Role of Predictive Analytics in Information System Research

Running Head: PROACTIVE ANALYTICS 0
Predictive analytics

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Introduction
Predictive analytics is an advanced statistics that is used to make predictions regarding
unseen and unknown future events. In every organization, there are different predictive
analytics tools such as statistics, data mining, modelling. Organizations collect the
information from various sources. Predictive analytics allows the organization to grab those
information and predict them which might coke in the future. It makes the organization to
become forward looking, proactive as well helps in anticipating the outcomes. In every type
of industry whether it is healthcare, retail or hospitality industry, information systems are
used to store the collect and store the information (Gandomi and Haider, 2015). It is most
helpful in the fast changing environment that poses various challenges for the psychological,
economic as well as other theoretical models. It can detect new behaviour and patterns
leading the development of theoretical model. Therefore, predictive analytics plays an
important role in the information system in various aspects. By taking the understanding of
various authors on information system research, discussion is carried on in the report.

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Role of predictive analytics in information system research
In the paper, the need of predictive analytics into information system is highlighted and it
depicts several ways through which goals could be accomplished. Predictive analytics covers
various methods like statistical and others through which data could be predicated and it
includes ways through which predictive power could be assessed. It was found that predictive
analytics is not only used for practically predicting the outcomes but they also play a
significant role in building theories and testing the figures. There are various roles that could
be undertaken in predictive analytics like generation of new theories, measurement
development, analysing and comparing the present theories, improvement I the existing roles
and assessment of all the model so that relevant changes could be made (Stefanovic, 2014).
In information system predictive analytics relies exclusively on statistical modelling were
they analyse and evaluate the power of casual model so that predictive power could be used
(Pearlson, Saunders and Galletta, 2016). Predictive power is very important in information
system as it is necessary for assessing the power and building empirical model at each step.
Information system uses advanced predictive analytic to support empirical research. It
supports in filling the gap of making decision as it accessed all the present processes and then
build new theories so that gap can be resolved.
In the paper, it was clearly mentioned that prediction is one of the core activity in the
scientific field as it models information system using causal explanatory statically modelling.
It influences casual models by testing it hypothetically rather than making empirical
predictions. Predictive analytics main goal is to have high predictive power. Predictive model
is used to develop correct decision by eliminating all the loopholes. The other term that is
used in place of predictive analytics is qualitative empirical modelling that covers two
components one is designed for predicting future by observing the current scenario. The other
component is evaluating the predictive power of the model that is designed. Predictive power
generates accurate predications so that accurate decisions could be taken and models could be
modified (Parikh, Kakad and Bates, 2016). Sometimes, accurate data are needed whereas
sometimes organization becomes liable to predict the data. Predictive analytics can make
predictions only if data is available in large quantity. Thus, it is needed to fill the gap between
predictions and true model. The major difference between these two models is the level at
which these models operate. In the paper it was found that, predictive analytics rely on the
relation between all the measurable variable and statistical methods are based on the casual