Analytics

2.  Data Where house
 Data Mining
 Business Analytics
 Business Intelligence
 Decision Making

3. Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
Which customers
are most likely to go
to the competition ?
What impact will
new products/services
have on revenue
and margins?
What product prom-
-otions have the biggest
impact on revenue?
What is the most
effective distribution
channel?

4. 4
I can’t find the data I need
– data is scattered over the network
– many versions, subtle differences
I can’t get the data I need
need an expert to get the data
I can’t use the data I found
results are unexpected
data needs to be transformed from
one form to other

5. 5
5
A single, complete and
consistent store of data
obtained from a variety
of different sources
made available to end
users in a what they can
understand and use in a
business context.

6. 6
 Data Warehouse: (W.H. Immon)
A subject-oriented, integrated, time-variant,
non-updatable collection of data used in
support of management decision-making
processes
Subject-oriented: e.g. customers, patients,
students, products
Integrated: Consistent naming conventions,
formats, encoding structures; from multiple
data sources
Time-variant: Can study trends and
changes
Non-updatable: Read-only, periodically
refreshed

7. 7
Data
Warehouse
Integrated
Time VariantNon Volatile
Subject
Oriented

8. 8
Data is categorized and stored by business sub
rather than by application
Equity
Plans
Shares
Customer
financial
information
Savings
Insurance
Loans
OLTP Applications Data Warehouse Subject

9. 9
 Constructed by integrating multiple,
heterogeneous data sources
◦ relational databases, flat files, on-line
transaction records
 Data cleaning and data integration
techniques are applied.
◦ Ensure consistency in naming conventions,
encoding structures, attribute measures, etc.
among different data sources
 E.g., Hotel price: currency, tax, breakfast covered, etc.
◦ When data is moved to the warehouse, it is
converted.

10. 10
 The time horizon for the data warehouse is
significantly longer than that of operational
systems.
◦ Operational database: current value data.
◦ Data warehouse data: provide information from a
historical perspective (e.g., past 5-10 years)
 Every key structure in the data warehouse
◦ Contains an element of time, explicitly or implicitly
◦ But the key of operational data may or may not
contain “time element”.

11. 11
Data is stored as a series of
snapshots, each representing a period
of time
Time Data
Jan-97 January
Feb-97 February
Mar-97 March

12. 12
 A physically separate store of data
transformed from the operational
environment.
 Operational update of data does not occur in
the data warehouse environment.
◦ Does not require transaction processing, recovery,
and concurrency control mechanisms.
◦ Requires only two operations in data accessing:
 initial loading of data and access of data.

13. 13
Typically data in the data warehouse is not updated or delelted.
Insert
Update
Delete
Read Read
Operational Warehouse
Load

14. 14
A process of
transforming data into
information and making
it available to users in a
timely enough manner
to make a difference
The process of
constructing and using
a data warehouseData
Information

15.  DM is a way to develop the intelligence from
data which organization collects, organize
and stores to gain better understanding of
their customers, operations and to solve the
organizational problems.
 Latest strategic weapons are :
 Decision making based on analytics
 To understand the customer better
 To optimize the supply chain to maximize
their return on investments

16.  DM was originally used to describe the
process through which previously unknown
patterns in data were discovered
 Used to describe or discover or “Mining” the
“Knowledge” from the large amount of data.
 It include : 1. Knowledge Mining
2. Knowledge Discovery
3. Pattern Searching/Analysis
4. Find out correlation b/w data
5. To find out Trends in business
6. Prediction for the future

17.  DM relatively new term but roots are in traditional
analysis and statistical methods
 DM is positioned at intersection of
1. Artificial Intelligence
2. Machine Learning
3. Mathematical/Statistical Modeling
4. Management Science and
Information System
5. Data Bases
 Works on Client/Server Architecture or web based
Information System
 New tool for visualization
 To find out the unexpected results.

18. Data
Bases
Data Mining
Pattern
Recognition
Machine
Learning

19.  Customer Relationship Management
 Banking
 Insurance
 Manufacturing and Production
 Government and Defense
 Travel Industry
 Health Care
 Medicine
 Entertainment Securities
 Sports.

