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Information systems coupled with internet, cloud computing, mobile devices and Internet of Things have led to massive volumes of data, commonly referred as big data. It includes mix of structured, semi-structured and unstructured real-time data, constituting of data warehouse, OLAP, ETL and information. Business firms and academicians have designed unique ways of tapping value from big data. There is a great scope of using large datasets as an additional input for making decisions. The aim of the paper is to explore the role of big data in these areas for making better decisions. Here we explore how big data can be used to make smart and real-time decisions for improving business results. The paper undergoes literature review and secondary data to provide a conceptual overview of potential opportunities of big data in decision making. The paper discusses the concept of big data, its role in decision making and also the competitive advantage of big data for different firms. The paper also discusses a framework for managing data in decision making. The topic must be addressed for taking better decisions for firms which will contribute to high quality knowledge.
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Vol. 11, No. 1, 2018, pp. 36 - 44
ISSN 1979-3561 | EISSN 2759-9363
Role of Big Data in Decision Making
Shirish Jeble
Faculty of Management
IBS Business School, Pune, India
Email: (Corresponding Author)
Sneha Kumari
Symbiosis Center for Research and Innovation
Symbiosis International University, Pune, India
Yogesh Patil
Symbiosis Center for Research and Innovation
Symbiosis International University, Pune, India
Information systems coupled with internet, cloud
computing, mobile devices and Internet of Things have led to
massive volumes of data, commonly referred as big data. It
includes mix of structured, semi-structured and unstructured
real-time data, constituting of data warehouse, OLAP, ETL and
information. Business firms and academicians have designed
unique ways of tapping value from big data. There is a great
scope of using large datasets as an additional input for making
decisions. The aim of the paper is to explore the role of big data
in these areas for making better decisions. Here we explore how
big data can be used to make smart and real-time decisions for
improving business results. The paper undergoes literature
review and secondary data to provide a conceptual overview of
potential opportunities of big data in decision making. The
paper discusses the concept of big data, its role in decision
making and also the competitive advantage of big data for
different firms. The paper also discusses a framework for
managing data in decision making. The topic must be addressed
for taking better decisions for firms which will contribute to
high quality knowledge.
Keywords: big data, big data analytics, social media analytics,
marketing analytics
Information systems have evolved over the years from
being transactions recording system to supporting business
decisions at different levels. Traditional information systems
depended primarily on internal data sources such as
enterprise resource planning systems (ERPs) for making
business decisions. These datasets were structured and used
relational database management system (RDBMS). These
were used for supporting internal business decisions such as
inventory management, pricing decisions, finding out most
valuable customers, identifying loss making products etc.
Besides, data warehouse was built using this data for analysis
and mining purpose. These data sources were integrated with
data from business partners such as suppliers and customers
using enterprise application integration (EAI) platforms. EAI
enabled seamless integration of information systems
between business partners. It enhanced speed of business to
business transactions (B2B), communication and reduced
cost of inter-company transactions.
In the next wave in early nineties, arrival of internet
further simplified integration of firms with their business
partners. In the last decade, information systems coupled
with internet, cloud computing, mobile devices and Internet
of Things have led to massive volumes of data, commonly
referred as big data. It includes structured, semi-structured
and unstructured real-time data, constituting of data
warehouse, OLAP, ETL and information. Computer science
has advanced to store and process large volumes of diverse
datasets using statistical techniques. Business firms and
academicians have designed unique ways of tapping value
from big data. The objective of this paper is to explore the
role of big data in making better decisions and how big data
can be used to make smart and real-time decisions for
improving business results.
The revolution of big data is more powerful than the
analytics which were used in the past. Using big data helps
managers to make better decisions on the basis of evidences
rather than intuition. Businesses are collecting more data
than required for any use (McAfee et al., 2012); big data
helps in making better predictions and smarter decisions.
Leaders across industries use big data for better managerial
practices. There are several researches conducted in
individual areas such as transactional data, social media data,
supply chain big data etc. However, there is lack of holistic
review of understanding potential of big data for decision
makers. Driven by this need we explore the role of variety of
big data in various decision-making scenarios. This paper
acts as a bridge this gap by achieving the following
a) To explore the existing literature on the fundamental
concepts of big data and its role in decision making
b) To explore role of big data in making strategic, tactical
and operational decisions.
The study is useful for making important decisions with
the help of big data. In the present era, big data has been used
Jeble et al.: Role of Big Data in Decision Making
Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018 37
in many business and educational sectors. This has led to
make better predictions and better decisions.
In the next section, we review extant literature on big
data and how it is gaining significance for business and
society. Here we have reviewed several definitions of big
data from leading big data and analytics professionals. We
also touch upon different ways in which applications of
analytics can be classified. Third section discusses various
applications and benefits of big data. Here we review how
different institutions such as banks or business firms have
been able to collect, analyze and use big data for enhancing
their business performance.
Role of Analytics based decision making using big data
is nothing new for some of the leading companies. However,
there are still many small and medium size companies which
can start taking advantage of this emerging field. In the
fourth section, we present a framework on big data that can
be used by such companies. This framework could be a
starting point to refine the model suitable for their
businesses. Finally, in the last section we conclude our study
with our findings and suggest future research directions.
In this section, we review the extant literature which
provides various ways in which firms are using big data for
analysis and decision making. After defining the objectives
of our research, we identified keywords such as “Big Data’,
‘Big Data and Decision Making’ and ‘Big Data Analytics’.
We searched through research papers in top journals,
conference papers and web sources and shortlisted relevant
papers. Good quality research papers have been selected
through Scopus, Science Direct and Google Scholar
database. The identified keywords have been typed in the
database and papers relevant to the topic have been selected.
Figure 1 shows the number of papers per year published in
various journals.
2.1 What is Big Data?
Big data has been defined in several ways by several
authors. Boyd and Crawford (2012) have defined big data as
cultural, technological and scholarly phenomenon while Fan
et al. (2014) have defined big data as the ocean of
information. According to Kitchin (2014), big data is defined
as huge volume of structured and unstructured data. Waller
& Fawcett (2013) define big data as datasets that are too large
for traditional data processing systems and therefore require
new technologies to process them. Dubey et al. (2015)
describe it as the traditional enterprise machine generated
data and social data. Big data is a term that describes the large
volume of data both structured and unstructured that
inundates a business on a day-to-day basis. But it’s not the
amount of data that’s important. It is what organizations do
with the data that matters. Big data can be analyzed for
insights that lead to better decisions and strategic business
According to Dyche (2014), the concept of big data for
many people is just millions of data which can be analyzed
through technologies. Big data in true sense is the proper use
of data through technologies in any particular aspect. Big
data evolved in the first decade of the 21st century embraced
first by the online and startup firms. A new type of data
voice, text, log files, images and videos have come into
existence (Davenport and Dyche, 2013). The proper use of
big data results in several applications of big data helping in
decision making.
