TECHNOLOGY
Big Data in Financial Services
Business intelligence technology brings big data analytics
within reach of small businesses.
By Robert L. Burrows, MBA
Over the past 15 years, technological innovations dramatically decreased the cost
of computing power, data storage and data transmission. No process has been more
impacted by this trend than knowledge management and analysis.
In 1999, one megabyte of RAM (random access memory), which represents the
“computing horsepower” of a computer, cost approximately $2.35, but today the price is
approximately $.0051. The cost of one gigabyte of hard drive data storage in 1999 was
nearly $90, but today the price has dropped to approximately $0.062. In 1998, to stream
a two-hour movie at the same quality possible today, the bandwidth would have cost
Robert L. Burrows is
program director, Business
Intelligence Master
of Science at Full Sail
University, and member
of Digital Analytics
Association and The Data
Warehousing Institute.
Prior to Full Sail, Rob
spent over 25 years as an
executive with software
and technology companies
in the Internet, healthcare
and telecommunication
industries.
Robert.Burrows@wcinput.
com
FALL 2014 THE AMERICAN COLLEGE 71
big data in
financial services
Data Format (%)
around $270, but today that cost has dropped to about $0.053.
These changes represent decreases of more than 99 percent in
the prices of the key infrastructure components of knowledge
management systems.
Practically speaking, this trend means it is essentially free
for today’s businesses to acquire, store and process the incremental byte or billion bytes of data. Historically, the process of
extracting information about customers, operations, transactions and markets required businesses to spend time and money
on expensive surveys, focus groups, IT systems and database
services. Today, businesses have the ability to collect and analyze
enormous quantities of data inexpensively and relatively easily.
Realizing its value, businesses and governments decided to
collect and analyze every bit of data they can get their hands on.
Welcome to the world of big data.
Each day, approximately 2.5 quintillion bytes of data are
generated4. This phenomenon, volume, is one of the three characteristics of big data. The other two characteristics are velocity
and variety. Velocity refers to the speed at which the data is
created. In 2000, worldwide data storage capacity was estimated at 50 exabytes — an exabyte equals 1 billion gigabytes.
This capacity is estimated to have grown over 50-fold to 2,600
exabytes in 2012, and is projected to be over 7,200 exabytes in
2017, as shown in the following chart5.
This spectacular increase in data generation means that by
2017, over 95 percent of the world’s stored data will have been
created in only 15 years. The third characteristic, variety, is the
major source of big data’s complexity. Data is now available in
many formats, not only in neatly structured data files. Storage
technology has made it possible to
digitize and store video, audio and
photo files, as well as equipment sen100
sor data. Businesses large and small
90
are developing their capabilities to
80
take advantage of this rich trove
of information. The data mining,
70
analytic and reporting processes uti60
lized to identify patterns, trends and
50
correlations are the key components
40
of business intelligence (BI).
WHAT ROLE CAN BI PLAY IN THE
FINANCIAL SERVICES INDUSTRY?
According to a 2013 survey by
the University of Oxford and
IBM’s Institute of Business
72 THE WEALTH CHANNEL MAGAZINE FALL 2014
Value, 71 percent of businesses in banking, insurance,
credit card issuance, credit rating, brokerage and investment management had already started “using big data and
analytics, up from 36 percent just two years ago.” The rationale is straightforward: Companies see these techniques
as a way to develop valuable competitive advantages.
Companies such as FICO, Visa, Schwab and JP Morgan are
examples of this big data-mining trend. Founded by Bill Fair
and Earl Isaac in 1956, FICO has a long-established tradition
of using data to improve business decision-making, beginning with services such as credit scoring. Today, FICO offers
a wide range of analytic and decision management products.
Credit card issuers such as Visa use big data to protect their
cardholders from fraudulent charges by analyzing and tracking
customers’ transactions. Charles Schwab uses data generated
by customer interactions with its website to optimize online
experiences and provide better service. JP Morgan used analytics to develop a tool that encourages employees to adopt more
effective investment methods.
HOW CAN A SMALL BUSINESS
IMPLEMENT BI CAPABILITIES?
Lacking the resources of big financial services companies, small to medium-sized businesses (SMBs) can
be overwhelmed by the perceived difficulty of implementing BI capabilities in their business operations.
Using a modular approach, SMBs can grow their BI toolkits
in a focused, effective and affordable manner. Setting priorities
is the critical step in this process. In the same 2013 University
DATA EXPLOSION
Analog Format
Digital Format
8,000
7,000
6,000
5,000
4,000
3,000
30
2,000
20
1,000
10
0
0
2000
2007
2012
est.
2017
Proj.
SOURCE: Hillbert and Lopez. “The World’s Technological Capacity to Store, Communicate, and Compute Information” Science, April 2011
Exabytes
TECHNOLOGY
of Oxford and IBM’s Institute of Business Value survey, “… 55
percent of financial industry respondents pinpointed customer-centric projects as their top priority.” Their objective is to
better understand their customers so they can deliver better
service — driving satisfaction and potentially add-on sales.
These customer-centric projects can be readily launched with
data that an SMB already collects through its various business
systems. This internal data is the foundation of any big data
project. Sifting through transactions, demographic data and
contact notes is the first step in implementing customer-centric
analytic projects.
These internal data sources can be augmented with hundreds
of public datasets provided by government, academic and international organizations. Examples of these public data sources
available to businesses include:
• Data.gov (data.gov) provides hundreds of free data sets covering financial services like banking, lending and insurance.
• Google Public Data Explorer (google.com/publicdata/
directory) makes available datasets and projections from a
variety of academic institutions, government and international organizations.
• DataMarket (datamarket.com) aggregates thousands of
datasets from U.S. and international sources.
Another key element in the data-mining process is using the
right BI tool to analyze the data. The proliferation of cloudbased applications has provided more options at affordable
prices. SMBs are no longer limited to Excel. New tools
are available that deliver greater analytic, visualization and
data-processing functionality. Some vendors for an SMB to
consider include:
• GoodData – gooddata.com
• Tableau – tableausoftware.com
• Qlikview – qlik.com
• Pentaho – pentaho.com
• Indicee – indicee.com
Continued advances in BI technology are bringing big data
analytics within reach of small businesses. The key to a successful big data project is to decide what question needs to be
answered before choosing a particular BI tool.
1 - reference: jcmit.com/memoryprice.htm
2 - reference: jcmit.com/diskprice.htm
3 - reference: http://blog.streamingmedia.com/2010/01/bandwidth-pricingtrends-cost-to-stream-a-movie-today-five-cents-cost-in-1998-270.html
4 - reference: storagenewsletter.com/rubriques/market-reportsresearch/
ibm-cmo-study/
5 - reference: sciencemag.org/content/332/6025/60
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