20. Analytics is the use of:
data,
information technology,
statistical analysis,
quantitative methods, and
mathematical or computer-based models
To help managers gain improved insight about
their business operations and make better,
fact-based decisions.

21.  Business Analytics
The use of analytical methods:
1. Either manually or automatically
2. To derive relationships from data
3. It include the access & reporting
Analysis of data supported by software to
drive business performance and decision
making

22. Information, Analysis And Decisions:
The Basics
Analysis
Information
Why did it
happens
What is
Happening
What is Likely
to happen
What should
I do about you ?
Descriptive
Analysis
Diagnostics
Analytics
Predictive
Analytics
Prescriptive
Analytics
Analytic excellence leads to better decisions

23. Descriptive
Diagnostics
Discovery
Predictive
Prescriptive
Value
SkillLevel

25.  OLAP: On Line Analytical Processing : An
information system that enables the user, while at a PC, to query the
system, conduct an analysis, and so on. The result is generated in
seconds.
 OLTP: Online Transaction Processing : OLTP
concentrates on processing repetitive transactions in large
quantities and conducting simple manipulations
 Ad hoc Queries & Reports: A query that cannot be
determined prior to the moment the query is issued. (On Demand
Report).
Routine Reports like Schedule report, Summary Report, Key Indicator,
Annual Report >
 Data/Text/Web Mining & Search Engine:

26. EIS: Executive information systems (EIS)
Provides rapid access to timely and relevant information as
well as monitoring an organization’s performance
Executive support systems (ESS)
Also provides analysis support, communications, office
automation, and intelligence support
 Management Science and Statistical Analysis
 Data Mining & Predictive Analysis: analyzes past
performance
 Business Performance Management

27. A graphical, animation, or video presentation
of data and the results of data analysis:
The ability to quickly identify important trends in
corporate and market data can provide competitive
advantage
Check their magnitude of trends by using predictive
models that provide significant business
advantages in applications that drive content,
transactions, or processes

30.  Score Cards:
Focuses on a given metric and compares it to a forecast or
target
 Dash Board:
Visual Similarities to a car Dash Board.
Provide graphical depiction of current key performance
indicators in order to get the faster response to change
the areas such as sales, customer relation, performance
assessment on a single screen.
“Score cards” and “Dash Boards” are often used
interchangeably .

31.  There is a strong relationship of BA with:
- profitability of businesses
- revenue of businesses
- shareholder return
 BA enhances understanding of data
 BA is vital for businesses to remain
competitive
 BA enables creation of informative reports

32.  Descriptive analytics:
Uses data to understand past and present
 Predictive analytics
Analyzes past performance
 Prescriptive analytics
Uses optimization techniques

33. Example: Retail Markdown Decisions
 Most department stores clear seasonal inventory by
reducing prices.
 The question is:
When to reduce the price and by how much?
 Descriptive analytics: examine historical data for
similar products (prices, units sold, advertising, …)
 Predictive analytics: predict sales based on price
 Prescriptive analytics: find the best sets of pricing and
advertising to maximize sales revenue

34.  Performance management systems (PMS) are BI tools
that provide scorecards and other relevant
information that decision makers use to determine
their level of success in reaching their goals

35.  Business Intelligence is an umbrella that combines
1. architectures
2. analytical tools
3. databases
4. applications and methodologies.
 Business Intelligence is transformation of data to
information, then to decisions and finally to action.
 Business intelligence; major objective is to enable the
real time access, manipulation, appropriate analysis of
data. By providing current data, situations and
performance to decision maker so that they get valuable
insight to make powerful decision.

36. Business
intelligence
portals
Querying & report
Work flow
ETL
Data where house
data marts

37.  The Data/information explosion is the rapid
increase in the amount of published
information or data and the effects of this
abundance.
 As the amount of available data grows, the
problem of managing the information
becomes more difficult, which can lead to
information overload.

38.  From clicks to likes, today's networked world
is creating vast amounts of data at a
significantly increasing rate.
 Combine that with exponentially increasing
internal data and you get the data explosion
commonly known as Big Data.
 The ability to translate this vast array of
structured and unstructured input into usable
business intelligence provides a key
competitive advantage for technology
companies.