1.
Big Data

2.
Hello!
I am Vikas Samant.
Working With Entrench Electronics and
Pentaho as a Big Data and Data Science
Engineer.
2

3.
What will you
Learn :
Big Data and Data Science
What is Bigdata?
Characteristics of Big Data
3
Big Data use cases
Processing Big Data

4.
What is Big
Data?

5.
“
 Big data is a term that describes the large volume
of data
– structured, semi-structured and unstructured
– that overpower a business on a day-to-
day basis
5

6.
Big data can be analyzed for insights
that lead to better decisions and
strategic business moves.
6
Big Data Contd…

7.
Big Data
Characteristics

8.
Big Data: 3V’s Volume
Variety
8
Velocity

9.
9
Some Make it
4V’s:

10.
 Volume refers to the vast amounts of data generated
every second. We are not talking Terabytes but
Zettabytes or Brontobytes.
 If we take all the data generated in the world between
the beginning of time and 2000, the same amount of
data will soon be generated every minute.
1.Volume
10

11.
 Velocity is the frequency of incoming data that needs
to be processed. The flow of data is massive and
continuous.
 Think about how many SMS messages, Facebook
status updates, or credit card swipes are being sent
on a particular telecom carrier every minute of every
day, and you’ll have a good appreciation of velocity.
2.Velocity
11

12.
 Variety refers to the different types of data we can
now use. In the past we only focused on structured
data that neatly fitted into tables or relational
databases, such as financial data.
 In fact, 80% of the world’s data is unstructured (text,
images, video, voice, etc.) With big data technology
we can now analyse and bring together data of
different types
3.Variety
12

13.
 Veracity refers to the messiness or trustworthiness of
the data. With many forms of big data quality and
accuracy are less controllable .
 Just think of Twitter posts with hash tags,
abbreviations, typos and colloquial speech as well as
the reliability and accuracy of content but technology
now allows us to work with this type of data.
4.Varacity
13

14.
Big Data :
Data Structure
14
Structured
Semi-Structured
“Quasi” Structured
Unstructured

15.
 Data containing a defined data type, format, structure.
 Example: Transaction data and Data in Databases.
1. Structure
Data
15

16.
 Textual data files with a discernable pattern,
enabling parsing.
 Example: XML data files that are self describing and
defined by an xml schema.
2.Semi-
Structure
Data
16

17.
 Textual data with erratic data formats, can be
formatted with effort, tools, and time.
 Example: Web clickstream data that may contain
some inconsistencies in data values and formats.
.
3.Quasi
Sturecture
Data
17
http://www.google.com/#hl=en&sugexp=kjrmc&cp=8&gs_id=2m&xhr=t&q=
data+scientist&pq=big+data&pf=p&sclient=psyb&source=hp&pbx=1&oq=d
ata+sci&aq=0&aqi=g4&aql=f&gs_sm=&gs_upl=&bav=on.2,or.r_gc.r_pw.,cf.
osb&fp=d566e0fbd09c8604&biw=1382&bih=651

18.
 Data that has no inherent structure and is usually
stored as different types of files.
 Example: Text documents, PDFs, images and video.
4.Unstructure
Data
18

19.
Big Data
and
Data Science

20.
How does Big Data relate to Data Science?
Big Data and
Data Science
20

21.
Big Data and
Data Science
21
Data Science is the process of deriving insights from Big data to form decisions
and provide support to Organizations.

22.
Big Data and
Data Science
22

23.
Data Science : Python and R
Big Data and
Data Science
23

24.
Data Science Process
24

25.
Big Data Use
Cases:

26.
26
BIG D A T A USE C A S E S :
1 . O p t i m i z e F u n n e l
C o n v e r s i o n
2 . B e h a v i o r a l
A n a l y t i c s
3 . C u s t o m e r
S e g m e n t a t i o n
4 . F r a u d
D e t e c t i o n

27.
1. Optimize Funnel
Conversion
27

28.
28
1. OPTIMIZE FUNNEL
CONVERSION
Big data analytics allows companies to track leads through the
entire sales conversion process, from a click on an adword ad
to the final transaction, in order to uncover insights on how the
conversion process can be improved.

29.
COMPANY
T-Mobile
Industry
Communication
Employees
38000
Type
Optimize Funnel
Conversion
Purpose:
T-Mobile uses multiple indicators, such as billing and sentiment analysis, in
order to identify customers that can be upgraded to higher quality products,
as well as to identify those with a high lifetime customer-value, so its team
can focus on retaining those customers.

30.
2. Behavioral
Analytics
30

31.
31
2. Behavioral analytics
With access to data on consumer behavior, companies can
learn what prompts a customer to stick around longer, as well
as learn more about their customer’s characteristics and
purchasing habits in order to improve marketing efforts and
boost profits.

32.
COMPANY
Nestle
Industry
Food and
Beverage
Employees
38000
Type
Behavioral Analytics
Purpose:
Customer complaints and PR crises have become more difficult to handle thanks
to social media. To better keep track of customer sentiment and what is being said
about the company online, Nestle created a 24/7 monitoring center to listen to all
of the conversations about the company and its products on social media. The
company will actively engage with those that post about them online in order to
mitigate damage and build customer loyalty.

33.
3. Customer
Segmentation
33

34.
34
3. CUSTOMER SEGMENTATION
By accessing data about the consumer from multiple sources,
such as social media data and transaction history, companies
can better segment and target their customers and start to make
personalized offers to those customers.

35.
COMPANY
Heineken
Industry
Food and
Beverage
Employees
64270
Type
Customer
Segmentation
Purpose:
Thanks to its partnerships with Google and Facebook, Heineken has access
to vast amounts of data about its customers that it uses to create real-time,
personalized marketing messages. One project provides real-time content to
fans who happen to be watching a sponsored event.

36.
4. Fraud Detection
36

37.
37
7. FRAUD DETECTION
Financial firms use big data to help them identify
sophisticated fraud schemes by combining multiple points
of data.

38.
COMPANY
Discovery
Health
Industry
Insurance
Employees
5000
Type
Fraud
Detection
Purpose:
Discovery Health uses big data analytics to identify fraudulent claims and
possible fraudulent prescriptions. For example, it can identify if a healthcare
provider is charging for a more expensive procedure than was actually
performed.

39.
Processing
Big Data

40.
Big Data
Technologies
40

41.
Big Data
Vendors
41

42.
What is Hadoop
Framework
42
Hadoop is an open source framework that supports the processing
and storage of extremely large data sets in a distributed computing
environment with commodity Hardware‘s.

43.
Why Hadoop?
43
Studies show, that by
2020, 80% of all
Fortune 500
companies will have
adopted Hadoop.
A study at McKinsley Global Institute predicted that by 2020, the annual
GDP in manufacturing and retail industries will increase to $325 billion
with the use of big data analytics.

44.
Thanks!
Any questions?
44