BIG Data

BIG DATADone by

Priya UpadhyayArun choudhury

Big Data at a Glance..Q What is big data?

According to industry analyst Doug Laney (currently with Gartner) – 3Vs

At SAS, which consider two additional dimensions when thinking about big data -

For me more then petabytes data, Now day’s we generates more then 2.5 Exabyte's data/ day

Volume Velocity Variety

ComplexityVariability

Cont..Q Why big data?

Q 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.

Confident decision making

Greater operational efficiencies, Cost reductions and

Reduced risk.

More data Accurate analyses

Emerging Technologies and Trends

Source: EY Global survey

Big Data Framework

Hadoop

Source: Hortonworks

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 non-

transactional 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 user-generated content. More recently, mobile applications and platforms such

as Foursquare provide organizations with location-based 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 non-transactional 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

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.

How Big data is changing Hiring Process.

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

Weaknesses Present technology does not support all

formats Complex logic Human conversations are complicated Huge interpretation is required

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

Threats

Cyber threat May incorrectly predict human behavior Leakage of private data

Thank You