Course Name
Course sub Name and
sub captions
B i g d a t a a n d
“THE NEXT FRONTIER FOR INNOVATION,
COMPETITION, AND PRODUCTIVITY”
MCKINSEY CONSULTING
Big data will create 34$ billion in 2013.
Most of the spending will involve upgrading
‘transitional solutions’ to handle the flood of data
entering organizations from a variety of sources”
Big data – the 3Vs
Vast amount of data (terabytes, petabytes)
speed of data in and out (Real time or near
range of data types and sources (logs,
Big data – variety Structured data
ContactName ZipCode CustomerName CustomerID Roi 77123 Din 10 Ido 77568 Hagit 11 Noa 77856 Penni 13 ShipData OrderDate ProductID CustomerID OrderNbr 1.7.13 1.6.13 1111 10 100001 1.8.13 5.7.13 2222 11 100004Big data – variety
Unstructured Data
Unstructured data
Big. Non –relational MessyNot easily represented in RDBMS.
Today, data is born digital.
Books, newspapers, Video, Photos and etc.
“Unstructured information accounts for more than 80 percent
of all data in organizations” From ClaraBridge.
Big data – variety MetaData
Examples:
•
Tags
•
Time and date
•
Headers
•
GPS information about a picture.
•
Smartphone, Workstation.
The ration of structured to unstructured data
Big data – variety
You have to integrate different sources to get most value
out of it!
Big data
... there are known knowns; there
are things we know that we know.
There are known unknowns; that is
to say, there are things that we
now know we don't know.
But there are also unknown
unknowns – there are things we do
not know we don't know.
Why to consider a big data solution?
A statistical approach
Long tail
The long tail was popularized by Chris Anderson in an
October 2004 Wired magazine article.
Anderson claims that Amazon.com, Apple and Yahoo!
as examples of businesses applying this strategy
Long tail
A
long tail
of some distributions of
numbers is the portion of the distribution
having a large number of occurrences far
from the "head" or central part of the
Big data is extremely fragmented
•
In 1980s the series M*A*S*H became the most watched
television broadcast in the history of television (about 125m).
•
Back then customer habits were fairly homogenous.
•
Same books, songs, newspapers, movies and etc.
•
In 2001 the most popular show in the world is
CSI (63m viewer around the world).
•
There is no mass market anymore.
The current state of using data in
organization- questions
-
Sales
-
Which ones are selling better than others?
o
Which customers are buying from France?
- Marketing:
o
What is our company’s market share?
o
How has that change from last year or last quarter?
- Supply chain:
o
What are our current inventory levels of key products and parts?
o
Will we have enough inventory to meet current and future
Critical business questions
in the big data age.
- Disconnect and fragmented data.
- The told tools can’t address many questions.
Examples:
◦
How do our customer feel about our products or
customer service?
◦
What products would our customer consider buying?
◦
When is the best time of the year to launch a new
product?
◦
What are people publicly saying about our latest
commercial or brand?
Revenue of almost USD 30 billion.
Macy's is considered the largest store operator in the USA.
The company manages to run a daily price check analysis of its 10,000 articles in less than two hours
Searching for aggressive price reductions.
If there is no market competitor, the prices remain unchanged.
→T-Mobile USA has integrated Big Data across multiple IT systems to combine customer transaction and interactions data in order to better predict customer defections.
→By leveraging social media data (Big Data) along with transaction data from CRM and Billing systems, T-Mobile USA has been able to “cut
customer defections in half in a single quarter”.
Use case: Risk management
Deduce Customer Defection
Assets over $2.2 trillion in 2012.
50 million consumers and small customer.
The bank is focusing in big data.
They put emphasis on integrated approach to customer
and an integrated organizational structure.
- The bank utilizes transaction and propensity models to
determine types of their customer.
- The various sales channels can also communicate with each
other.
- A customer who starts an application online but does’t
complete it, get a follow-up offer in the email, or up an
appointment at a physical branch location.
Starting a big data projects
Common mistake in big data projects:
Create a big data environment and then go looking for
problems to solve with it.
The right way:
1. Start with the problem the business wants to solve.
This will determine the analytic techniques, data sources
and what it means to have ‘quality’ data.
Why should you care about big data?
•
Silence the HiPPOs (highest-paid person‘s opinion)
•
Being able to interpret unimaginable large data
stream.
•
You have to care about big data to be competitive in
the future!
Move from “governance by instinct” to
governance
by data
.
How to process big data?
Use cases-analytics
Banks and finances.
spending pattern, credit reviews. E-Commerce
buying patterns, Increase customer satisfaction and prevent churn and Design price plans & Best offers
security analysis, Predicting fraud
Social networking and Content Management systems
analyzing social media feeds for product sentiments. Insurance
