Analyzing Big Data: The Path to Competitive Advantage

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IntroductionAlmost every business collects data, and the majority of it is about their customers. Over the

past several years the cost of storing data has decreased substantially, thanks mostly to cloud

solutions. Now companies gather huge amounts of information and store it just in case it may

turn out to be useful someday. Accumulating and storing data are easy. Analyzing data to

figure out what is valuable is much more complex. And knowing how to extract meaningful,

actionable insights is even more difficult, but is crucial to bolstering the bottom line.

Big Data can solve those problems. With the appropriate software and tools, companies can

comb through the nonproductive, non-revenue-producing data in storage and analyze it to

identify business trends and reveal new opportunities.

Big Data is different from ordinary database information because of the massive volume, the

variety (structured, semi-structured, and unstructured), and the velocity. Now information such

as product reviews, email, video, blogs, and tweets are in the data mix. Social media feedback

has become a useful research tool for businesses, but sophisticated analysis of the massive

quantities was not possible before analytic tools were developed specifically for Big Data.

Additionally, Big Data encompasses data generated by machines such as sensors.

How Big Is Big Data?In 2000, 800,000 petabytes of data were stored worldwide. (One thousand terabytes equals

one petabyte.) 2012 alone created 2.8 zettabytes of data. (One zettabyte is one million

petabytes.) By 2020, it is estimated that the world will have 40 zettabytes of data stored.

Why Use Big Data?Big Data analytic tools facilitate the examination of large amounts of different types of data to

reveal hidden patterns and correlations that are not otherwise easily discernible. Increasingly,

companies are spotting trends from social media that they quickly convert into new products

or add to existing products. Big Data can unlock significant value by making information

transparent. It helps companies make smarter, faster, and more strategic decisions.

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IBM’s Big Data @ Work Survey of 1144 professionals found that 63 percent of respondents

reported that the use of information including Big Data and analytics is creating a competitive

advantage for their organizations. While over 50 percent of respondents stated that they had

not yet begun Big Data analysis, 47 percent planned to do so.

In another study, The Deciding Factor: Big Data & Decision Making, conducted by the

Economist Intelligence Unit for Capgemini, two-thirds of the executives note that they

consider their organizations to be “data-driven,” meaning that data collection and analysis

are the foundation of their firms’ business strategies and day-to-day decision-making. The

percentages are highest in the energy, financial services, healthcare, and pharmaceuticals

industries. More than half of the respondents say management decisions that are based purely

on intuition or experience are increasingly regarded as questionable. And 65 percent affirm

that more management decisions are based on validated analytic information. That figure rises

to 73 percent for the financial services sector.

Big Data allows companies to make better decisions using existing and new data sources.

It offers the capability to better understand and predict consumer behavior. Real time data

capture enables faster decision-making, such as when a customer is on a website or on the

telephone speaking with a customer service representative.

The use of Big Data has improved the performance of businesses by 26 percent on average

and that influence will grow to 41 percent over the next three years, according to the

Capgemini study.

How Industries Use Big DataRetail

Retail is an obvious early adopter of Big Data, with rich customer data helping merchants

to personalize customer service and better manage inventory levels. Merchandising is

currently the number one use of Big Data in retail. Online merchants can analyze data on

visitor browsing patterns, login counts, past purchase behavior, and responses to promotions.

Merchants can eliminate what isn’t working and focus on what does. Some analytic solutions

are so finely tuned to dynamic pricing, they can tell a merchant whether it needs to offer a 25

percent discount or if a 15 percent discount will suffice for a particular customer according

to Retention Science, a software company that helps clients retain customers using Big Data

analytics. Similar products can be cross-sold within seconds to a customer paying at the cash

register. Data analysis also allows for tighter control of inventory so items aren’t overstocked..

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Automotive

Big Data is being utilized to accelerate product design, improve vehicle performance and

augment the driver experience. Sensor-generated data from onboard communications, GPS,

and telematics will become more important as manufacturers move towards the “connected

vehicle” with more than 10,000 sensors per vehicle generating data that streams back to the

manufacturer. Big Data analytics will propel predictive maintenance as sensors provide data

that can quickly flag atypical events and alerts drivers to proactively take corrective actions.

Healthcare

More and more scientific advances owe their success to the ability of supercomputers to

rapidly crunch data. Currently, large datasets and algorithms present tantalizing opportunities

just waiting to be examined. With Big Data, that progress could extend to the development

of models and systems that track, sort, and analyze the development of complex biological

systems. Researchers could effectively polish their diagnostic and predictive abilities to design

and deliver solutions in fundamental research, drug design, delivery, and eventual cure.

Big Data can be helpful in designing inclusion and exclusion criteria for clinical trials,

performing predictive models on virtual trials, identifying patients for recruitment, and

identifying unintended uses and indications. The McKinsey Global Institute estimates that

applying Big Data strategies to healthcare decision-making could generate up to $100

billion annually in the United States alone. In their article, “How Big Data can revolutionize

pharmaceutical R&D,” authors J. Cattell, S Chilukuri, and M. Levy foresee that predictive

modeling of biological processes and drugs could become significantly more sophisticated.

By leveraging the diversity of available molecular and clinical data, predictive modeling could

help identify new potential-candidate molecules with a high probability of being successfully

developed into drugs that act on biological targets safely and effectively.

Financial Services

In most financial institutions the current use of Big Data is to contain fraud and comply

with rules on money laundering. Predictive credit risk models that tap into large amounts of

payment data are being adopted in consumer and commercial collections practices to help

prioritize collections activities.

Financial services companies are looking to leverage large amounts of consumer data across

multiple service delivery channels to uncover consumer behavior patterns and increase

conversion rates, according to The Deciding Factor: Big Data & Decision Making, a report

written by the Economist Intelligence Unit for consulting firm Capgemini.

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ChallengesThe biggest current challenge is being able to articulate a compelling business case for using

Big Data. Most companies are unsure of the value of a lot of data they already have collected.

Advanced analytic capabilities are necessary to make the data actionable.

Other challenges include organizations silos that interfere with decision-making. If departments

don’t share data, the customer profile is incomplete. Another problem is a shortage of qualified

Big Data scientists. A lack of qualified people is impeding Big Data implementations and a

talent shortage is driving up costs.

Recommendations• Start small using existing data with which you are already comfortable. Keep things

simple and then move on to more complex uses.

• Focus on functions that you believe can drive the most improvement. This will help

build a business case for the usefulness of Big Data.

• Commit initial efforts to customer-centric outcomes.

Remember — while you may be holding back on Big Data implementation, your competitors

are using it to gain an advantage over you.

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