Various techniques and tools enhance the decision-
making ability. Firms such as Amazon, Netflix have
developed algorithms to find out correlation between
customer searches, past purchase history to predict which
products customer is likely to buy. Customers are reminded
about their past searches or recommended products based on
their purchase history. This creates opportunity of customers
buying some of the recommended products thereby boosting
Figure 1 Classification of research papers year wise from top journals
Jeble et al.: Role of Big Data in Decision Making
38 Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018
sales. This technique results in as much as one third of their
new sales (Artun and Levin, 2015). Telecom companies
scrape through massive volumes of data to predict which
customers are most likely to leave them. This helps in
designing policies towards customer retention.
2.2 Five Vs of Big Data
While the term “big data” is relatively new, the act of
gathering and storing large amounts of information for
eventual analysis is ages old. The concept gained momentum
in the early 2000s when industry analyst Doug Laney
articulated the now-mainstream definition of big data as the
three Vs Volume, Velocity and Variety. With further
refinement, big data is now characterized with five V’s as
summarized in Table 1 below.
Table 1 Five V's of big data
Large amount of Data in terabytes or
petabytes has been doubling every
forty months (Davenport, 2014)
Rate of Data accumulation is
increasing in every business or
There are multitude of Data Sources
like enterprise systems, social media,
text, video, audio, email, RFID, web
applications and other digital devices.
Quality of Data is very essential for
the accuracy of decision.
Economic & Social Outcomes can be
improved by obtaining value from the
heterogeneous data.
Reputed data analytics firm SAS considers two
additional dimensions such as Variability and Complexity
when it comes to big data.
Variability - In addition to the increasing velocities and
varieties of data, data flows can be highly inconsistent with
periodic peaks. Is something trending in social media? Daily,
seasonal and event-triggered peak data loads can be
challenging to manage, even more so with unstructured data.
Complexity - Today's data comes from multiple
sources, which makes it difficult to link, match, cleanse and
transform data across systems. However, it’s necessary to
connect and correlate relationships, hierarchies and multiple
data linkages or your data can quickly spiral out of control.
2.3 Different Sources of Big Data
In addition to traditional information systems, big data
originates from different sources such as social network
sites, cloud applications, software, social influencers, Data
warehouse appliances, public, network technologies, legacy
documents, business applications, meteorological data and
sensor data. Few sources are explained below.
A. Transactional data
Transactional data coupled with statistical tools such
as regression analysis and decision tree can help in defining
a model to predict an outcome such as sales forecast or level
of success of a new product launch. The model can take
inputs of past data and predict dependent variable. These
models can easily be created using statistical tools like SPSS
or SAS. All the past data with independent variables is
known as transactions and keeping a track on these
transactions is mainly referred as ‘Transactional Processing
System’. The main purpose of Transaction Processing
System is to capture the information and update the data for
the operational decisions in an organization. There are two
ways to process transactions namely Batch processing which
processes the data as a single unit over a period of time and
Real Time Processing System where data are processed
immediately. Both the methods are helpful for making
operational decisions in any organization.
B. Social media data
Popularity of social media in recent years is leading to
information getting collected at every possible location in the
world. Events are getting reported as they occur. Netizens are
happy to share their views, product or service feedback,
movie reviews within minutes on Facebook, twitter or
WhatsApp. This provides a unique opportunity for decision
makers to gather market intelligence. People share their
information through social media which helps customers to
make purchasing decisions by having a glance at the
feedback, customer complaints and miscellaneous services
provided with a product. Sentiments of the consumers are
also expressed on social media which help companies to
make production decisions. Social media Analytics is also
used for gathering competitive intelligence about the firm’s
product and services offered by the competitors in any
particular market segment. This also promotes new business
ideas for improving the business life cycle. Therefore, the
social media data are very essential in making marketing
decision at strategic, operational and tactical level.
C. Internet Applications
With evolution of internet, millions of users are surfing
through various websites generating high volumes of click
streams, web searches for products or services. There are
numerous online ecommerce websites (such as Amazon,
Flipkart, Alibaba, eBay, Paytm, etc.)
search engines (Google, Yahoo, Bing, etc.) or online banking
applications where millions of users are logging in daily and
using them. During their searches or transactions various
click streams and logs get generated which could be of value.
D. Data from electronic instruments
There are numerous electronic media such as smart
phones, RFID tags, GPS Sensors, machines connected to
networks, scanners, cameras which generate high volumes of
datasets. These are other sources of big data.
2.4 Big Data Analytics
Big data analytics has emerged as an important tool for
supporting managerial decision making. Dyché (2014)
suggests that big data discovery efforts can reveal previously
unknown findings which can result in insights that are
helpful for managerial decision making. Before the invention
of computers, people were limited in their ability to store and
process data. There were experts who used to make decisions
Jeble et al.: Role of Big Data in Decision Making
Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018 39
on the basis of their intuitions. These intuitions were not
always perfect as huge data collection was not possible
(Duan and Xiong, 2015). In the present era, big data has led
to volume, velocity and variety of data. This has made it
easier to analyze the data in terms of statistical reliability and
improvement of models (Chen et al., 2012); Big data
Analytics is used in making decisions in e-commerce, e-
government, politics, science, technology, health, security
and public safety through database segmentation, graph
mining, social network analysis, text analytics, web analytics
and sentiment affect analytics, criminal network analysis,
cyber-attack analysis, multilingual text analysis, health
analytics and patient network analysis.
Multi criteria decision making tool also helps in
decision making in health industry in order to understand the
complete evaluation process by providing a decision support
tool (Venkatesh et al., 2010). RFID are introduced for data
warehouse which could be integrated in terms of logic and
operations (Zhong et al., 2015).
Big data analytics has a significant effect on business
value and firm performance leading to savings, reduce
operating costs, communication costs, increase returns
improve customer relations and developing new business
plan. Big data analytics constitute of advance analytic
techniques to operate big data sets. Advanced Analytics
prepares big data for making intelligent decisions by the
users (Russom, 2011). The analysts compare the historic data
from the data warehouse which leads to making better
decisions. Big data analytics is not just about data volume
but it deals with data variety. According to a research report
by Russom (2011) it has been found that there is a very few
percentage of the population who are aware of the terms like
predictive analytics, advance analytics and big data
analytics. RDBMS, data warehousing, data mining,
clustering, association, OLAP, BPM, ETL, regression,
classification, analysis, genetic algorithm, multivariate
statistical analysis and heuristic research are the tools for big
data analytics. Big data provides a great benefit for making
decisions by providing beneficial data to customers,
providing benefits to the business analytics and specific
analytic application. In spite of the benefits there are few
barriers in the use of big data analytics for making decisions.
