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Call for Papers
24th European Conference on Information Systems (ECIS 2016), 12-15 June,
2016, Istanbul/Turkey
Track: Big Data, Business Analytics and Decision Support
Submission Deadline: November 27th, 2015
Track Description
With the emergence of Big Data and Analytics in recent years, business
decision-makers and government policy-makers are increasingly turning to
Big Data and Analytics for insight to solve complex problems. Needless to
say, in this day and age, the success of businesses relies heavily on the
accuracy and the timeliness of the decisions made by their managers. Often
called evidence based management, which is driven by Big Data and
Analytics, has much to offer to businesses and government agencies to
succeed in their challenging endeavours.
In addition to its promises, Big Data also raises new challenges for both
practitioners and academics. Big Data is not just “big”. The sheer volume
of the data is only one of many challenges that are often associated with
Big Data, the others include variety, velocity, veracity, variability, and
value proposition, among others (Jagadish et al., 2014; McAfee &
Brynjolfsson, 2012; O'Leary, 2013; Sathi, 2012). While advances in hardware
and software are helping, there still are many more challenges to
characterize and to tackle in order to realistically leverage the promises
of Big Data and Analytics to solve complex real-world problems.
Big Data by itself, regardless of the size, type, or accumulation speed, is
worthless unless business users do something with it that delivers value to
their organizations. That’s where “big” analytics comes into the picture.
Although organizations have long run reports and dashboards against their
transactional data repositories (under the names of Business Intelligence
and Data Warehousing), most have not utilized unstructured (or less
structured) data repositories for in-depth and on-demand explorations. This
is partly because analysis tools either did not have the capabilities or
the data repositories did not have the richness that the power users
needed. But this phenomenon is about to change (and had already changed for
some) in a dramatic fashion; thanks to the new Big Data and Analytics
paradigms.
This track invites paper submissions that address acquisition, storage,
analysis, inference of Big Data and its implication/incorporation into
business process, behaviours, and decision-making practices. We are
particularly interested in those philosophical approaches, research
methodologies, and managerial/practical implications/applications that
provide novel insight into Big Data and Analytics as they relate to
managerial decision making. This research track encourages submission of
new and novel theoretical, applied, pedagogical, behavioural, design
science, data analysis methods/methodologies, as well as best practice that
focus on data mining, text mining, statistical analysis, econometrics, Web
mining, social analytics and sentiment analysis.
Topics of interest include, but are not limited to:
• Big data and emerging research philosophies and methodologies
• Big data and new individual and group decision-making techniques and
opportunities
• The impact of big data on the traditionally prominent methodological
approaches to IS studies
• The classic philosophical/methodological approaches and the emerging
field of big data
• How the methodologies, analytical approaches and techniques may vary
depending on the business decision-making levels; i.e. strategic, tactical,
and operational
• Social networks and big data
• Modelling big data
• How business strategy-formulating and government policy-making can
benefit from big data
• Big data velocity and decision-making
• Big data and its impact on DSSs, Expert systems (new forms such as
recommender systems), etc.
• Implications of big data in making strategic, tactical, and
operational decisions
• Real time analysis
• Implications for decision-making at the individual and organizational
levels
• Querying and interpreting unstructured data; such as text analytics
and sentiment analysis
• Integral real time analysis of structured and unstructured data
• Integral visualization of structured and unstructured data
• Use of big data and business models
• Developing, using, managing decision support systems
Track Link: http://ecis2016.eu/files/downloads/Tracks/T02.pdf
Submission Link: http://precisionconference.com/~ecis
Track Chairs
Nazim Taskin, School of Management, Massey University, New Zealand,
n.taskin@massey.ac.nz
Dursun Delen, Spears School of Business, Oklahoma State University, USA,
dursun.delen@okstate.edu
Ali Intezari, School of Management, Massey University, New Zealand,
a.intezari@massey.ac.nz
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