big data


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BIG DATA, BIG MONEY How Banks Are Turning Data Growth Into a Competitive Advantage What does Big Data mean to a financial institution in terms of its practical impact, and how can Big Data methodologies improve an institution’s risk and decision-making processes? This infographic offers some answers to these important questions.

DATA STORAGE AND GROWTH IN THE BANKING INDUSTRY

¬

2020

44.5 EB

WHAT IS AN EXABYTE? 1 exabyte = 1,000 petabytes (or one quintillion bytes)

1 petabyte = 1,000 terabytes 1 terabyte = 1,000 gigabytes 1 gigabyte = 1B bytes

440%

GROWTH In 2009, U.S. banks and capital markets firms were estimated to hold more than 1 exabyte (EB) of stored data.

10 EB

Total U.S. bank and capital market stored data (based on a growth rate in line with total global data growth estimated from other sources) will increase to 10 EB in 2015 and 44.5 EB in 2020 – growth of more than 440%.

2015

Securities and investment firms with 1,000 or more employees store an average of 3.9 PB of data.

That’s more per-firm data than comparably sized firms in any other sector, including communications/ media (1.8 PB per firm on average), utilities (1.5 PB), government (1.3 PB), insurance (.87 PB), and manufacturing (.83 PB).

Banks with more than 1,000 employees store an average of 1.9 PB of data.

THE “FOUR V’S” THAT DEFINE “BIG DATA” Big Data isn’t just about more data – it’s about where that data comes from, how quickly it moves, and whether it can be put to productive use.

VOLUME

More data flowing into banks than ever before – and exponential data growth

Data commonly arrives in near-time and real-time streams

V

VARIETY

VELOCITY

Structured and unstructured data, internal and external data

VERACITY

The credibility and reliability of the data used to support decision-making activities

INITIATIVES LAUNCHED TO MAXIMIZE THE VALUE OF DATA

40%

76% IT

According to International Data Corp., more than 40% of all banks are ramping up to launch big data and analytics business technology strategies.

76% say thought leadership on Big Data within their firms is a collaborative effort of business and IT.

Many respondents said the chief goal is to improve the ability to analyze diverse data sources and new data types – not simply manage very large data sets.

54%

RELATED FINDING: 54% of European

retail banks surveyed in late 2012 reported that analyzing Big Data to improve their understanding of customers’ needs and risk was a top priority.

TURNING BIG DATA IDEAS INTO MEASURABLE RESULTS

V8

RISK AND FRAUD PREVENTION An Asia-Pacific bank used high-performance analytical techniques to calculate a range of liquidity-risk measures. It analyzed a portfolio of 30 million complex cash-flow investments in 50,000 scenarios – in less than 8 hours.

HOURS

x 96 HOURS

100 HOURS

4

A large U.S. bank used high-performance analytics to reduce its loan-default calculations on a portfolio of 10 million mortgages from 96 hours to just 4 hours.

5

A large U.S. bank used high-performance analytics to reduce from more than 100 hours to less than 5 minutes the time needed to identify problem loans – preventing tens of millions of dollars in charge-offs.

HOURS

MINUTES

CUSTOMER INTELLIGENCE AND RETENTION Visa used the Apache Hadoop™ standard for storing and processing data to analyze two years of customer transaction records – 73 billion transactions totaling 36 TB of data. The processing time required for the analysis fell from one month to 13 minutes – more than 3,300 times faster.

A global payments firm working with more than 15,000 firms issuing more than 1.9 billion credit and debit cards – and more than 75 billion transactions valued at almost $6 trillion – analyzes 20,000 transactions per second across 17 dimensions to understand client-product relationships and drive pricing and strategy decisions.

$250 MILLION

A U.S. financial services firm uses data from 17 million customers and 19 million daily transactions – generating more than 10,000 database variables – as an early-warning system to help detect customer disengagement. The bank’s solution generates more than $250 million in annual incremental revenue and reduces the costs associated with acquiring replacement customers.

Big Data allows banks to make better, smarter, faster decisions. Is your institution ready to take advantage of this opportunity?

Sources: The Economist, FICO, Forrester Research, Infosys, McKinsey Global Institute, NewVantage Partners, SAS Institute, Crowe Horwath LLP

www.crowehorwath.com

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