big data analytics

1. Big Data Analytics and its use in Digital Marketing
Group 6
Members:
Mr. Sadanand Gupta (2015PGPMX022)
Mr. Samir Shah (2015PGPMX023)
Mr. Sanmeet Dhokay (2015PGPMX025)
Mr. Vishit Trivedi (2015PGPMX030)
11th June, 2017

2. Introduction
• Big Data burst upon the scene in the first decade of the 21st century is the Next Big Thing in the IT
world.
• The first organizations to embrace it were online and startup firms.
• Firms like Google, eBay, LinkedIn, and Facebook were built around big
data from the beginning.
• Like many new information technologies, big data can bring about dramatic cost reductions,
substantial improvements in the time required to perform a computing task, or new product and
service offerings.
• Big Data generates value from the storage and processing of very large quantities of digital
information that cannot be analyzed with traditional computing techniques.

3. The Model Has Changed…
• The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming data
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4. How much data?
• Google processes 20 PB a day
• Facebook has over 2.5 PB of user
data + 15 TB/day and handles 40
billion photos from its user base.
• eBay has 6.5 PB of user data + 50
TB/day (5/2009)
• Walmart handles more than 1
million customer transactions
every hour.

5. Who is Generating Big Data
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time)
Sensor technology and networks
(measuring all kinds of data)
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6. Characteristics of Big Data
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7. Big Data Analytics
Traditional Analytics (BI) Big Data Analytics
Focus on • Descriptive analytics
• Diagnosis analytics
• Predictive analytics
• Data Science
Data Sets • Limited data sets
• Cleansed data
• Simple models
• Large scale data sets
• More types of data
• Raw data
• Complex data models
Supports Causation: what
happened, and why?
Correlation: new
insight More accurate
answers
vs
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8. Merging the Traditional and Big Data Approaches
IT
Structures the data
to answer that
question
IT
Delivers a platform to
enable creative
discovery
Business Users
Explores what questions
could be asked
Business Users
Determine what
question to ask
Monthly sales reports
Profitability analysis
Customer surveys
Brand sentiment
Product strategy
Maximum asset utilization
Big Data Approach
Iterative & Exploratory Analysis
Traditional Approach
Structured & Repeatable Analysis
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Structured
vs.
Exploratory

9. Challenges in Handling Big Data
• The Bottleneck is in technology
• Data volume, Processing capabilities, New architecture, algorithms,
techniques are needed
• Also in technical skills
• Experts in using the new technology and dealing with big data
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10. 10
Power of Big Data

11. Applications of Big Data (Sectors)
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12. Applications of Big Data Analytics
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Insights from website visitors:
• How they reached your site
• The pages they visited
• What they bought
• How long they stayed on the site
• What page they were on when they left
And much more……
 What their occupation is
 Their level of education
 How much they earn
 If they have a family to support
 Their financial priorities
 How they like to spend their disposable income
 Why they have/haven’t bought from you
 What they were looking for when they came to your site
 Whether they found what they were looking for when they
came to your site

13. Applications of Big Data Analytics
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For instance, Google Analytics can tell us:
• If certain pages of our site have higher than expected
exit rates.
• Which pages of our site keep visitors engaged the
longest (we can use this in collaboration with exit data to
compare our best and worst performing pages and make
changes accordingly).
• Which devices your visitors are using (and aren’t using)
to access your website. This allows developers to
prioritize optimizing the functionality of the site for the
devices your visitors are actually using.

14. Big data: This is just the beginning
2010
VolumeinExabytes
9000
2025
Percentage of uncertain data
Percentofuncertaindata
You are here
Sensors
& Devices
VoIP
Enterprise
Data
Social
Media
3000
6000
100
0
50
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Veracity
Source: IBM Global Technology Outlook 2012 IBM source data is based on analysis done by the IBM Market Intelligence Department. IBM Market Intelligence data
is provided for illustrative purposes and is not intended to be a guarantee of future growth rates or market opportunity
Volume
Variety

15. References:
- http://mattturck.com
- http://www.smartinsights.com
- http://www.infodiagram.com
- IBM Global Technology Outlook
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16. Thank You!!!
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