TYPES OF BUSINESS ANALYTICS AND ITS APPLICATIONS

The four subsets of data analytics are descriptive, diagnostic, prescriptive, and predictive. Businesses across all types of industries utilize these specialty areas in analytics to increase overall performance at all levels of operations. These four types of analytics all work together and can be used together to help improve business performance

1. Types of Business
Analytics
Dr.A.Karthikeyan MBA.,Ph.D

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3. Dr.A.Karthikeyan MBA.,Ph.D

4. 1. Descriptive Analytics
To Know, What Happened?
•Descriptive analytics is usually
the first type of data analysis
that explains what happened
recently and in the past.
•Both data mining and data
aggregation practices are
applied for this.
•Descriptive analytics is defined
“as a branch of data analytics that
focuses on summarizing and
interpreting historical data to gain
insights and understand patterns,
trends, and relationships within
the data”
Dr.A.Karthikeyan MBA.,Ph.D

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6. Applications of Descriptive Analytics
●Tracking course enrollments,
course compliance rates,
●Recording which learning resources
are accessed and how often
●Summarizing the number of times a
learner posts in a discussion board
●Tracking assignment and
assessment grades
●Comparing pre-test and post-test
assessments
●Analyzing course completion rates
by learner or by course
●Collating course survey results
●Identifying length of time that
learners took to complete a course
Dr.A.Karthikeyan MBA.,Ph.D

7. 2. Diagnostic Analytics
•Diagnostic analytics is a sort of business analytics that assists in understanding
why things happened in the past.
•You may understand the driving causes by using drill-downs, data mining, data
discovery, and correlations.
•This advanced analytics strategy is typically used as a step before Descriptive
Analytics to determine the reasons behind certain outcomes in finance,
marketing, cybersecurity, and other fields.
Dr.A.Karthikeyan MBA.,Ph.D

8. Diagnostic Analytics Examples
•Analyzing market demand
•Recognizing technological difficulties
•Customer behavior explanation
•Enhancing the organizational culture
Dr.A.Karthikeyan MBA.,Ph.D

9. 3. Predictive Analytics
To Predict, What May Happen in the Future?
Predictive analytics breaks down past information to help
businesses make practical guesses about what could
happen in the future.
That helps analysts to:
1)Plan effectively,
2)Set practical goals,
3)Restrain expectations.
DR.A.KARTHIKEYAN MBA.,PH.D

10. How does predictive analytics work? 
•Predictive analytics is based on probabilities.
•Using a variety of techniques – such as data mining, statistical modelling
(mathematical relationships between variables to predict outcomes) and machine
learning algorithms (classification, regression and clustering techniques)
•predictive analytics attempts to forecast possible future outcomes and the
likelihood of those events.
•To make predictions, machine learning algorithms, for example, take existing
data and attempt to fill in the missing data with the best possible guesses.
Dr.A.Karthikeyan MBA.,Ph.D

11. •A newer branch of machine learning is deep learning, which, according to
Cornerstone Performance Management, mimics the construction of ‘human
neural networks as layers of nodes that learn a specific process area but are
networked together into an overall prediction.’ Deep learning examples include
credit scoring using social and environmental analysis and sorting digital
medical images such as X-rays to automate predictions for doctors to use when
diagnosing patients.
Dr.A.Karthikeyan MBA.,Ph.D

12. Advantages and Disadvantages of Predictive
Analysis?
•It is based on probabilities, it can never be completely accurate
•Predictive analytics can also improve many areas of a business, including:
•Efficiency, which could include inventory forecasting
•Customer service, which can help a company gain a better understanding of who their
customers are and what they want in order to tailor recommendations
•Fraud detection and prevention, which can help companies identify patterns and changes
•Risk reduction, which, in the finance industry, might mean improved candidate
screening 
Dr.A.Karthikeyan MBA.,Ph.D

13. Predictive Analytics Examples
•E-commerce – predicting customer preferences and recommending products to
customers based on past purchases and search history
•Sales – predicting the likelihood that customers will purchase another product or
leave the store
•Human resources – detecting if employees are thinking of quitting and then
persuading them to stay
•IT security – identifying possible security breaches that require further investigation
•Healthcare – predicting staff and resource needs
Dr.A.Karthikeyan MBA.,Ph.D

14. 4. Prescriptive Analytics
•Prescriptive analytics generates
recommendations for dealing with
comparable future scenarios based
on prior results. For the available
internal and external data, it employs
a variety of tools, statistics, and ML
algorithms.
•It tells you what might happen when
it might happen, and why.
Dr.A.Karthikeyan MBA.,Ph.D

15.  Advantages and Disadvantages
•Provides invaluable insights in order to make the best possible,
data-based decisions to optimize business performance.
•It requires large amounts of data to produce useful results, which
isn’t always available.
Dr.A.Karthikeyan MBA.,Ph.D

16. Prescriptive Analytics Examples
•Monitoring changes in manufacturing pricing
•Improving Asset Management
•Price forecasting
•GPS technology, since it provides recommended routes to get the user to their
desired destination based on such things as journey time and road closures.
•Testing Identification
•Youtube uses prescriptive analytics to offer a customized viewing experience.
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17. Other areas of application
•Oil and manufacturing – tracking fluctuating prices  
•Manufacturing – improving equipment management, maintenance, price
modelling, production and storage
•Healthcare – improving patient care and healthcare administration by evaluating
things such as rates of readmission and the cost-effectiveness of procedures
•Insurance – assessing risk in regard to pricing and premium information for clients
•Pharmaceutical research – identifying the best testing and patient groups for
clinical trials.
Dr.A.Karthikeyan MBA.,Ph.D

18. 5. Cognitive Analytics
•Cognitive Analytics, which combines Artificial Intelligence and Data
Analytics, is one of the most recent types of business analytics.
•It examines the available facts in the knowledge base to get the best
answers to the queries given.
•Cognitive analytics encompasses a wide range of analytical tools for
analyzing massive data sets and tracking customer behavior
patterns and developing trends.
Dr.A.Karthikeyan MBA.,Ph.D