These barriers generally include inadequacy of staff for
handling the advanced analytics for decision making, lack of
business support and the problems that frequently arises with
the database software.
2.5 Classification of Analytics
In general analytics can be classified into 3 categories
based on the purpose of use descriptive, predictive and
prescriptive. Descriptive analytics explains a phenomenon
from past data through reports, dashboards, which helps in
understanding what has happened. Predictive analytics helps
us to understand what can happen. It supports predictions
based on past data, correlations between variables and
patterns. Prescriptive analytics is another powerful tool that
supports executive decision making. It helps to understand
different outcomes under different scenarios. It consists of
various tools such as optimization, simulations, what-if-
analysis scenarios with change in input set of parameters.
Managers can make a decision with proper understanding of
expected outcomes and plan contingency in advance.
Data sources play an important role in the way it can be
used for analysis. Analytics can be further classified into
Text, Audio Video, Web or Network analytics based on
source of data. In the following section, we discuss these in
2.5.1 Text Analytics
Document representation, enterprise search system,
search engines, user models, relevance of feedback, query
processing’, billions of searches of customer for a particular
product on google, searches on Amazon’s website provide
indicator of intention to purchase the product by customer.
Amazon, Jet Airways many other ecommerce firms use this
feature to recommend products or flights when next time
customer will be browsing their website, thereby improving
the probability of customer purchase decision.
2.5.2 Audio and Video Analytics
Audio analytics takes seconds to process audio through
technology mainly for safety purpose in any organization and
can track a wide range of sound in the environment. Video
analytics is used to process and analyze videos from variety
of fields and industries. This helps in extracting events
helpful for taking operational decisions.
2.5.3 Web Analytics
Online retailer Amazon uses data mining techniques to
mine the big data such as click streams, web searches, order
history, online etc. to derive intelligence. This intelligence is
used to make decisions about product promotions and it is
working successfully for companies such as Amazon. A
correlation is derived between previous purchase history and
potential new purchase based on similar purchases in the
past. This correlation is used to identify potential customers
and promote different products to these customers using
digital media such as emails, Facebook or by flashing
messages on
2.5.4 Network Analytics
Network analytics provides information about devices
which are connected to network and how they are interacting
with each other. This information helps in designing network
policies, to make actionable decisions that help in improving
business performance and reducing costs.
2.6 Technology for Big Data Analytics
Due to increasing competition in business, there is need
for rapid information and data analysis. Rapid data analysis
results in better understanding and hence leads to better
decision making (Schläfke et al., 2012). Technology is
helping to use analytics for predicting the level of risks for
disease and infection. Shein (2012) reported that big data can
be a great tool in making decisions in medical field. The
hospital collects data from electronic devices that monitor
the premature babies. There is a huge amount of data which
cannot be analyzed by human beings. So, the role of
technology can be seen here. Structured data looks for
patterns that predicts the onset of diseases and reduces the
stay of a person in a hospital. New algorithms can also
correlate a patient’s behavior change to infection.
Jeble et al.: Role of Big Data in Decision Making
40 Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018
In the current era, business executives are challenged
with high expectations from customers, high competition,
rising costs of labor and materials and shorter product
lifecycles. Globalization is blurring the boundaries among
nations. Location and distance from the market are no longer
barriers to access the markets. In such a volatile
environment, firms need to continuously scan for risks and
opportunities and make business decisions quickly based on
available data. In this section, we discuss the role of
traditional “small data” as well as “big data” for making
business decisions.
3.1 Traditional decision support systems
Traditional decision support systems supported internal
business decisions based on data generated by transactions
processing systems such as ERPs (Davenport & Dyché,
2013). Further evolution led to addition of similar systems
on supply and demand side (SRM and CRM). These systems
helped to integrate internal operations of the firm with their
business partners such as suppliers (e.g. Ariba) and
customers (e.g. Siebel). All these systems used well defined
structured data in relational databases. Internal operational
and tactical decisions were made from these decision support
systems (such as how to price the products for optimizing
sales, status inquiry of orders, inventory planning, cost
analysis, outstanding balance payments according to their
due dates etc.). This information helped in accuracy and
speed of internal decisions. Traditional data sources provided
inputs to data warehouse and data mining operations. Overall
architecture included core transaction database, data
warehouse which stores extracted data, classifies that data
into smaller databases. Further data mining tools provide
business intelligence from these datasets. Data mining from
accumulated data helped to analyze and identify patterns,
correlations or association rules (Han et al., 2011).
3.2 Benefits of Using Big Data in Decision
In the last few years, with advent of big data, the
information requirements of executives have changed. In
addition to traditional datasets described above, there are
large datasets coming from variety of sources in structured,
semi-structured or unstructured forms. There are several
ways in which firms can tap value from these datasets to
make strategic, tactical and operational decisions.
Business transaction data when mined for association
rules provide key insights for decision makers about products
bought together or predicting demand for certain items.
Getting an understanding of patterns helps retailers such as
Wal-Mart to redesign their isles and placement of products
together leading to improved sales (Shaw et al., 2001).
Prediction of demand for certain items, helps in improved
planning ahead of major natural disasters like hurricanes
(Shaw et al., 2001). Analysis of terabytes of data coming
from aircraft engine provides indicators of part failures
thereby improving maintenance as well as safety (Dyche,
2014). Table 2 below summarizes, how big data driven
insights lead to information, prediction and actionable
Table 2 Role of big data in making decisions
Big data source
Big data driven Insights
Actionable Decisions
Google search for a
product or brand
Customer intention to buy a
particular product
Identify customer preference for a
particular brand
Predicting demand for product
Google search by
specific key words
What particular information citizens
are looking for or concerned about
Predict spread of flu by geography
by regions
Mayer-Schönberger &
Cukier, 2013
Amazon search
Customer intention to buy a particular
Reminder to customer next time
she/he visits the site leading to
chances of sale website
Amazon Purchase
Using association rules mined from
billions of records, identify which
different products are bought by
Product recommendation
(customer who bought this also
bought) website
Walmart POS data
Using association rules mined
from billions of records, identify
which products customers buy
together (market basket analysis)
Facing disaster such as
hurricanes people buy some
unusual things like pop-tarts etc. in
addition to usual water, batteries,
shovels etc.
