The PPTs are about various types of big data analytics i.e. descriptive, predictive, and prescriptive analytics

1. BIG DATA
ANALYTICS

2. POINTS TO BE COVERED
• What is Analytics
• Business Analytics
• Classification of Analytics
• Types of Analytics (Descriptive, Predictive, and Prescriptive)
• Business Intelligence vs. Data Science

3. WHAT IS ANALYTICS?
• Analytics is the scientific process of discovering and communicating the meaningful
patterns which can be found in data
• It is concerned with turning raw data into insight for making better decisions
• Analytics relies on the application of statistics (in case of quantitative data), computer
programming, and operations management in order to quantify and gain insight to
the meanings of data
• It is especially useful in areas which record a lot of data
Source: https://www.techopedia.com/definition/30296/analytics

4. WHAT IS ANALYTICS?
• Analytics is the process of breaking the problem into simpler parts and using
inferences based on data to take decisions
• Analytics is not a tool or technology, rather it is a way of thinking and acting

5. BUSINESS ANALYTICS
• Business Analytics specifies application of Analytics in the sphere of Business. It
includes:
– Marketing Analytics
– Risk Analytics
– Fraud Analytics
– CRM Analytics
– Loyalty Analytics
– Operation Analytics
– HR Analytics

6. BUSINESS ANALYTICS
Within the business, analytics is used in all sorts of industries like:
• Banking and Finance
• Healthcare
• Insurance Analytics
• Retail
• Telecom
• Web (Online Business)

7. “Big Data Analytics” is the new
term which is used to analyze the
big data like terabytes or even
petabytes of data

8. CLASSIFICATION OF ANALYTICS
We are going to look closer at three broad classifications of analytics:
1. Based on the industry
2. Based on the business function
3. Based on kind of insights offered

9. CLASSIFICATION OF ANALYTICS
1. Based on the industry
• There are certain industries which have always created a huge amount of data like
credit cards and consumer goods
• These industries were among the first ones to adopt analytics
• Analytics is often classified on the basis of the industry it is being applied to, hence you
will hear terms such as:
– Insurance Analytics
– Retail Analytics
– Web Analytics
– Health Analytics
and so on

10. CLASSIFICATION OF ANALYTICS
2. Based on the business function
• We can even classify analytics on the basis of the business function it’s used in.
Classification of analytics on the basis of business function goes as follows:
– Marketing Analytics
– Sales Analytics
– HR Analytics
– Supply Chain Analytics
and so on

11. CLASSIFICATION OF ANALYTICS
3. Based on kind of insights offered
• We can even classify analytics on the basis of kind of insights offered. Classification of
analytics on the basis of kind of insights offered goes as follows:
– Fraud Analytics
– Customer Sentiment Analytics
– Loyalty Analytics
– Operations Analytics
– Return on Investment Analytics
– Crime Analytics
and so on

12. The goal of any analytics solution
is to provide the organization with
actionable insights for smarter
decisions and better business
outcomes

13. Different types of analytics,
however, provide different
types of insights

14. So it is important for managers to
understand what each analytics
type delivers

15. THREE KEY ANALYTICS TYPES
Analytics solutions are of three principal types:
1. Descriptive, which uses business intelligence and data mining to ask: “What has
happened?”
2. Predictive, which uses statistical models and forecasts to ask: “What could happen?”
3. Prescriptive, which uses optimization and simulation to ask: “What should we do?”

16. The three types build on one
another, with descriptive
analytics being the most
common and prescriptive
analytics the most advanced

17. THREE KEY ANALYTICS TYPES
1. Descriptive Analytics (Questions Type)
 What customers are most receptive to what types of merchandising campaigns?
 What are the characteristics of customers (e.g., age, gender, customer tenure, life
stage, favorite sports) who are most responsive to merchandising offers?
 Are there certain times of year where certain customers are more responsive to
merchandising offers?

