Dt. Ananta Prasad Nanda Faculty IBCS, SOA University Sub:BA Outline For Today:D-P-P Analytics

Dt.
Ananta Prasad Nanda

Faculty IBCS,SOA University
Sub:BA
Outline for Today:D-P-P analytics
What is Business Analytics?
Business analytics is a method of applying statistical techniques
combined with applied mathematics and computers science to improve
decision making strategy in business scenarios. With the application of
business analytics improve in HR planning, sales strategies, policy
making, financial activities, product pricing and so forth can be done.
Types of Data analytics with Techs:

Descriptive Analytics – It is a method to analyze data that helps to describe, show
or summary data of past and present forms. It consists of measures of central
tendency and measures of dispersion. Measures of central tendency (mean, median
& mode) represent the entire set of data with one representative value. Also known
as the central value. Measures of dispersion (variance, standard deviation & range)
give the spread of data [variability]. It helps to identify the problem in the data.
•
Cont…..
Predictive Analytics – It is a method to analyze data for determining patterns and
make a prediction about the likelihood of future outcomes by using historical data
(forecasting). It uses methods like data mining, statistical modelling and
machine learning for forecasting data.
Prescriptive Analytics – Descriptive analytics gives the overview of “What has

happened?” and while predictive analytics gives the prediction of “What will
happen?” Prescriptive analytics gives the best optimal solution from the alternatives
for stronger business performance. Basically, it is an implementation of a certain
plan based on the results of descriptive and predictive analytics (decision support).
Descriptive analytics: Descriptive analytics is typically the
starting point in business intelligence. It uses data aggregation
and data mining to collect and organize historical data,
producing visualizations such as line graphs, bar charts, pie
charts. Descriptive analytics presents a clear picture of what
has happened in the past, such as statistical modeling does, and
it stops there — it doesn’t make interpretations or advise on
future actions.
Use Of Descriptive Analytics:
Descriptive analytics is helpful to identify answers to simple questions about what
occurred in the past. When you’re doing this type of analytics, you’ll typically
start by identifying KPIs as benchmarks for performance in a given business area
(sales, finance, operations, etc.). Next, you’ll determine what data sets will inform
the analysis and where to source them from, then collect and prepare them.
You’ll use various methods to see patterns and measure performance, such as
pattern tracking, clustering, summary statistics, and regression analysis. Finally,
you’ll create visualizations to make the data quickly and easily understandable.
Examples of descriptive analytics
Descriptive analytics can benefit decision-makers from every department in a

company, from finance to operations. Here are a few examples:
The sales team can learn which customer segments generated the highest dollar
amount in sales last year.
The marketing team can uncover which social media platforms delivered the
best return on advertising investment last quarter.
The finance team can track month-over-month and year-over-year revenue
growth or decline.
Operations can track demand for SKUs across geographic locations throughout
the past year.
Predictive Analytics:
It is the use of data to predict future trends and events. It uses
historical data to forecast potential scenarios that can help drive
strategic decisions.
The predictions could be for the near future—for instance,

predicting the malfunction of a piece of machinery later that day—
or the more distant future, such as predicting your company’s cash
flows for the upcoming year.
Predictive analysis can be conducted manually or using machine-

learning algorithms. Either way, historical data is used to make
assumptions about the future.
Examples:
Finance: Forecasting Future Cash Flow: Every business needs to keep periodic financial
records, and predictive analytics can play a big role in forecasting your organization’s future
health. Using historical data from previous financial statements, as well as data from the broader
industry, you can project sales, revenue, and expenses to craft a picture of the future and make
decisions.
Entertainment & Hospitality: Determining Staffing Needs: One example explored in Business
Analytics is casino and hotel operator Caesars Entertainment’s use of predictive analytics to
determine venue staffing needs at specific times.
• In entertainment and hospitality, customer influx and outflux depend on various factors, all of
which play into how many staff members a venue or hotel needs at a given time. Overstaffing
costs money, and understaffing could result in a bad customer experience, overworked
employees, and costly mistakes.
• To predict the number of hotel check-ins on a given day, a team developed a multiple
regression model that considered several factors. This model enabled Caesars to staff its
hotels and casinos and avoid overstaffing to the best of its ability.
3 . Marketing: Behavioral Targeting
In marketing, consumer data is abundant and leveraged to create