Store layouts redesign to place
such products together
Inventory planning based on
buying patterns prior to
disasters such as hurricanes
Waller & Fawcett, 2013
Dyché, 2014
Jeble et al.: Role of Big Data in Decision Making
Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018 41
Table 2 Role of big data in making decisions (Con’t)
Comparative analysis between
competing products
Plan product strategy
Vries et al.,2016
Information about speed, routes,
direction, braking, drive train
Redesign Routes leading to saving
of millions of gallons of fuel
Davenport & Dyché, 2013
Create complete profile for
customer journey
Design future strategies for
improved customer service
Davenport & Dyché, 2013
3.2.1 Understanding Customer Journeys
Leading banks such as Wells Fargo, Bank of America
and Discover get to understand their customer relationship
using big data gathered through variety of sources as
described in Figure 2 below. They create complete profile of
customer journey’s using mix of structured, semi-structured
and unstructured data originating from call center logs,
website clicks, transaction records, ATM transactions,
clickstreams etc. This profile helps them to understand
reasons for customer attrition, correlating journeys with
customer opportunities and problems (Davenport & Dyché,
Figure 2 Customer journey
3.2.2 Competitive Intelligence
There are several studies conducted to understand
consumer sentiments, attitudes and opinions using social
networking sites (SNSs). In addition to consumer sentiment
about their own products, business executives need to know
what customers think about competitor’s products. This
intelligence will help to plan innovations in future products
or design a strategy to market the products. Mining of a
social media data can obtain comparative analysis of
consumer opinions and sales performance of a business and
its competitors.
Similarly, trends analysis provided by google provides
a good mechanism of comparing product searches of two or
more competing products. These analytics provides insights
on how different products, services or persons are being
searched over the web in different geographies. This can
provide valuable intelligence regarding product awareness
and designing future marketing strategies or new product
launches. There are websites such as
which provide glimpse of possible ways in which this
intelligence can be tapped.
3.2.3 Cost and Time Reduction
There are numerous opportunities of cost and time
reduction using big data. Big data technologies such as
Hadoop clusters are emerging as significantly low-cost
option compared to traditional databases. It can play a role in
real time decisions regarding promoting offers and services
to customers based on their current locations. UPS saves
millions of dollars in fuel by collecting, analyzing data from
telematics sensors installed on its 46,000 vehicles and
redesigning its vehicle routes using this large dataset
(Davenport & Dyché, 2013).
3.2.4 Optimization and Simulations of Supply Chains
Supply chains are getting increasingly complex with
multitude of suppliers and business partners. Over last two
decades, members of supply chain have implemented
enterprise systems which record every transaction. With
advancement of EAI information sharing happens between
business partners such as suppliers and customers. For
efficient movement of goods across supply chains,
technology plays an important role. Scanning devices such
as sensors and RFID, location tracking devices like GPS,
video recordings etc. all these churn large volumes of data
with inventory movement. Supply chain analytics enhances
capability of decision makers by getting an integrated view
of the data within supply chain. We can extract, transform,
analyze data from data sources within supply chain system
and run analytics to derive intelligence. Supply chain
analytics provide several advanced capabilities such as
dashboards, pattern and trend analysis, drill down views,
forecasts, knowledge base, scenario and what-if analysis,
simulations and optimization capabilities. These enhance
decision making capabilities and interpretations of situations
which is very crucial for firms in competitive business
environments (Nair, 2012).
3.2.5 Predicting Future Outcomes
There are several opportunities of using datasets for
predicting future outcomes. Analytics frameworks can be
developed to analyze different datasets and make predictions
as listed below-
a) Based on historical transactional data, using forecasting
models such as regression predict future sales for the
product or services for a firm.
b) Based on correlations found in historical purchases,
identify products purchased together by customers.
Referring to these correlations and purchase history of
a customer, predict which products a customer is most
likely to buy and make online recommendations.
(Artun & Levin, 2015)
Profile of Customer Journeys
Call center
Jeble et al.: Role of Big Data in Decision Making
42 Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018
Figure 3 Conceptual framework on big data and decision making
Analyze historical data, study patterns of customer
attrition from the past data and identify which variables
contribute to customer attrition. Predict which customers are
most likely to leave, take proactive steps to retain them.
(Artun & Levin, 2015)
There are many more applications of predictive
analytics such as predicting which customers are most likely
to buy which products based on their historical purchases.
Also, based on Google searches, outbreak of diseases in
certain geographic locations can be predicted.
3.2.6 Real-time Decision Making
Several visionary companies have developed real-time
decision-making capabilities using supply and demand side
information. Using analytics, they have online real-time
decision-making capabilities that cannot be matched by
traditional business models. Transport service providers such
as Uber uses big data for real time routing of cars to minimize
pick up times and optimize customer experience of a ride
(Woodie, 2015). Ola and Uber provide real-time information
to both customer and cab driver on Google map. They
receive continuous stream of high volume cab demand data
and availability of cabs in different geographic areas. They
come up with demand management strategies based on real-
time demand information. City of Singapore has recently
introduced a demand driven, shared private transportation
concept enabled by data analytics and mobile technology
called as Beeline. This system uses crowd sourced travel
patterns, transportation data to identify potential travel routes
and dynamically allocates buses to routes based on demand
patterns. This reduces travel time for commuters and
increase use of shared transport services (Askari, 2015).
In this section, we describe conceptual framework for
firms which would like to develop analytics practices for
their business to support business decisions in different
areas. Driven by the objectives and the literature, conceptual
framework in Figure 4 has been designed. As described in
earlier section, several leading companies are practicing
analytics in one or more aspects of their business. There are
still several small and medium size firms which have yet to
adopt analytics for gaining competitive advantage. We have
developed a framework for such firms to bring analytics into
their mainstream business practice. We recommend that once
analytics practice is established in the company, this function
can be headed by a senior executive. This will ensure that
critical insights gained from data analysis are used for
decision making. Further, analytics can be incorporated in
performance measurement system such as balanced
scorecard, real-time dashboard or KPIs.
From the literature review, authors have explored
different ways in which big data plays critical role in
analytics which in turn provide insights for decision making.
Figure 3 clearly depicts the path from big data to decision
making. The constructs have been discussed in detail below.
The conceptual framework shown in Figure 3 depicts a
relationship between five constructs.
