Various ways during which analytics help enterprises drive business growth – Stastwork
                     VARIOUS WAYS DURING 
WHICH  ANALYTICS HELP 
ENTERPRISES  DRIVE BUSINESS 
GROWTH
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Statswork  Group www.statswork.com
Email: [email protected]
TODAY'S DISCUSSION
Outline
Introduction
EMERGENCE OF THE NEW CONCEPT OF TECH-
BUSINESS-  ANALYTICS (TBA)
Predictive analytics
Descriptive Analytics 
(DESCBA)  Prescriptive 
Analytics (PREBA)  
Conclusion
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 analy
tics techniques in the business domain  to derive 
and predict useful decisions is called BA
EMERGENCE OF It is the mechanism by which organizations use  statistical tools and technology to examine 
 THE NEW  historical  data to attain new insights and improve strategic  decision-making.
CONCEPT OF  It needs managerial skill that integrates 
TECH- business  experience, recognizes market value 
opportunities, or  recognizes problems, with a 
BUSINESS-  great understanding of  analytical techniques 
required to align technical talent  with 
ANALYTICS  functional managers to drive business change.
(TBA): A new model industry 4.0 technology is 
founded on  business analytics using a recently 
developed  analysis framework called 
predictive analysis [7].
Contd...
PREDICTIV
E  This uses statistics and network analytics to forecast future  models.
ANALYTICS 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 organizational 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.
Contd...
DESCRIPTIV
E  This uses data mining, data intelligence and web 
ANALYTICS  analytics  to present the trending information of the near past and  current events, helping business 
(DESCBA): 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.
Contd...
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 recognize the areas of strength and 
weakness in an  organization.
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?" Contd...
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].
Contd...
PRESCRIPTIV 
 E This uses AI, optimization, and reasoning to provide 
ANALYTICS  the  most suitable set of models for the organization to choose  from, indicating the pros and cons of each.
(PREBA): These layers also contain two interconnected 
analytics:  Inquisitive analytics and Preemptive 
Analytics.
Tanhael y s isnquisitivaep p  raonvael/yretijcesc t  usepsr e  psotsaittiisotnicsa,l   wahniled   
tfaoc  tor the of three layers is essential for theproper
combinatio and application of Business
nar  chitecture.analytics
[fu5n] ctioning
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.
Contd...
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