3. BASICS
Data
• Data is a set of values of qualitative or quantitative variables.
• It is information in raw or unorganized form. It may be a fact,
figure, characters, symbols etc.
Information
• Meaningful or organized data is called information.
Analytics
• Analytics can be defined as a process that involves the use of
statistical techniques (measures of central tendency, graphs,
and so on), information system software (data mining, sorting
routines), and operations research methodologies (linear
programming) to explore, visualize, discover and communicate
patterns or trends in data.
• Analytics is the discovery , interpretation, and
communication of meaningful patterns or summary in data.
4.
5.
6. BUSINESS ANALYTICS
Analytics is the process of converting data into
insights. It is “the extensive use of data, statistical
and quantitative analysis, explanatory and
predictive models, and fact-based management to
drive decisions and actions.”
Business Analytics (BA) is the process of examining
data sets in order to draw conclusion about the
information it contains.
Analytics is the way of thinking and acting on data.
7. THE HISTORY OF THE EVOLUTION OF BUSINESS
ANALYTICS
Business analytics and technology have improved at exponential rates and will likely
continue to do so as we look to the future. It’s important to see how far this technology
has come to place just how meaningful it has been on business growth throughout
time.
There are clear and direct correlations between the evolution of business analytics
platforms and the booming success of industry expansion.
8. Covering all areas of business transactions, industry,
and verticals, business analytics measure daily
insights in the areas of...
Finance
Sales
Marketing
Social media
Search engine optimization (SEO)
Consumer data
Target audience data
e-Commerce
Human resources
and much, much more...
9.
10. BUSINESS ANALYTICS INITIATIVES CAN HELP
BUSINESSES
Increase revenues: Business analytics is a methodology or
tool to make a sound commercial decision. Hence it impacts
functioning of the whole organization. Therefore, business
analytics can help improve profitability of the business,
increase market share and revenue and provide better return
to a shareholder.
Improve operational efficiency: Facilitates better
understanding of available primary and secondary data, which
again affect operational efficiency of several departments.
Customer service efforts: Converts available data into
valuable information. This information can be presented in any
required format, comfortable to the decision maker.
Provides a competitive advantage: Respond more quickly
to emerging market trends and It is how this information is
utilized makes the company competitive.
11. BUSINESS ANALYTICS VS BUSINESS INTELLIGENCE
Business Analytics measures portrays current data which is the best possible strategy
to deal with it.
On the other hand, Business Intelligence sees the current happenings, and what has
been done in the past to deal with it.
Business Analytics involves the usage of Descriptive, Predictive and Prescriptive
Analysis,
whereas Business Intelligence only uses descriptive analysis
Examples of Business Analytics
Credit ratings/targeted marketing:
•Given a database of 100,000 names, which persons are the least likely to default on
their credit cards?
•Identify likely responders to sales promotions Fraud detection
•Which types of transactions are likely to be fraudulent, given the demographics and
transactional history of a particular customer? Customer relationship management:
•Which of my customers are likely to be the most loyal, and which are most likely to
leave for a competitor?
14. DESCRIPTIVE ANALYTICS
Descriptive analytics answers the question of what
happened. For instance, a healthcare provider will
learn how many patients were hospitalized last
month; a retailer – the average weekly sales
volume; a manufacturer – a rate of the products
returned for a past month, etc.
15. DIAGNOSTIC ANALYTICS
At this stage, historical data can be measured
against other data to answer the question of why
something happened. Thanks to diagnostic
analytics, there is a possibility to drill down, to find
out dependencies and to identify patterns.
16. PREDICTIVE ANALYTICS
Predictive analytics tells what is likely to happen. It
uses the findings of descriptive and diagnostic
analytics to detect tendencies, clusters and
exceptions, and to predict future trends, which
makes it a valuable tool for forecasting.
17. PRESCRIPTIVE ANALYTICS
The purpose of prescriptive analytics is to literally
prescribe what action to take to eliminate a future
problem or take full advantage of a promising trend.
An example of prescriptive analytics from our
project portfolio: a multinational company was able
to identify opportunities for repeat purchases based
on customer analytics and sales history.
18. ANALYTICS LIFE CYCLE
1. Problem Identification
2. Hypothesis formulation
3. Data Collection
4. Data Exploration/preparation
5. Model Building
6. Model Validation and Evaluation
19. PROBLEM IDENTIFICATION
The problem is a situation which is judged to be
corrected or solved
Problem can be identified through
1) Comparative/benchmarking studies
2) Performance Reporting
3) Asking some basic questions
a) Who are affected by the problem?
b) What will happen if problem is not solved?
c) When and where does the problem occur?
d) Why is the problem occurring
e) How are the people currently handling the problem?
20. HYPOTHESIS FORMULATION
Frame the questions which need to be answered.
Develop a comprehensive list of all possible issues
related to the problem.
Reduce the list by eliminating duplicates and
combining overlapping issues.
Using consensus building get down to a major
issue list.
21. DATA COLLECTION
Data collection techniques are
Using data that is already collected by others
Systematically selecting and watching characteristics of
people, objects, and events.
Oral questioning respondents either individually or as a
group
Collecting data based on answers provided by the
respondents in written format.
22. DATA EXPLORATION
Importing data
Variable Identification
Data Cleaning
Summarizing data
Selecting subset of data
23. MODEL BUILDING
Building a Model is a very iterative process
because there is no such thing as final and perfect
solution.
Many of the machine learning and statistical
techniques are available in traditional technology
platform
24. MODEL VALIDATION AND EVALUATION
Like model building the process of validating model
is also a iterative process.
25. CHALLANGES FOR BUSINESS ANALYITCS
Business analytics depends on sufficient volumes of high
quality data.
The difficulty in ensuring data quality is integrating and reconciling
data across different systems, and then deciding
what subsets of data to make available.
Analytics was considered a type of after-the-fact method of
forecasting consumer behavior by examining the number of units
sold in the last quarter or the last year.
This type of data warehousing required a lot more storage
space than it did speed.
Now business analytics is becoming a tool that can influence
the outcome of customer interactions.
When a specific customer type is considering a purchase, an
analytics-enabled enterprise can modify the sales pitch to
appeal to that consumer.
This means the storage space for all that data must react
extremely fast to provide the necessary data in real-time.
