Various ways during which Analytics help
Enterprises drive Business Growth
Dr. Nancy Agnes, Head,Technical Operations, Statswork info@statswork.com
I. INTRODUCTION
Business Analytics help in extracting necessary
information from all the data available to transform
into a coherent structure for further use. The process
of data implementing the advanced analytics
techniques in the business domain to derive and
predict useful decisions is called BA.
II.EMERGENCE OF THE NEW CONCEPT OF
TECH-BUSINESS-ANALYTICS (TBA):
It is the mechanism by which organisations use
statistical tools and technology to examine historical
data to attain new insights and improve strategic
decision-making. It needs managerial skill that
integrates business experience, recognises market
value opportunities, or recognises problems, with a
great understanding of analytical techniques required
to align technical talent with functional managers to
drive business change. A new model industry 4.0
technology is founded on business analytics using a
recently developed analysis framework called
predictive analysis [7].
III. PREDICTIVE ANALYTICS:
This uses statistics and network analytics to
forecast future models. Predictive business analytics
uses various statistical techniques to build predictive
models that extract data from databases, detect
trends, and include a predictive score for a range of
organisational outcomes. It uses quantitative
techniques (e.g., propensity, segmentation, network
analysis and econometric forecasting) and technology
(such as models and rule-systems) that use past data
for the future.Predictive analytics generates
information from historically available datasets to
determine and predict future trends and outcomes.
The predictive analysis system encompasses 4 steps:
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Gathering data on present trends
Develop postulates based on present trends
Generate argument based description
Predict the future.
IV. DESCRIPTIVE ANALYTICS (DESCBA)
This uses data mining, data intelligence and web
analytics to present the trending information of the
near past and current events, helping business models
know the demands and drawbacks of the markets.
It provides access to historical and current data. It
provides the ability to alert, explore and report using
both internal and external data from various sources.
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Descriptive analytics is the procedure of
parsing historical data to understand the
changes in a business to make it better in
future.
Using a range of historical data and
benchmarking, decision-makers obtain a
holistic view of performance and trends to
base business strategy.
Descriptive analytics can help to recognise
the areas of strength and weakness in an
organisation.
Examples of metrics used in descriptive
analytics comprise year-over-year pricing
modifications, the number of users, monthover-month sales growth, or the total
revenue per subscriber.
 Descriptive analytics is now being used in
conjunction with newer analytics, such as
predictive and prescriptive analytics.
 In its simplest form, descriptive analytics
answers the question, "What happened?"
A mixture of a descriptive-analytical process that
provides insight into what happened and a
predictive analytical process that provides insight
into what might happen helps users predict what
will happen, when it will happen and why [8].
4.
5.
6.
V. PRESCRIPTIVE ANALYTICS (PREBA):
This uses AI, optimization, and reasoning to provide
the most suitable set of models for the organization to
choose from, indicating the pros and cons of
each.These layers also contain two interconnected
analytics: Inquisitive analytics and Preemptive
Analytics. The inquisitive analytics uses statistical
and factor analysis to approve/reject prepositions,
while the combination of three layers is essential for
the proper functioning and application of Business
analytics architecture.[5]
VI. CONCLUSION
The analysis conducted by most published studies
shows that IoT has massive potential on businesses
across many sectors. The data collected from IoT
implementation allows businesses to increase
productivity, which benefits sales and marketing,
resource management, growth potential, and
profitability. Since many applications can be
accessed on mobile devices, IoT makes users' day-today activities much more convenient. It also helps
with inventory management, tracking product use,
and tracking sales rates and locations.
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