18. THREE KEY ANALYTICS TYPES
1. Descriptive Analytics
 Asking “What has happened?”,
 descriptive analytics mines data to provide trending information on past or current
events that can give business managers the context they need for future actions
 Characterized by the use of key performance indicators, descriptive analytics drills
down into data to uncover details such as:
– The frequency of events
– The cost of operations and
– The root cause of failures

19. THREE KEY ANALYTICS TYPES
1. Descriptive Analytics
 The most common type of analytics used by organizations
 It typically displays information within a report or dashboard view

20. THREE KEY ANALYTICS TYPES
2. Predictive Analytics (Questions Type)
 Which customers are most likely to visit the store for a back-to-school promotion?
 Which customers are most likely to respond to the new Michael Jordan basketball
shoe?
 Which customers are most likely to respond to a 50 percent off in-store markdown on
Nike apparel?
 Which customers are likely to respond to an offer of a free pair of Jordan Elite socks
when they buy new shoes?

21. THREE KEY ANALYTICS TYPES
2. Predictive Analytics
• Asking “What could happen?”
• predictive analytics provides answers that move beyond using historical data as the
principal basis for decisions
• Instead, it helps managers anticipate likely scenarios—so they can plan ahead, rather
than reacting to what has already happened

22. THREE KEY ANALYTICS TYPES
2. Predictive Analytics
• Using descriptive data accumulated over time, predictive analytics utilizes models for
predicting events
• It does not, however, recommend actions
• Predictive capabilities such as forecasting and simulation provide enhanced insight
that managers can use to make more informed decisions

23. THREE KEY ANALYTICS TYPES
2. Predictive Analytics
• Characterized by the use of trends of time-series data and correlations to identify
patterns
• Predictive analytics applies advanced statistical analysis and data mining
• As well as sophisticated mathematics to validate assumptions and test hypotheses
• To provide a solid, data-based foundation that can raise managers’ confidence in
conclusions

24. THREE KEY ANALYTICS TYPES
2. Predictive Analytics (Example)
• Organizations might use these results to identify conditions for potential out-of-stock
or over-stock in parts inventory. They might also use them to evaluate asset failure
productivity history to anticipate the likelihood of failure in a particular timeframe.

25. THREE KEY ANALYTICS TYPES
2. Prescriptive Analytics (Question Type)
 E-mail Bill Schmarzo a 50 percent discount coupon for two pairs of Nike Elite socks
when he buys his new pair of Air Jordans
 Text Max Schmarzo that he will receive a triple-point bonus when he buys Nike
this coming weekend
 Mail Alec Schmarzo a $20 cash coupon good only if he visits the store within the next
14 days

26. THREE KEY ANALYTICS TYPES
3. Prescriptive Analytics
• Asking “what should we do?”,
• Prescriptive analytics explores a set of possible actions and suggests actions based on
descriptive and predictive analyses of complex data
• Though the final decision is up to the managers, prescriptive analytics solutions can
provide a reliable path to an optimal solution for business needs or resolution of
operational problems

27. THREE KEY ANALYTICS TYPES
3. Prescriptive Analytics
• Characterized by rules, constraints and thresholds, prescriptive analytics makes use of
advanced capabilities such as optimization and mathematical models to reveal not
only recommended actions but also why they are recommended, along with any
implications the actions might have.
Example
• Organizations might use these results to identify inventory that should be re-ordered
now, that should be moved to a different distribution center or that should be
disposed of

28. THREE KEY ANALYTICS TYPES
3. Prescriptive Analytics
• Prescriptive analytics takes uncertainty into account and recommends ways to
the risks that can result from it.
• Its ability to not only examine potential outcomes but also make recommendations
helps managers make decisions when the data environment is too large or complex
be understood without the help of technology.

