study on what exactly is big data and its usage in the real life
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BIG DATA
Done by
Priya Upadhyay
Arun choudhury
Big Data at a Glance..
What is big data?
For me more then petabytes data, Now day’s we
generates more then 2.5 Exabyte's data/ day
According to industry analyst Doug Laney (currently with
Gartner) – 3Vs
Volume
Velocity
Variety
At SAS, which consider two additional dimensions when
thinking about big data Variability
Complexity
Cont..
Why big data?
More
data
Accurate
analyses
Confident decision
making
Greater
operational
efficiencies,
Cost
reductions and
Reduced risk.
Should matter to you?
Determine root causes of failures, issues and defects in near-real
time, potentially saving billions of dollars annually.
Big data may be as important to business – and society –
as the Internet has become.
Emerging Technologies and
Trends
Source: EY Global
survey
Big Data Framework
Hadoop
Source:
Value Beyond Open Source
Technical differentiators
,
– Built-in analytics
• Text processing engine, annotators, Eclipse tooling
• Interface to project R (statistical platform)
– Enterprise software integration (DBMS, warehouse)
– Simplified programming / query interface (Jaql)
– Integrated installation of supported open source
– Web-based management console
– Platform enrichment: additional security, job scheduling options
performance Feature, world-class support…
Business benefits
– Quicker time-to-value
– Reduced operational risk
– Enhanced business knowledge with flexible analytical platform
– Leverages and complements existing software assets
Big data Strategies
Performance Management
Performance management involves understanding the meaning of big
data in company databases using pre-determined queries and
multidimensional analysis.
The data used for this analysis are transactional, for example, years
of customer purchasing activity, and inventory levels and turnover.
Managers can ask questions such as which are the most profitable
customer segments and get answers in real-time that can be used to
help make short-term business decisions and longer term plans.
Data Exploration
This approach leverages predictive modelling techniques to predict
user behaviour based on their previous business transactions and
preferences.
Cluster analysis can be used to segment customers into group.
Once these groups are discovered, managers can perform targeted
actions such as customizing marketing messages, upgrading service,
and cross/up-selling to each unique group. Another popular use case
is to predict what group of users may “drop out.”
Armed with this information, managers can proactively devise
strategies to retain this user segments.
Social Analytics
Social analytics measure
the vast amount of nontransactional data. Social
analytics measure three
broad categories:
awareness, engagement,
and word-of-mouth
Awareness looks at the
exposure or mentions of social
content and often involves
metrics such as the number of
video views and the number of
followers or community
members.
Engagement measures the
level of activity and interaction
among platform members,
such as the frequency of usergenerated content. More
recently, mobile applications
and platforms such as
Foursquare provide
organizations with locationbased data that can measure
brand awareness and
engagement, including the
number and frequency of
check-ins,
Decision science
Decision science involves experiments and analysis of nontransactional data, such as consumer-generated product ideas
and product reviews, to improve the decision-making process.
decision scientists explore social big data as a way to conduct
“field research” and to test hypotheses.
Crowd sourcing, including idea generation , enables companies
to pose questions to the community about its products and
brands. Decision scientists, in conjunction with community
feedback, determine the value, validity, feasibility and fit of
these ideas and eventually report on if/how they plan to put
these ideas in action.
For example, the Starbucks Idea program enables consumers
to share, vote, and submit ideas regarding Starbuck’s
products, customer experience, and community involvement.
Application of big data
How Big data is changing
Hiring Process.
Catalyst IT Services, a Baltimore-based technology
outsourcing company that assembles teams for
programming jobs
Catalyst ,asks candidates to fill out an online assessment.
Catalyst uses it to collect thousands of bits of information
about each applicant, in fact, it gets more data
from how they answer than what they answer.
Cont.
Someone who labors over a difficult question might fit an
assignment that requires a methodical approach to problem
solving, while an applicant who takes a more aggressive
approach might be better in another setting.
Analyzing millions of data points can show what attributes
candidates have that fit in specific situations—something human
bias can't do.
For one measure of success, employee turnover at Catalyst is
only about 15% a year, compared with more than 30% for its
U.S. competitors and more than 20% for similar companies
overseas.
Big Data In The Amazing World of Gaming
Zynga ,the San Francisco game maker behind FarmVille,
Words with friends, and Zynga Poker. snares 25 terabytes a
day from its game.
Big data can help capture customer preferences and put that
information to work in designing new products.
The data that they pull from Facebook is used to offer
marketers a precise demographic target for their segmented
online campaigns.
Cont.
Big Data also plays a part in designing the games.
Zynga’s smartest Big Data insight was to realise the
importance of giving their users what they want, and to this
end monitored and recorded how its games were being
played, using the data gained to tweak gameplay according
to what was working well.
Implications for Finance
The finance industry should be the first to benefit
All types of risk assessment and reduction
Investor behavior analysis that changed after the credit bust of 2008
• Lets take a look at How That Can Be Done?
Customer traits can be gathered
E.g. past purchasing behavior, social network activities, lifestyle. etc…
etc
The more the data the better the risk profiling
Insure the box implements this strategy in providing insurance to
drivers
It looks at acceleration, deceleration, and other patterns to form an
algorithms to tailor an insurance policy
Pattern Detection and Risk
Reduction
•Enterprise Risk Management
Can be used for enterprise risk management
The management taking loan can be assessed
The guiding elements could be claims, new business, investment
management factors or even lifestyle of managers
Better risk management can be extracted out of this procedure
•Anomalies Finder
Deviation from usual pattern can be easily detected (outliers)
E.g. can find out when a credit card is used in distant locations in no time
Fraud transaction can be prevented in advance
Visa has 500 analysis aspects to look at any transaction
It has more at stake to consider big data
•Preventing ATM Robbery
ATMs can be monitored
Old-school robbery styles can be easily detected
and prevented
Improved Customer
Satisfaction
Banks can integrate all information of a client in a coherent system to
expedite the interactions
Online tools can be improved when all customer feedback in taken
into account
• Social Media
Perhaps, the biggest advantage is for social media
They have vast number of users; e.g. Whatsapp, Facebook, Viber,
Twitter etc.
Real-time intel, and their responses toward new products, services
and advertisements
The usage of products can guide social network firms in designing
their next moves: That’s how Facebook is so successful.
How people use the app, how long they stay on it for, what they do
over here, location they log in to at, etc…etc…..
It is all done in instants
imagine the cost and time savings that would have been incurred on
surveys
Can Boost Sales & Lower
Costs
Plastic money can reveal a lot about consumer behavior
Take a couple for example entering a supermarket together
When this monitoring is imported to financial institution it
can be deployed in a smart way
E.g. when to start retirement plan, or offer a more lucrative
return instrument
Call centers can muster the data such as voice recognition,
social comments or emails to analyze the future and modify
their staff capacity
SWOT Analysis
Strengths
Helps in analytics in Science,
Medical, etc.
New horizon of statistical research
Support from all industries
Cloud computing made it easier to
adopt to
Opportunities
More adaptive people
Next big opportunity for
investment
Now all sort of data can be
processed
Huge information management for
e-commerce and social media
Weaknesses
Present technology does not
support all formats
Complex logic
Human conversations are
complicated
Huge interpretation is required
Threats
Cyber threat
May incorrectly predict human
behavior
Leakage of private data