This document provides an introduction to business analytics. It defines business analytics as combining data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with possible scenarios to make well-informed decisions. The document also discusses the evolution of business analytics and some key techniques used in business analytics like descriptive analysis, predictive analysis, and prescriptive analysis.

1. Introduction
to
Business Analytics

2. • Analytics is a field which combines following into one -
I. Data,
2. Information technology,
3. Statistical analysis,
4. Quantitative methods and
5. Computer-based models
• This all are combined to provide decision makersall the
possible scenarios to make a well thought and
researched decision.

3. Meaningof BusinessAnalytics
• Business analytics (BA) refersto
—"The skills, technologies, practices for continuous
developing new insights and understanding of business
performance based on data and statistical methods".
—"the practiceof explorationof an organization'sdata with
emphasis on statistical analysis. Business analytics is used
by companies committed to data-driven decision making.

4. —"The statistical analysis of the data a business has
acquired in order to makedecisions that are based
on evidence rather than a guess".
—"A combination of data analytics, business
intelligence and computer programming. It is the
science of analysing data to find out patterns that
will be helpful in developingstrategies"

5. Evolution of Business Analytics
Business analytics has been existence since very long time and has
evolvedwith availabilityof newerand bettertechnologies.
It has its roots in operationsresearch,which was extensivelyused during
WorldWar ll. Operationsresearchwas an analyticalwayto look at datato
conductmilitaryoperations.
• Over a period of time, this techniquestartedgetting utilizedfor business.
Here operation'sresearchevolved into managementscience. Again, basis
for managementscience remainedsame as operationresearch in data,
decisionmakingmodels,etc.

6. • As the economies started developing and companies
became more and more competitive, management
science evolvedinto-
—Business intelligence,
—Decision support systems and into
— PC software.

7. • To make data-driven decisions
• Convertsavailable data into valuable information.
• Eliminate guesswork
Get faster answer to questions
• Get insight into customer behavior
• Get key business metrics reports when and where
needed

8. • It impacts functioning of the whole organization. And
hence, can-
— Improve profitability of the business
—Increase market share and revenue and
—Provide better return to a shareholder
— Reduce overall cost
—Sustain in competition
— Monitor KPIs (Key Performance Indicators) and
—React to changing trends in real time

9. CHALLANGES FOR BUSINESSANALYITCS
• Businessanalyticsdepends on sufficientvolumes of high
quality data.
• The difficulty in ensuringdata quality.
• Datawarehousingrequire a lot more storagespace than
it did speed.
• Businessanalyticsis becominga tool that can influence
the outcomeof customerinteractions.

10. • Technology infrastructureand tools must be able to
handlethe data and BusinessAnalytics processes.
• Organizations should be prepared for the changes
that Business Analytics bring to current business and
technology operations.
Dr,Amitabh Mishra

11. Scope of BusinessAnalytics
• Business analytics has a wide range of
application and usages-
—Descriptive analysis
—Predictive analysis
—Prescriptive analysis

12. DescriptiveAnalysis
• This branch of Business Analytics analyses and finds
answer to the question-
"What has happened in the past?".
• Descriptive analysis/ statistics performs the function
of "describing" or summarizing raw data to make it
easily understandableand interpretable by humans.

13. PredictiveAnalytics
• This branch of Business Analytics, uses forecasting
techniques and statistical models to find out-
What is going to happen in future?
• Predictive analysis helps us in predicting the future
course of events and taking necessary measures for the
same.

14. • Predictiveanalysis employ-
— Predictivemodelling and Machinelearningtechniques.
• Predictivemodelingusesstatisticsto predictoutcomes.
• Machine learning(ML) statistical is the scientific
study of algorithmsand modelsthat computersystemsuse to
performa specifictask without using explicitinstructions,relying
on pattems and inferenceinstead. Machine learning algorithms
builda mathematicalmodelbased on sampledata, knownin order
to make predictions or decisions without being explicitly
programmedto performthe task.

15. PrescriptiveAnalytics
This branchof Analytics,makesuse of optimizationand simulation
algorithmsto find answerto the question-
"What should we do?".
PrescriptiveAnalysisis usedto give adviceson possibleoutcomes.
• This is a relativelynew field of analyticsthat allows users to
recommendseveraldifferent possiblesolutionsto the problemand
to guidethem aboutthe best possiblecourse of action.

16. USERS OF BUSINESS ANALYITCS
1. Students
2. Business man
3. Accountants and Auditors
4. Organization/Companies/Group of industries/
Small firm

17. 1. MS-EXCEL
2. SPSS
3.
4. SAS
5. E-views

18. MAIN SOFTWARE USED FOR BUSINESS
ANALYITCS
1. MS-EXCEL
2. SPSS
3.
4. SAS
5. E-views

19. • SPSS-
—SPSS Statistics is a software package used for statistical
analysis. Long produced by SPSS Inc., it was acquired by
IBM in 2009. The current versions (2014) are officially
named IBM SPSS Statistics.
• MS-EXCEL-
— Microsoft Excel is a spreadsheet application developed by
Microsoft for Microsoft Windows. It features calculation,
graphing tools, pivot tables, and a macro programming
language called Visual Basic for Applications.

20. MS-EXCEL in Business Analytics
—Microsoft Excel is a spreadsheet application
developed by Microsoftfor MicrosoftWindows.
— It features
• Calculation,
• Graphing tools,
Pivottables,and
• A macroprogramminglanguagecalledVisualBasic

21. Find

22. Data Mining
Text Mining
Forecasting
Predictive Analytics
Optimization
Visualization

23. Data Mining —Create models by uncovering previously
unknowntrends and pattern in vast amounts of data e.g.
detect insurance claims frauds, Retail Market basket
analysis.
There are various statisticaltechniques through which data
mining is achieved.
— Classification (when we know on which variables to classify the
data e.g. age, demographics)
— Regression
—Clustering (when we don't know on which factors to classify
data)
— Associations & Sequencing Models

24. • Text Mining —Discover and extract meaningful
patterns and relationships from text
collections. E.g.
—Understand sentiments of Customers on social
media sites like Twitter, Face book, Blogs, Call
centre scripts etc. which are used to improvethe
Product or Customer service or understand how
competitors are doing.

25. • Forecasting —Analyze & forecast processes that take place
over the period of time. E.g.
— Predictseasonalenergy demandusing historicaltrends,
—Predict how many ice creams cones are required considering
demand
• Predictive Analytics — Create, manage and deploy
predictive scoring models. E.g.
— Customer churn & retention,
— Credit Scoring,
—Predictingfailureinshopfloor machinery

26. Optimization— Use of simulations techniques to
identify scenarios which will produce best results.
E.g.
—Sale price optimization,
—Identifyingoptimal Inventoryfor maximumfulfilment
& avoid stock outs.
• Visualization— Enhanced exploratory data
analysis & output of modelling results with highly
interactive statistical graphics.