4.1 Develop data sources
Data sources include traditional data sources such as
enterprise systems, customer data, supplier data, social
media data, logistics trajectory etc. As a first step, firm needs
to have information systems infrastructure and processes in
place to collect data through variety of sources based on its
business model. For example, a products company focusing
on designing new products and assembling them will collect
data related to supply, distribution and logistics. A logistics
company will collect data related to its fleet movement,
packages and routes etc.
4.2 Data Mining
Data mining is a process of discovering patterns in large
datasets using various statistical techniques, computer
programs and database systems. It helps in getting
meaningful information from the data. Once data sources are
established, based on analytics requirements from different
departments, data warehouse will be developed to store
multi-dimensional data for query and analysis purpose.
Mining these datasets helps in finding previously unknown
patterns, correlations and association between different
4.3 Data Analysis
Having large volumes of variety of datasets are
necessary but not sufficient. Firm needs to develop capability
in analytics to get insights from the data. Firm needs to
develop a team of analytics professionals with several
interdisciplinary skills in the team- a) knowledge and
experience in statistics tools such as R software, SPSS, SAS
etc. b) programming knowledge c) domain knowledge about
the business processes d) data management skills in SQL e)
experience in data analysis. With help of domain knowledge
and data analysis various opportunities of analytics projects
can be identified.
4.4 Analytics
As described earlier, firms can take advantage of three
types of analytics descriptive, predictive and prescriptive.
Firm can develop their requirements based on opportunities
of acquiring new customers, retention of existing customers
or identifying risks for its business. Business analytics uses
Mining Data
Analysis Analytics Decisions
Jeble et al.: Role of Big Data in Decision Making
Operations and Supply Chain Management 11(1) pp. 36 - 44 © 2018 43
a set of principles, statistical tools and computer algorithms
to extract knowledge from the data. IBM, SAP, Oracle, SAS,
SPSS and R software have already developed tools for data
analysis and model development. Hadoop provides
framework for developing model from different data sources.
4.5 Decision Making
Business analytics is emerging as a potential tool for
enterprises to improve their business performance in terms
of customer service, customer retention and acquisition.
Predictive analytics helps us to predict what can happen
based on certain available information. This gives a
competitive advantage for a firm to plan ahead. Patterns from
data, correlations and associations are helpful for improving
sales performance, identifying right customers for products
or segmenting markets. Analytics has applications in every
domain where data can be collected. Supply chain analytics
are available in the areas of inventory optimization,
procurement planning, demand forecasting, fleet and route
sizing and optimization (Nair, 2012). Social media data
provides competitive intelligence, new product ideas and
reviews of existing product crucial information helpful to
decide next strategies (Bell, 2012). Big data discovery can
provide startling and highly actionable findings (Dyche,
2014). Main goal of data science is to improve ability of
managers to make better business decisions (Provost and
Fawcett, 2013). Leading companies such as Amazon, Wal-
Mart, Google or Netflix have mastered the art of using data
and analytics as a tool for predictions, simulations or
sometimes for just getting insights. Amazon and Wal-Mart
use analytics for decision making in all aspects of conducting
their business right from generating demand to managing
their supply chains efficiently.
We have come a long way since information revolution
has changed the way business firms work. Big data is helping
firms to get competitive advantage using different analytics
techniques. These techniques help us to get insights, patterns,
correlations and associations which could not be understood
through traditional small data. These support decisions
making process for business executives with the help of
social media data, competitive intelligence, cost and time
reduction strategies, supply chain analytics, web analytics
etc. Firms which recognize significance of big data and
developing products around data have received huge
dividends in recent years. Many firms use analytics in almost
all aspects of conducting their business to reap the benefits
of analytics based decision making. In this paper, we present
a conceptual framework for developing analytics capabilities
and how this emerging knowledge can be help small and
medium firms to compete using lesser resources. It can be
adopted by such companies with changes in line with their
business domain and model. This framework can be a
starting point for further analysis, enhancement and future
research opportunities.
With continued digitization of every aspect of society
as well as business, pace of generation of high speed high
volume data is going to continue. This provides a sound
opportunity to exploit the field of analytics for decision
making in different business domains. There are several
unique research opportunities in different business, scientific
and government domains wherever data is generated
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Shirish Jeble is currently a faculty at IBS Business School, Pune, India with the Department of IT and Operations. He holds
a Masters in Management from University of Mumbai and is currently a PhD scholar at the Symbiosis International University,
India. He has over 20 three years’ experience that includes both industry and academics. He has over five years of experience
of PG teaching and research experience in the area of operations management, supply chain management, lean, information
technology and business analytics. His research interests include big data and sustainable business development.
Sneha Kumari is a Junior Research Fellow at Symbiosis International University. She has completed her graduation in
Agriculture under Indian Council of Agriculture Research fellowship and her Masters in Agribusiness Management under
Indian council of Agriculture Research merit. Currently she is pursuing PhD from Symbiosis International University. She has
published few research papers in the area of agriculture, sustainability, technology, climate change and attended several
national and international conferences.
Yogesh Patil is Professor and Head Research Publications at Symbiosis Centre for Research and Innovation, Symbiosis
International University, Pune, India. He holds Master and Doctorate from University of Pune, India and has over 16 years of
PG teaching and research experience in the area of environmental science, management and technology. His research interests
include waste management, sustainability, climate change, industrial ecology and reverse logistics. He has published over 50
research papers/chapters and successfully completed projects funded by UGC, IFS, Sweden; OPCW, The Netherlands and
World Bank. He has received several awards and fellowships.
... These data sets are organized and used for management systems through relational databases (RDBMS). These have been used to support internal business decisions such as inventory management, pricing decisions, discovering the most valuable customers, identifying lost products, etc. (AlTaweel & Al-Hawary, 2021;Al-Hawary & Al-Syasneh, 2020;Jeble, Kumari & Patil, 2018). ...
... These significant effects, along with the observed strong positive relationship between data analytics efficiency and decisionmaking performance, indicate that improvement in data analytics efficiency indicators will improve the company's decisionmaking performance. (Jeble et al., 2018) showed that big data helps companies gain a competitive advantage by using various analysis techniques, and these techniques help in obtaining insights, patterns, and correlations that cannot be understood through traditional small data. supporting business executives' decision-making with the help of social media data, competitive intelligence, cost and time reduction strategies, supply chain analytics, web analytics, etc. Companies who understand the importance of big data and product development around it have earned huge profits in recent years. ...