29. DESCRIPTIVE, PREDICTIVE, AND
PRESCRIPTIVE ANALYTICS
WHAT HAPPENED?
(DESCRIPTIVE/BI)
WHAT WILL HAPPEN?
(PREDICTIVE ANALYTICS)
WHAT SHOULD I DO?
(PRESCRIPTIVE ANALYTICS)
How many widgets did I sell
last month?
How many widgets will I sell
next month?
Order [5,0000] units of
Component Z to support widget
sales for next month
What were sales by zip code
for Christmas last year?
What will be sales by zip code
over this Christmas season?
Hire [Y] new sales reps by these
zip codes to handle projected
Christmas sales
How many of Product X were
returned last month?
How many of Product X will be
returned next month?
Set aside [$125K] in financial
reserve to cover Product X
What were company revenues
and profits for the past
quarter?
What are projected company
revenues and profits for next
quarter?
Sell the following product mix to
achieve quarterly revenue and
margin goals
How many employees did I
hire last year?
How many employees will I
need to hire next year?
Increase hiring pipeline by 35
percent to achieve hiring goals

30. DESCRIPTIVE, PREDICTIVE, AND
PRESCRIPTIVE ANALYTICS
WHAT HAPPENED?
(DESCRIPTIVE/BI)
WHAT WILL HAPPEN?
(PREDICTIVE ANALYTICS)
WHAT SHOULD I DO?
(PRESCRIPTIVE ANALYTICS)
How many Nike Hyperdunks
did I sell last month?
How many Nike Hyperdunks
will I
sell next month?
Order [50] Nike Hyperdunks to
support next month’s sales
projections.
What were apparel sales by
code for Christmas last year?
What will be apparel sales by
zip code over this Christmas
season?
Hire [3] temporary reps for
Store 12234 to handle
projected Christmas sales.
How many of Jordan AJ
Futures were returned last
month?
How many of Jordan AJ
will be returned next month?
Set aside [$125K] in financial
reserve to cover Jordan AJ
Futures returns.
What were company revenues
and profits for the past
quarter?
What are projected company
revenues and profits for next
quarter?
Mark down [LeBron
apparel] by 20 percent to
reduce inventory before new
product releases.

31. BUSINESS INTELLIGENCE (BI) VS.
DATA SCIENCE
 Many organizations and individuals are confused by the differences introduced by big
data, especially the differences between business intelligence (BI) and data science
 Big data is not big business intelligence (BI)
 Big data is a key enabler of a new discipline called data science that seeks to leverage
new sources of structured and unstructured data, coupled with predictive and
prescriptive analytics

32. BUSINESS INTELLIGENCE VS. DATA SCIENCE
BUSINESS INTELLIGENCE
1. Business Intelligence focuses on
reporting what happened
(descriptive analytics).
2. Business Intelligence operates with
schema on load in which you have
to pre-build the data schema before
you can load the data to generate
your BI queries and reports.
DATA SCIENCE
1. Data science focuses on predicting
what is likely to happen (predictive
analytics) and then recommending
what actions to take (prescriptive
analytics).
2. Data science deals with schema on
query in which the data scientists
custom design the data schema
based on the hypothesis they want
to test or the prediction that they
want to make.

33. BUSINESS INTELLIGENCE VS. DATA SCIENCE
BUSINESS INTELLIGENCE
3. BI focuses on descriptive analytics:
that is, the “What happened?” types
of
questions. Examples include:
 “How many widgets did I sell last
month?”
DATA SCIENCE
3. Data scientists focus on predictive
analytics (“What is likely to
happen?”) and prescriptive analytics
(“What should I do?”) types of
questions. For example:
 “How many widgets will I sell next
month?”
 “Order [5,000] Component Z to
support widget sales for next month”

34. BUSINESS INTELLIGENCE VS. DATA SCIENCE

35. Organizations that try to “extend” their
business intelligence capabilities to
encompass big data will fail. That’s like
stating that you’re going to the moon, then
climbing a tree and declaring that you are
closer. Unfortunately, you can’t get to the
moon from the top of a tree. Data science
is a new discipline that offers compelling,
business-differentiating capabilities,
especially when coupled with business
intelligence.