 2004; Davenport and
Harris, 2007; Davenport et al., 2010; Wixom et al., 2013). Wixom et al. (2013) reported how a
retailer maximized value from BDBA. Table I presents a non-exhaustive list of BDBA applications.
Table I presents research claims that require further investigation. In spite of the growing
popularity of BDBA, we propose that the critical challenge preventing organizations maximally
exploiting “Big Data” and world class technology is lack of adequate skills. In our next section we
will further discuss the pressing need for education and training in BDBA.
Table I Some important applications of Big Data and Business Analytics (BDBA)
References
Applications of BDBA
Langley (2014)
Study conducted on logistic service providers suggested 97 percent of shippers and 93 percent of 3PLs feel that
improved data-driven decision making is essential for future supply chain success
BDBA can have major impact on supply chain network design and management
Waller and Fawcett
(2013)
Tweney (2013)
Study reveals that big retailers are leveraging Big Data to improve customer service, reduce fraud and implement
just-in-time systems
Wilkins (2013)
BDBA can help in a manufacturing environment to improve quality, logistics, and order fulfillment cycle
Kiron (2013)
In operations management BDBA can be used for managing customer relationships and operations risk and improve
production efficiency
Davenport et al.
In a retail environment real-time information leads to voluminous data that becomes unstructured when not properly
(2012)
managed, but with proper treatment can be used for predicting desired outcomes
Manyika et al. (2011) BDBA can be useful to support global manufacturing and supply chain innovation by creating data transparency,
improving human decision making and promoting innovative business model
VOL. 47 NO. 4 2015
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
PAGE 175
3. Education and training in BDBA
Waller and Fawcett (2013) advocated for change in the foci of research and education to meet
the growing challenge of BDBA, while Shah et al. (2012) argued that skill availability for exploiting
BDBA to enhance organizational value is a serious issue that needs urgent attention. In response
to such calls we have identified the research objectives that form the core of our paper. Our first
objective is to identify the skills required for BDBA professionals based on a review of the literature
and through appreciative inquiry (AI) approach, which is a quasi-ethnographic perspective.
3.1 Methodology
AI, termed a discipline of positive change, has evolved counter to dominant inquiry, a system
which focusses on the negative. However, most critiques of the AI approach are based on
potential bias due to the use of positive questions. Hence, we adopt a balanced approach which
tries to identify both the positive and negative, and then search for ways to employ the positive
to help transform the negative. AI involves four stages: discovering, dreaming, designing, and
action. In the discovery stage the researcher attempts to unearth what is good in the seemingly
bad situation. In the design stage a blueprint is prepared to guide participants toward the goal(s)
dreamed in the previous phase; and in the action stage participant(s) execute the blueprint. In our
present study, due to time constraints, we have included only three stages of AI in a
quasi-ethnographic approach which reflects the microscopic view of data-richness embodied in
full scale ethnographic studies.
3.2 Data collection
We interviewed ten heads of business analytics for companies situated in Mumbai and the Pune
region of Maharashtra, using Skype due to their busy schedules. They were initially reluctant
to participate; however, when we explained our research objectives, they became excited and
agreed to share their valuable time with us.
We used probing questions to gain as much depth as possible. Using the principles of AI
and ethnographic research, we engaged the respondents and attempted to uncover their true
feelings, with more emphasis on the positive about their lives as BDBA professionals.
The remaining sections of the present paper revolve around the framework set by the themes in
the seven questions:
1.
How do you feel about being a business analytics head?
2.
What factors influenced you to become business analytics professional?
3.
What are the various pros and cons of the business analytics field?
4.
What is your opinion about “Big Data” and to what extent will “Big Data” impact the existing
“Business Analytics”?
5.
What are the skills required to be a successful professional in BDBA?
6.
What is the current situation in terms of available talent in India for BDBA?
7.
How can education and training help to solve the existing crisis of shortage of talent in
BDBA?
Direct responses from the participants constitute the bulk of the paper, alongside integration with
the BDBA literature.
3.2.1 Likes and dislikes of BDBA profession. Almost every respondent answered this question.
They all feel proud to be business analytics professionals, because it is highly demanding in
terms of skill. They expressed that BDBA professionals are in great demand due to an acute
shortage of skilled professionals. . In recent years, organizations have invested huge amounts
in building BDBA capabilities. Concerns include the rapid evolution of data science, BDBA,
making continuous training and skills updating vital for this profession, along with the shortage of
skilled team members. All interviewees noted that most recruits in BDBA have insufficient skills to
meet the growing needs of this fast-evolving profession.
PAGE 176
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
VOL. 47 NO. 4 2015
“BDBA professionals are in great demand due to
an acute shortage of skilled professionals”
3.2.2 Skills required for business analytics profession. All interviewees responded to this
question, feeling that their IT skills blended with communication skills helped them to enter into
this profession. Second, their passion to read and explore further has helped them to grow faster.