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The aim of the research is to test the impact of big data on the quality of decision making. The necessary data was collected from telecommunication companies operating in Jordan. Three companies provide telecommunications services in Jordan: Zain, Umniah, and Orange. Non-probability purposive sampling was used. Hence, the study instrument was distributed to managers at the senior and middle levels of these companies. The structured equation modeling (SEM) technique was employed to test the study hypotheses. The results confirmed that big data impacts decision quality. Besides, it was determined that the highest impact was on velocity. The researchers suggest that Jordanian telecom companies invest in information technology that allows them to provide data with accuracy, reliability, and high quality, as well as improve the qualifications of their employees.
... Queste analisi avanzate rendono i BD fruibili alla direzione aziendale supportandola nelle scelte strategiche. I BDA possono avere un effetto significativo sulle performance aziendali, favorendo la riduzione dei costi operativi e dei costi di comunicazione e, al contempo, agevolando lo sviluppo di nuovi prodotti, il miglioramento delle performance di mercato e dei piani strategici (Jeble et al. 2018). ...
... Utilizzando gli strumenti basati sui BDA, le aziende possono identificare efficacemente nuove opportunità, intercettare le eventuali minacce e attuare le migliori scelte strategiche (Özemre e Kabadurmus, 2020). Jeble et al. (2018) esplorano il ruolo dei BD nell'ambito dei processi decisionali e valutano come i BD possono essere impiegati per prendere le migliori decisioni in tempo reale e conseguire un effettivo vantaggio competitivo. Nell'analisi condotta, gli studiosi attribuiscono ai BDA la capacità di supportare efficacemente il processo decisionale, data la possibilità di avvalersi di una mole significativa di dati acquisiti nei social media e nel web e di realizzare strategie di mercato più competitive. ...
... IoT is well known for increasing efficiency, pro-ductivity, safety, convenience, and response time and resolving labour shortages in various logistics and warehouse operations while cutting costs and positively influencing the environment. For example, the order picking procedure accounts for 50-55 per cent of overall warehouse operating expenses (Jeble et al., 2018;Nagda et al., 2019) and largely determines warehouse efficiency. IoT can make the picking procedure efficient by enhancing real-time visibility and traceability in warehouses (Affia & Aamer, 2021). ...
The introduction of Industry 4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and others has revolutionised the traditional warehousing and logistics industry, resulting in significant changes to various operations and decision-making. Despite the undeniable importance of this topic, the primary research on the impact of IoT technology is inconsistent and scattered. The present study aims to review state-of-art literature on the application of IoT technology in the warehousing and logistics field and suggests a path for the future research through an in-depth analysis of studies done in this area. Sixty-four research articles were carefully selected after a thorough search of the Scopus and EBSCO databases, covering the period from January 2011 to 07th December 2021, to examine the applications of the IoT in the warehousing and logistics business. These articles were thoroughly reviewed and classified in terms of year-wise distribution, major publication outlets, types of study, and highly cited papers to understand the evolution and ongoing trends in this field. The findings reveal that majority of the studies on IoT in the warehousing and logistics domain have been conducted in developed countries. While logistics has been widely investigated, studies on the warehousing domain are limited. Also, there is an under-presentation of various theories in IoT research. The study highlights various gaps by synthesising existing literature and provides a fertile ground for conducting future research in this domain. Supply chain practitioners and researchers will find this review timely and valuable, which offers several valuable implications for them.
... Such patterns may be practised by discussing the design of information systems that support such pattern. (Jeble et al., 2018) Massive amounts of data, known to as "big data," have been generated as a result of the integration of an information systems with the internet, the cloud computing, the mobile devices, and the Internet of Things. OLAP as well as ETL are all part of the data warehouse, which contains a variety of data types, including the structured, the semi-structured, as well as the unstructured data. ...
The Decision-making based on solid facts as well as intelligence is referred to as the strategic data-based decision making. Unlike intuition-based decision making, in which judgments are made based on unsubstantiated opinions about the company and also its external environment, one such approach is based on facts and data. Data-driven strategic decision-making is indeed a popular recommendation for boosting efficiency as well as competitiveness, as well as ensuring responsibility to the public and shareholders. Strategic decision-making based on the data necessitates extensive data collecting and impartial analysis. Data-driven organisations are attempting to improve their decision-making abilities in the current age of big data. There is, meanwhile, a dearth of studies examining the capabilities of the (DDDM) data-driven decision making. There are four methods to defining capabilities: an unrelated, an unidirectional, entangled and mixed. DDDM capacity is then defined as a multi-dimensional construct using the procedure entanglement method.
... Big data analytics help enterprises understand their business environments, customers' behavior and needs, and their competitors' activities (Vassakis, Petrakis & Kopanakis, 2018). Specifically, big data obtain insights, patterns, correlations, and associations, which could not be understood through traditional small data (Jeble & Patil, 2018). Big data improves existing capabilities, for fault detection, predictive maintenance (Moyne & Iskandar, 2017), and automating decision-making tasks (Müller, Fay & vom Brocke, 2018). ...
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Purpose: Although the decision to adopt Industry 4.0 is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization. Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC), and technique for order performance by similarity to ideal solution (TOPSIS). The top management discussed and rendered insights into the results. Findings: Results show that the high-mix and labor-intensive plant (Plant 1) has the highest urgency, whereas the largely automated plant (Plant 3) has the lowest urgency to adopt the nine pillars. The findings provide empirical evidence of the effect of the recent Industry 4.0 awareness programs in Plant 1 and advanced infrastructure would lead to organization inertia (Plant 3) to aggressively pursue technological change. The most urgent pillar is cybersecurity, and the least urgent pillar is additive manufacturing (AM), outlining the concern over cyber threats when product information is increasingly integrated into the supply chain and technology immaturity of AM in production. Research limitations/implications: A limitation of this study is that the comparative survey only focused on three plants and the tactical level management of an organization. Originality/value: This study contributes to the knowledge of Industry 4.0 readiness by being the first to show different levels in the sense of urgency of the tactical level managements on the relevant technologies, which potentially affect the direction and the pace of Industry 4.0 adoption.
... e goal is to optimize energy consumption and network bandwidth consumption and try to reduce the network through graph segmentation flow. Based on the improved particle swarm algorithm, Jeble et al. [14] proposed an energy-aware virtual machine placement algorithm. In addition to the optimization goals of energy consumption and network consumption, studies have also considered factors such as the reliability, quality of service, and security of virtual machine placement. ...
