This document discusses descriptive, predictive, and prescriptive analytics techniques. It states that the goal of analytics is to obtain actionable insights that lead to smarter decisions and better business outcomes. Descriptive analytics examines past performance to understand reasons for success or failure. Predictive analytics uses statistical modeling and data mining to determine probable future outcomes. Prescriptive analytics synthesizes data and machine learning to suggest optimal decision options by anticipating future risks and opportunities along with the implications of various choices.

1. Analytics with Descriptive,
Predictive and Prescriptive
Techniques
GOAL OF ANALYTICS IS TO GET ACTIONABLE
INSIGHTS RESULTING IN SMARTER DECISIONS
AND BETTER BUSINESS OUTCOMES.

2. Overview
 In the present scenario, the demand of analytics skill is due to
the extensive use of electronic databases for record keeping and
electronic commerce in digital economy.
 As the use of analytics grows quickly, companies will need
employees who understand the data.
 Analytic tools range from spreadsheets with statistical functions
to complex data mining and predictive modelling applications.
 To produce algorithms and analysis help businesses make better
decisions.
 As patterns and relationships in the data are uncovered, new
questions are asked and the analytic process iterates until the
business goal is met.

3. Overview
 Deployment of predictive models involves scoring data records
(typically in a database) and using the scores to optimize real-
time decisions within applications and business processes.
 BA also supports tactical decision making in response to
unforeseen events, and in many cases the decision making is
automated to support real-time responses.
 Analytics have revolutionized the way business is done around
the world.
 All companies, no matter what size, rely on data and analytics
to make critical business decisions.
 From understanding consumer behavior to predicting market
trends, even right down to product features, many moves are
driven by analytics and data in companies across the world.

4. Analytics
Predictive
Analytics
Prescriptive
Analytics
Descriptive
Analytics

5. Descriptive Techniques
 Descriptive analytics looks at past performance and
understands that performance by mining data to look
for the reasons behind past success or failure.
 Descriptive analytics also capture and quantify
relationships among factors to allow assessment of risk
or potential associated with a particular set of
conditions and guiding decision making.
 Descriptive analysis can be utilized to develop further
analysis that can simulate large number of
individualized agents and make predictions.

6. Predictive Techniques
 Predictive analytics uses data to determine the
probable future outcome of an event or a likelihood of
a situation occurring.
 Predictive analytics encompasses a variety of
statistical techniques from modeling, machine
learning, and data mining that analyze current and
historical facts to make predictions about future
events.
 In business, predictive models exploit patterns found
in historical and transactional data to identify risks and
opportunities.

7. Prescriptive Techniques
 Prescriptive analytics synthesizes data, mathematical
sciences, business rules and machine learning to make
predictions and suggests decision options on how to
take advantage of a future opportunity or mitigate a
future risk and illustrate the implication of each
decision option.
 Prescriptive analytics not only anticipates what will
happen and when it will happen, but also why it will
happen.
 Prescriptive analytics can continually process new data
to improve prediction and provide better decision
options.

8. Thank You