content, advertisements, and strategies to better reach potential
customers where they are. By examining historical behavioral
data and using it to predict what will happen in the future, you
engage in predictive analytics.
Predictive analytics can be applied in marketing to forecast sales

trends at various times of the year and plan campaigns
accordingly.
Manufacturing: Preventing Malfunction
While the examples above use predictive analytics to take action based on
likely scenarios, you can also use predictive analytics to prevent unwanted or
harmful situations from occurring. For instance, in the manufacturing field,
algorithms can be trained using historical data to accurately predict when a
piece of machinery will likely malfunction.
When the criteria for an upcoming malfunction are met, the algorithm is
triggered to alert an employee who can stop the machine and potentially
save the company thousands, if not millions, of dollars in damaged product
and repair costs. This analysis predicts malfunction scenarios in the moment
rather than months or years in advance.
Some algorithms even recommend fixes and optimizations to avoid future

malfunctions and improve efficiency, saving time, money, and effort. This is
an example of prescriptive analytics; more often than not, one or more types
of analytics are used in tandem to solve a problem.
Health Care: Early Detection of Allergic Reactions:
Another example of using algorithms for rapid, predictive
analytics for prevention comes from the health care industry.
 When a reaction is predicted to occur, an algorithmic response

is triggered. The algorithm can predict the reaction’s severity,
alert the individual and caregivers, and automatically inject
epinephrine when necessary. The technology’s ability to predict
the reaction at a faster speed than manual detection could save
lives.
Prescriptive Analytics:
Prescriptive analytics also looks at future scenarios, but it
employs a more technological approach. By utilizing
complicated mathematical algorithms, artificial intelligence and
machine learning, prescriptive analytics takes a deeper look into
the “what” and “why” of a potential future outcome.
In addition to providing a more in-depth look into the future,
prescriptive analytics can help a company see multiple potential
options in its future and their respective potential outcomes. As
more data comes in, prescriptive analytics can alter its
predictions and suggestions.
Inventory planning
As a small retailer, it’s common to want to know how much stock you need in
order to fill your shelves. Analytics can help you plan a more precise
stocking strategy.
Guy Yehiav, CEO of business intelligence company Profitect, said that as
the retail landscape changes, businesses can use prescriptive analytics to
clarify predictive data and improve sales. To clarify how both types of
analytics can be used together, Yehiav gave the example of a retailer that
offers free expedited shipping to loyal customers. Based on past customer
behavior, a predictive model would assume that customers will keep the
majority of what they purchase with this promotion. However, one customer
purchases eight items of clothing but decides to keep only one.
Weather forecasts
Predicting the weather can be a dicey proposition, but with the change of
seasons comes the shift from indoor activities to fun in the sun. One small
business sector that benefits from nicer weather and increased physical
activity is sporting goods stores.
If the store’s forecasts indicate that sales of running shoes will increase as
warmer weather approaches in the spring, it might seem logical to ramp up
the inventory of running shoes at every store. However, in reality, the sales
spike likely won’t happen at every store across the country all at once.
Instead, it will creep gradually from south to north based on weather
patterns.
“To flip the switch on massive running-shoe distribution nationwide would
be a huge mistake, even though the predictive analytics indicate sales will
be up,” Sengupta said. “But with prescriptive analytics, you can pull in third-
party sources, like weather and climate data, to get a better
recommendation of the best course of action.”

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Dt.

Ananta Prasad Nanda

Types of Data analytics with Techs:

Prescriptive Analytics – Descriptive analytics gives the overview of “What has

Descriptive analytics can benefit decision-makers from every department in a

The predictions could be for the near future—for instance,

Predictive analysis can be conducted manually or using machine-

In marketing, consumer data is abundant and leveraged to create

Predictive analytics can be applied in marketing to forecast sales

Some algorithms even recommend fixes and optimizations to avoid future

 When a reaction is predicted to occur, an algorithmic response

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