However, some of them further indicated that these skills are not enough where data were
voluminous and unstructured. They also feel that they do not have an exact solution for these
challenges.
3.2.3 Pros and cons of business analytics field. This question asked BA heads to delineate their
profession from others. However, they responded positively, stating that like all professions, it has
pros and cons. However, it is very challenging and satisfying for those people who love to explore
from data, while those who do not may dislike the profession as it involves minimum interaction
with other professionals in the same organization.
3.2.4 Big Data and impacts of Big Data on existing business analytics. In contrast to the previous
questions, when we asked the professionals’ opinions about “Big Data,” most of them had no
clear answer. Some stated that “Big Data” is a recent concept which is likely imminently to impact
the way “Business Analytics” is performed. However, some of them also expressed concerns
toward voluminous data, indicating that it may lead to faulty interpretation of existing voluminous
data. We tried to explore perspectives on “Big Data” further, however, participants were reticent
as they feel that the concept is still in its infancy and their organizations need more time to analyze
and understand the application of “Big Data.”
3.2.5 What are the skills required to be successful BDBA professionals?. This question forms the
core of our paper. We attempted to engage our participants so that they would express their
original opinions about this question. Most indicated that IT skills supported by communication
and analytical skills are very important. Some even suggested that operations research and a
background in statistics are really important for success in the BDBA profession. However, most
of these professionals were not highly conversant with the use of the nascent concept of
“Big Data” and thus can be categorized, based on capabilities, as “aspirational” (LaValle et al.,
2011). Therefore, to further understand the skills required by BDBA professionals we looked to
extant literature (e.g. Chen et al., 2012; Waller and Fawcett, 2013). Most of the skills identified are
technical (hard) skills, as classified in Table II.
Hard skills. Rao (2014) attempted to define hard skills as domain knowledge or technical skills
which are important for successful execution of the task. We have identified hard skills based on
extant literature and AI as outlined in Table II.
Soft skills. Soft skills represent individuals’ attitudes and communication skills, leadership ability and
passion for excellence that Rao (2014) defined as most important for successful performance.
Despite this, soft skills are often ignored, sometimes resulting in failure. We have outlined the
relevant soft skills based on extant literature and our AI as outlined in Table II. The role of education is
regarded as critical for building hard and soft skills among BDBA profession aspirants.
4. A framework for education and training for BDBA
The basic premise of our theoretical framework (see Figure 1) comprises three elements:
education, BDBA skills, and demographic profile (age, gender, educational background, etc.).
The right kind of education is critical to skill development and its effectiveness depends upon the
training pedagogy (Katharaki et al., 2009). Here, it could be formal (i.e. university or college-based
education) or non-formal (i.e. distance education, e.g. through webinar, Skype or video
conferencing) depending upon the learners and their requirements. In our model we have
identified training as a moderating variable, and controlled for the demographic profiles of the
learners to further account for differences in learning ability.
VOL. 47 NO. 4 2015
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
PAGE 177
Table II BDBA skill set
Hard skills
Soft skills
Statistics
Forecasting
Optimization
Quantitative finance
Financial accounting
Multivariate statistics
Multiple criteria decision making
Marketing
Research methods
Finance
Leadership ability
Team skills
Listening skills
Learning
Positive attitude
Communication skills
Interpersonal skills
Patience
Passion
Note: Authors own compilation
Figure 1 Education and training framework for BDBA skills
Demographic profiles (age, gender, educational qualifications)
Training
Formal Education
Informal Education
Hard Skills
Soft Skills
The building blocks of our conceptual framework (Figure 1), based on review of extant literature
and our AI, are further explained as follows.
Formal education
Refers to education offered through conventional teaching universities or colleges where there is
direct interaction between instructors and learners. Here, we assume a direct linkage between
formal education and hard and soft skills, as shown in Figure 1.
Informal education
Informal education is as old as humanity, but has received increasing attention in recent years.
Compared to formal education it is highly flexible as the learning pace is adjusted to the pace of
learners. In our conceptual framework we have assumed a direct linkage between informal
education and hard and soft skills for BDBA.
PAGE 178
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
VOL. 47 NO. 4 2015
Training
Most literature has used education and training interchangeably; however, Cross (1996) clearly
differentiated between them. Education can be measured by tenure of learning whereas training
is about building skills. If education is not supported by proper training, the learning outcome may
not translate into desired skills. Here we have assumed that the impact of education is moderated
by appropriate training.
Demographic profiles
In our assumptions we have controlled for the age, gender and educational qualifications of the
learner. This will further help us to understand differences in learning attitudes.
