Business analytics uses quantitative processes to help businesses make optimal decisions and discover business insights from data. It includes statistics, prediction, and optimization. Big data refers to extremely large data sets that are too large to be processed with traditional data processing applications. The combination of business analytics and big data allows businesses to gain insights from large, diverse data sets to improve processes, make predictions, optimize performance, and support decision making through techniques like predictive modeling, forecasting, and response analysis. Examples include a bank using a Hadoop cluster to build a more accurate risk score to better manage customer portfolios.

1. Business analytics and Big Data
Author: Abhishek kapoor
Twitter: @kapoorsunny

2. What is Business analytics(BA)
Quantitative processes for a business to arrive at optimal
decisions and to perform business knowledge discovery.

3. Types of Business analytics
• Statistics
Statistics is the study of the collection, organization, analysis, interpretation,
and presentation of data. It deals with all aspects of this, including the
planning of data collection in terms of the design of surveys and
experiments.
• Prediction
Analysis on patterns found in historical and transactional data to identify
risks and opportunities. Models capture relationships among many factors to
allow assessment of risk or potential associated with a particular set of
conditions, guiding decision making.
• Optimization
Process using statistical information and tools provided to fine tune the
process. Includes a mathematical modeling, an easy-to-use development
and visualization environment, and a state-of-the-art set of optimization
algorithms.

4. Whoa !!!!
Every day, we create 2.5 quintillion bytes of data

5. What is Big data
According to wikipedia
Big data is a collection of data sets.
Larger data sets is due to the additional information derivable
from analysis of a single large set of related data, as compared
to separate smaller sets with the same total amount of data.
Any information provided by any thing is when consolidated is a
part of Big Data.

6. Union of Business analytics and Big Data

7. Business optimization and Big Data
• Big data inspires new ways to transform processes,
organizations, entire industries. creating unique predictive
capabilities for intelligent decisions.
• Big data can fine tune the business process with the help of
tools it provides

8. Example of Business optimization and Big Data
• A large financial institution took separate data warehouses
from multiple departments and combined them into a single
global repository in Hadoop for analysis.
• The bank used the Hadoop cluster to construct a new and
more accurate score of the risk in its customer portfolios. The
more accurate score allowed the bank to manage its exposure
better and to offer each customer better products and advice.

9. Business Statistics and Big Data
Terabyte to Petabyte, structured to unstructured , big data
technology allows to store and process the data in an business
efficient way. And provide best possible information from data in
order to aid decision making.
This includes everything from planning for the collection of data
and subsequent data management to end-of-the-line activities
such as drawing inferences from data and presentation of
results.

10. Example of Business Statistics and Big Data
Technologies like HDFS (Hadoop filesystem, HBASE, Neo4j)
provide ability to store data of any form in a secure and fault
tolerant manner.
Map Reduce technology can interpret the domain specific data
With the help of interpreted data, business can make various
decisions.

11. Business Prediction and Big Data
Big data Tools and application programming interfaces can be
used to estimate or predict future patterns using business data.
Predictive analytic techniques generally take large amounts of
historical big data and detect patterns in said data that can be
used to predict the future, often by assigning a probability to
how likely something is to occur. A wide variety of techniques
can be used to build predictive analytic models

12. Examples Business Prediction and Big Data
Forecasting estimating quarterly sales, product demand.
Neural networks can assess how likely it is that a credit card
transaction is being performed by the cardholder.
Response models can predict how likely a particular person is to
respond to a particular marketing offer, based on the success or
failure of offers made in the past.
Predictive scorecards can determine the likelihood that someone
will fail to make payments on his or her loan in the coming year.

13. Thanks
Author Credential
Name: Abhishek kapoor
Twitter: @kapoorsunny