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Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. The main functions in the following aspects are realized: first, we built the website back-end delivery subsystem and data collection and analysis subsystem to realize the control of website delivery and data collection; second, we designed and developed a process management subsystem for booking, management, and delivery of website resources and developed the contract management and order management subsystems to realize the accurate placement of user portraits on the website; then, the placement data monitoring and effect feedback subsystems, and the data inventory subsystem of the website system were designed and developed. Finally, based on the research of Android and based on the Eclipse platform, this article has completed the construction of the Android 4.0.3 version environment, successfully used the Java development language to develop a website information intelligent analysis and navigation system, and analyzed the various functional modules of the entire system. Experimental results show that the system not only realizes ordinary route and site query functions but also combines map API, integrates big data and cloud computing technology, and realizes congestion avoidance query, time optimal query, population heat map, and real-time viewing. The nearby use of this software’s personnel density distribution and other functions provides great convenience for personal travel, which can facilitate the real-time planning of travel plans, and has great practical and practical significance. Through the different levels of testing of various subsystems, the website delivery system meets the functional and nonfunctional requirements proposed by the network and on this basis realizes the use of group wisdom based on Pearson correlation coefficient, Cosine similarity, and Tanimoto coefficient for collaborative filtering website recommendation algorithm. 1. Introduction In recent years, cloud computing technology has developed rapidly, and virtualization technology, as the key technology of cloud computing, has always been one of the hot research issues [1]. Virtualization technology includes multiple aspects, such as network virtualization, virtual machine placement, and virtual machine migration. This paper mainly focuses on the related issues of virtual machine placement in data centers. The online website delivery system is developed with the rapid development and gradual improvement of the online website market and big data technology. It aims to help website vendors and online video websites achieve professional and accurate placement and page-based website targeting. System software is for tasks such as delivery process management and website operation data statistics [2–5]. As a leading video sharing website, online websites have played more than tens of millions of times a day. The company built an online website delivery management system with the purpose of using the cloud computing platform to strictly control the website delivery process to ensure accurate delivery of the website. Under the premise, at the same time, it can integrate the company’s existing sales, product, and technical teams to form a reliable, diverse, and customizable website sales program [6–8]. The website system can effectively optimize the website delivery plan and automatically adjust the delivery parameters during the delivery process, ensuring the most effective use of the website resources from the technical level. In a cloud data center, in order to improve resource utilization, a scheme of placing multiple virtual machines on the same physical server is usually adopted, and the placement of virtual machines will consider many factors, including reliability, energy consumption, and network resources. Consumption and a reasonable and efficient virtual machine placement algorithm can improve the operation efficiency of the data center and save operation and maintenance costs. In the existing research, most of the virtual machine placement algorithms use traditional algorithms or heuristic algorithms [9–11]. Some researchers apply machine learning technology to the virtual machine placement problem, but most of them are just as auxiliary strategies, such as using machine learning technology for demand forecasting and so on. There are few algorithms that directly apply machine learning technology to decision-making. Yao et al. [12] proposed an energy-aware virtual machine placement algorithm based on the hybrid genetic algorithm, which comprehensively considers the energy consumption of physical servers in the data center and the resource consumption of network communication. Based on tabu search, Singh et al. [13] proposed a network-aware virtual machine placement algorithm and a heuristic virtual machine placement algorithm based on convex optimization. The goal is to optimize energy consumption and network bandwidth consumption and try to reduce the network through graph segmentation flow. Based on the improved particle swarm algorithm, Jeble et al. [14] proposed an energy-aware virtual machine placement algorithm. In addition to the optimization goals of energy consumption and network consumption, studies have also considered factors such as the reliability, quality of service, and security of virtual machine placement. Wu et al. [15] proposed a reliability-aware virtual machine placement algorithm. The algorithm is based on network topology, selects a series of available servers from candidate server clusters, then selects an optimization strategy based on K-Fault-Tolerance Guarantee, places the master and slave virtual machines on the selected available servers, and finally uses a heuristic strategy to solve the problem of remapping and optimization of tasks to virtual machines. Rehman et al. [16] added the consideration of load balancing and proposed a virtual machine placement algorithm based on energy consumption and service quality perception. Scholars proposed a virtual machine placement algorithm based on the particle swarm optimization algorithm. Unlike other studies, this paper considers the heterogeneity of physical servers and puts forward a corresponding virtual machine placement strategy while taking into account energy consumption and ensuring global QoS [17–19]. Focusing on the security issues in virtual machine placement, in response to attacks on virtual machines that have occurred in recent years, researchers have proposed a security-aware multiobjective optimization virtual machine placement algorithm to reduce the security risk of data centers in the cloud environment. At the same time, the utilization efficiency of CPU, memory, disk storage resources, and network bandwidth is considered. Due to the rapid development of 5G technology in recent years, some scholars have considered the issue of virtual machine placement in the mobile edge network environment. During the operation of the virtual machine, resource requirements are constantly changing, so some researchers have proposed a placement algorithm based on the demand forecast model for the dynamic changes of demand [20–22]. This system is distributed on a large number of distributed computers instead of local computers or remote servers. At the same time, the mechanism has greatly reduced the management cost of the website system data center, but the improvement of the system resource utilization rate has greatly guaranteed the overall availability of the website system, and the reliability of the system has also been greatly improved. On the other hand, cloud storage can ensure that the core data of the system are stored at multiple points, and the cold and hot backups are automatically switched, which reduces the probability of affecting the entire system due to a single-node failure. This paper also presents an algorithm improvement plan based on the multimodel decision-making. Decisions are made by training multiple models at the same time, and the optimal strategy is selected from all decisions as the final decision. This paper selects the classic FFD algorithm and the adopted algorithm as the comparison algorithm and evaluates the performance of the proposed algorithm model. The experimental data show that the performance of the algorithm can meet expectations, and the improved algorithm based on the multimodel decision mechanism has obtained better performance. 2. Construction of an Intelligent Analysis Model for Website Information 2.1. Hierarchical Architecture of Big Data Space In the big data space level, the user-defined map function accepts input key-value pairs. After completing the necessary data calculations, a set of intermediate key-value pairs will be generated. The framework class library will all have the intermediate value data with the same key name. It aggregated together and then passed to the reduction function through an iterator. The user-defined reduction function accepts the intermediate key name and the value set associated with it, and appropriately simplifies or merges these values to form a smaller value set [23]. Figure 1 shows the hierarchical architecture of the big data space.
