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:

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.

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, month-

over-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].

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-to-

day activities much more convenient. It also helps

with inventory management, tracking product use,

and tracking sales rates and locations.

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