5. Discussion
The proposed theoretical framework (Figure 1) shows how education, moderated by appropriate
training, can help to build the skills necessary for BDBA. To further generalize the outcome,
we have identified demographic variables as confounding variables to avoid compromising
the internal validity of the proposed framework. The education must be formal and informal, which
depends upon the needs of the learners. Formal education modules must be designed for young
graduates and undergraduates; informal education modules must be distinct from these and
will depend on adult learners’ requirements. However, these assumptions are based on our
pragmatic approach. The conceptual framework needs to be empirically tested.
5.1 Recommendations
We have outlined our recommendations based on preceding discussions:
1.
In order to successfully impart identified BDBA skills (see Table I), there is a need to revamp
our existing education system.
2.
The education system must take care of the needs of the learners. Hence it is recommended
to have both formal and informal education systems.
3.
Effective training moderates the learning outcome. We need to analyze the proper fit
between education and training to improve the effectiveness of the learning outcome.
4.
The education and training mix must be designed according to the learners’ backgrounds:
age, gender and educational backgrounds may differently impact the soft and hard skills of
aspiring BDBA professionals.
6. Conclusions
“Big Data and Business Analytics” has immense potential to increase organizational value,
provided that we have enough talent with sufficient hard and soft skills to meet the BDBA
challenge. In this paper an attempt has been made to understand the skill set required for a
successful career in BDBA. Although the subject has attracted burgeoning interest from
academia and practitioners in recent years, hardly any literature, with the notable exception of
Waller and Fawcett (2013), has focussed on the skills set required for a successful career in
BDBA. We have attempted to extend previous work using AI to identify the skills required to excel
in the BDBA profession. We have classified our analyses into two broad skills sets; first, hard
skills, where most of those identified corroborate the skills identified by Waller and Fawcett
(2013). Second, soft skills, which we derived using an AI approach supported by extant literature
(e.g. Chen et al., 2012). Overall, we have attempted to answer pending calls of eminent scholars
for education and training for BDBA (Chen et al., 2012; Waller and Fawcett, 2013). In response to
“The education system must take care of the
needs of the learners”
VOL. 47 NO. 4 2015
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
PAGE 179
this pressing need, we have attempted to develop a theoretical framework for education and
training for BDBA. However, the proposed framework needs to be examined using survey data.
6.1 Limitations of present study
In the present research, we have used a qualitative research approach, which may answer
“what?” and “why?” but cannot establish causal relationships. AI is a quasi-ethnographic method
with its own limitations. The technique involves positive discussion. Although we took utmost
care to eliminate bias, full-scale ethnographic studies are recommended to fully investigate
relationships in Information and Operations Management studies. Further, we derived our
framework based on our literature review and AI outcomes, but did not include organizational
culture or institutional drivers. It has become very important to understand these to further explore
the intention of an organization to embrace BDBA.
6.2 Further research directions
We can see how BDBA can be very useful for an organization to enhance its values. However, there
remain immense opportunities to study the skills gap in BDBA. Further research directions include:
1.
Education and training for successful careers in BDBA could be addressed through a Special
Issue of a leading journal such as Industrial and Commercial Training.
2.
The present study can be extended to include additional factors like leadership, country
culture, government support and institutional drivers to further understand the impact of
education on creating BDBA skills among aspiring professionals.
3.
To further explore the BDBA field, an interpretivist approach may help researchers to explore
the antecedents of the likes and dislikes of BDBA professionals, while a positivist approach
using survey methods can further help to establish causal relationships.
4.
Impacts of BDBA on supply chain network design and its performance may be explored.
5.
The role of BDBA can further be explored in forecasting demand of short PLC products in
highly uncertain environments.
6.
Empirical research can be conducted to predict the buying behavior of customers for any
product, further helping to provide the missing link in forecasting science.
7.
A study can be conducted on match and mismatch between learning ability and BDBA skills.
This can further help to design an appropriate education and training framework for specific
learners.
Indeed, we believe that our study outcome may trigger a response among our research
community and practitioners to resolve the existing debate regarding shortage of talent to
address BDBA challenges.
References
Anderson-Lehman, R., Watson, H.J., Wixom, B.H. and Hoffer, J.A. (2004), “Continental airlines flies high with
real-time business intelligence”, MIS Quarterly Executive, Vol. 3 No. 4, pp. 163-76.
APICS (2012), “APICS 2012 big data insights and innovations executive summary”, APICS, available at: www.
apics.org/docs/industry-content/apics-2012-big-data-executive-summary.pdf (accessed August 30, 2014).
Bean (2014), “Big data fatigue?”, MIT Sloan Review Blog, June 23, available at: http://sloanreview.mit.edu/
article/big-data-fatigue/ (accessed September 30, 2014).
Chen, H., Chiang, R.H. and Storey, V.C. (2012), “Business intelligence and analytics: from big data to big
impact”, MIS Quarterly, Vol. 36 No. 4, pp. 1165-88.