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Big Data und künstliche Intelligenz verändern Volkswirtschaften und soziale Beziehungen. Je mehr Informationen ein Unternehmen über einen Kunden hat, desto einfacher ist es für dieses, Geschäfte zu machen. In dieser Arbeit wird auf die Bedeutung von Big Data in Entscheidungsprozessen und die Essenz integrierten Datenmanagements in Unternehmen verwiesen. Information stellt einen Wirtschaftsfaktor dar. Ein Großteil des Mehrwerts, den Unternehmen bieten, stammt aus ihren Daten, die sie ständig analysieren, um mehr Effizienz zu erzielen und neue Produkte zu entwickeln. Das Ziel der Sammlung aller dieser Daten ist die Erschließung von Wissen. Als kritischer Erfolgsfaktor werden die entscheidenden Elemente bezeichnet, die zum Erfolg einer Organisation beitragen. Big-Data-Initiativen sind von entscheidender Bedeutung, um die traditionelle Entscheidungsfindung im Unternehmen in eine datengesteuerte Entscheidungsfindung umzuwandeln.
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Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures become an essential aspect of helping to better understand the contributions of predictors to the built model. In this work, we focus on the study of using generalized linear models (GLM) for the size of loss distributions. In addition, we address the problem of measuring the importance of the variables used in the GLM to further evaluate their potential impact on insurance pricing. In this regard, we propose to shift the focus from variable importance measures of factor levels to factors themselves and to develop variable importance measures for factors included in the model. Therefore, this work is exclusively for modeling with categorical variables as predictors. This work contributes to the further development of GLM modeling to make it even more practical due to this added value. This study also aims to provide benchmark estimates to allow for the regulation of insurance rates using GLM from the variable importance aspect.
How is it possible to systematically develop business model innovations for different domains? This paper provides a novel answer, using a methodological approach called Business Model Matrix (BMM), which merges the approaches of Business Model Canvas (BMC), Business Model Navigator (BMN), and Morphological Box.
Applying a business model approach, this chapter identifies various challenges in digital platform and platform-based business model development in the case of a physical port ecosystem. Using an empirical case, the chapter identifies the prerequisites and consequences of opportunities, value, and advantages for an existing ecosystem that aims to create a “digital twin.” It contributes to academic discussions on the intersection of ecosystems, platforms, and business models by exploring the antecedents and controversies of configuring ecosystem boundaries in a digital context. Moreover, the chapter contributes to research by analyzing how a previously closed ecosystem seeks to open its boundaries and interfaces, both internally among the internal ecosystem members and externally to the outside business environment.
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Big data has emerged as an important area of interest pertaining to the study and research of practitioners and academicians. The exponential growth of data is fuelled by the exponential growth of various internet and digital devices. Advancement in technology has made it economically feasible to store and analyse huge amounts of data. Big data is a juxtaposition of structured, semi-structured and unstructured real time data originating from a variety of sources. Predictive analytics provides the methodology in tapping intelligence from large data sets. Many visionary companies such as Google, Amazon, etc. have realised the potential of big data and analytics in gaining competitive advantage. These techniques provide several opportunities like discovering patterns or better optimisation algorithms. Managing and analysing big data also constitutes a few challenges – namely size, quality, reliability and completeness of data. This paper provides an extensive review of literature on big data and predictive analytics. It gives the reader details of the fundamental concepts in this emerging field. Finally, we conclude with the findings of our study and have outlined future research directions in this field.
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Purpose – The purpose of this paper is to offer a theory-driven, evidence-based approach to developing a brand’s messaging strategies on social media encompassing three messaging dimensions, namely, appeal, content, and richness. Design/methodology/approach – Using longitudinal data from three Fortune 200 companies – Delta Airlines, Wal-Mart, and McDonald’s – the authors empirically investigate comprehensive strategic messaging framework. Using ANOVAs and regression analyses, the authors test a set of hypotheses regarding the relations between a brand ' s purchase involvement, its message appeal, message content, and message richness, and engagement. Findings – Findings reveal significant relations between purchase involvement and appeal. Furthermore, the authors find that abstract content categories are best combined with richer media. Finally, both transformation appeal and richer media have a highly significant and positive effect on engagement. Research limitations/implications – The authors offer a theoretical ground and empirical validation of both a comprehensive typology of content categories and a holistic strategic messaging framework that can fill a significant void in the social media marketing literature that lacks integrative models for assessing, classifying, analyzing, and in turn, informing future social media marketing strategies. Practical implications – The validated framework can help managers better understand the diversity of messaging components as well as offer an analytical tool for assessing the nature of engagement associated with each appeal and category. Originality/value – To the best of the author’s knowledge, this paper offers the first comprehensive typology of content categories and validates it in the context of a strategic messages framework using real-world data finding strong support for all hypotheses.
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Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.
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Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
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The purpose of our paper is to select best suppliers for blood bags using TOPSIS tool. In our study, we have recognised the importance of blood bag suppliers who are critical to healthcare industry. We have developed a framework on the basis of criteria identified through literature review and expert opinion. In our study, we have used multi criteria decision making (MCDM) tool to select suppliers which are supported by various scholars, when decision is based on subjective and objective criteria. In our study, we propose a fuzzy hierarchical TOPSIS approach to support such an assessment. It enables decision makers in health industry to better understand the complete evaluation process and provide a more accurate, effective and systematic decision support tool.
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The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamouring for access to the massive quantities of information produced by and about people, things, and their interactions. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what 'research' means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
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Big data (BD) has attracted increasing attention from both academics and practitioners. This paper aims at illustrating the role of Big Data analytics in supporting world-class sustainable manufacturing (WCSM). Using an extensive literature review to identify different factors that enable the achievement of WCSM through BD and 405 usable responses from senior managers gathered through social networking sites (SNS), we propose a conceptual framework using constructs obtained using reduction of gathered data that summarizes this role, test this framework using data which is heterogeneous, diverse, voluminous, and possess high velocity, and highlight the importance for academia and practice. Finally we conclude our research findings and further outlined future research directions.
Purpose – The purpose of this article is to provide an interview with the former web analytics guru, Marshall Sponder. Design/methodology/approach – The paper provides an interview with Marshall Sponder, author of Social Media Analytics. Findings – This special issue concludes with an interview with web analytics guru, Marshall Sponder. For over a decade, Marshall has been at the forefront of this growing field, influencing and advising large companies on how to determine ROI from their web‐based activity. His book, Social Media Analytics, proffers strategies for getting the most out of social media, as well as predicting the direction this field is likely to take in future, something Sponder is perfectly placed to do. In this interview he discusses at length the importance of a tactical approach to social media, the tools needed for measuring ROI, and emerging trends in social media. Originality/value – The interview provides an insightful look at the emerging trends in social media analytics from the leading analyst in the field.