Cross, J. (1996), “Training vs education: a distinction that makes a difference”, Bank Securities Journal,
available at: www.internettime.com/Learning/articles/training.pdf (accessed July 15, 2014).
Davenport, T.H. and Harris, J.G. (2007), Competing on analytics: The New Science of Winning, Harvard
Business Press, Boston, MA.
Davenport, T.H., Barth, P. and Bean, R. (2012), “How ‘big data ‘is different”, MIT Sloan Management Review,
Vol. 54 No. 1, pp. 43-6.
PAGE 180
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
VOL. 47 NO. 4 2015
Davenport, T.H., Harris, J.G. and Morison, R. (2010), Analytics at Work: Smarter Decisions, Better Results,
Harvard Business Press.
Department for Business-Innovation and Skills (2013), “Seizing the data opportunity: a strategy for UK data
capability”, available at: www.gov.uk/government/uploads/system/uploads/attachment_data/file/34764/12p120c-guide-to-bis-2012-2013.pdf (accessed August 17, 2014).
Katharaki, M., Prachalias, C., Linardakis, M. and Kioulafas, K. (2009), “Business administration training
seminar for public sector executives: implementation and evaluation”, Industrial and Commercial Training,
Vol. 41 No. 5, pp. 248-57.
Kiron, D. (2013), “Organizational alignment is key to big data success”, MIT Sloan Management Review,
Vol. 54 No. 3, pp. 1-n/a.
Langley, J.C.J. (2014), “2014 Third-Party logistics study: the state of logistics outsourcing”, Capgemini
Consulting, 56pp, avilable at: www.capgemini.com/resource-file-access/resource/pdf/3pl_study_report_
web_version.pdf (accessed August 27, 2014).
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S. and Kruschwitz, N. (2011), “Big data, analytics: and the
path from insights to value”, MIT Sloan Manage. Rev., Vol. 52 No. 2, pp. 21-31.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), “Big data: the
next frontier for innovation, competition (p 9) and productivity”, technical report, McKinsey Global Institute.
Mithas, S., Lee, M.R., Earley, S., Murugesan, S. and Djavanshir, R. (2013), “Leveraging big data and business
analytics”, IT Professional, Vol. 15 No. 6, pp. 18-20.
Provost, F. and Fawcett, T. (2013), “Data science and its relationship to big data and data-driven decision
making”, Big Data, Vol. 1 No. 1, pp. 51-9.
Rao, M.S. (2014), “Enhancing employability in engineering and management students through soft skills”,
Industrial and Commercial Training, Vol. 46 No. 1, pp. 42-8.
Shah, S., Horne, A. and Capellá, J. (2012), “Good data won’t guarantee good decisions”, Harvard Business
Review, Vol. 90 No. 4, pp. 23-5.
Sharma, R., Mithas, S. and Kankanhalli, A. (2014), “Transforming decision-making processes: a research
agenda for understanding the impact of business analytics on organizations”, European Journal of Information
Systems, Vol. 23 No. 4, pp. 433-41.
Tweney, D. (2013), “Walmart scoops up Inkiru to bolster its ‘big data’ capabilities online”, available at: http://
venturebeat.com/2013/06/10/walmart-scoops-up-inkiru-to-bolster-its-big-data-capabilities-online/
(accessed August 11, 2014).
Waller, M.A. and Fawcett, S.E. (2013), “Data science, predictive analytics, and big data: a revolution that will
transform supply chain design and management”, Journal of Business Logistics, Vol. 34 No. 2, pp. 77-84.
Wilkins, J. (2013), “Big data and its impact on manufacturing”, available at: www.dpaonthenet.net/article/
65238/Big-data-and-its-impact-on-manufacturing.aspx (accessed August 13, 2014).
Wixom, B., Yen, B. and Relich, M. (2013), “Maximizing value from business analytics”, MIS Quarterly
Executive, Vol. 12 No. 2, pp. 37-49.
Further reading
Huang, T.C.K., Liu, C.C. and Chang, D.C. (2012), “An empirical investigation of factors influencing the
adoption of data mining tools”, International Journal of Information Management, Vol. 32 No. 3, pp. 257-70.
Lycett, M. (2013), “‘Datafication’: making sense of (big) data in a complex world”, European Journal of
Information Systems, Vol. 22 No. 4, pp. 381-6.
Nagle, T. and Sammon, D. (2014), “Big data: a framework for research”, in Phillips-Wren, G. et al. (Eds),
DSS 2.0-Supporting Decision Making with New Technologies, Vol. 261, IOS Press, pp. 395-400.
Corresponding author
Professor Angappa Gunasekaran can be contacted at: agunasekaran@umassd.edu
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
VOL. 47 NO. 4 2015
j
INDUSTRIAL AND COMMERCIAL TRAINING
j
PAGE 181