Another interesting but horribly designed "presentation" from IT Business Edge about Big Data (original is here: http://www.itbusinessedge.com/slideshows/seven-ways-to-make-big-data-an-actionable-opportunity.html)

1. Click through for seven ways to make Big Data an actionable opportunity for business,
as identified by PROS.
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2. Big Data is about connected data, piecing together multiple and separate data points
to identify patterns that help predict outcomes and prescribe actions – automatically.
When considering a Big Data software suite, make sure it has an analytical
component to it, so you are getting “smart” data.
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3. Companies that embrace and invest in Big Data will outperform their competitors in every
available financial metric. For example, in 2009, drilling activities plummeted across the
board. One global oilfield services company was facing challenges similar to its competitors,
including insurmountable profitability losses due to the instability of the oilfield business. In
addition, fluctuations in market demand were causing inconsistent data sources resulting in
imprecise pricing. After implementing segmentation and using Big Data science to forecast
demand, the company generated $200 million in incremental sales over a two-year period,
enabling them to outperform the market.
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4. Big Data science must include an assessment of data viability to provide more
accurate and reliable insights than generic analytics or business intelligence. With so
many varieties of data and variables to consider in building an effective predictive
model, companies should quickly and cost-effectively test and confirm a particular
variable’s relevance before investing in the creation of a fully featured model.
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5. Big Data only matters if it results in better outcomes, not just better insights. For
example, in the midst of health care reform, medical device manufacturers have
experienced excruciating pressures from hospital purchasing organizations looking to
reduce their costs. Using its Big Data, one company’s sales team was able to show the
purchasing organization its calculations on how to evaluate pricing. The result: a 6
percent increase in average selling price in four quarters, and the highest margin in
three years.
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6. The most valuable and measurable outcomes are those associated with sales growth,
profitability and competitive positioning. For example, one large chemical manufacturer
began experiencing declining revenue for one of its burgeoning businesses and recognized a
series of unaligned business processes. Working with its planning department, it used Big
Data to shape a new demand curve for where products should be shipped to satisfy customer
requirements. It was also able to use data science to identify prescriptive pricing guidance for
the sales team. Revenue increased by $200 million in the first year alone.
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7. Insights come from Big Data science; outcomes come from Big Data applications infused with data
science. This is Big Data nirvana. For example, one international storage and information management
company saw its incremental revenue level off. It faced issues including stick rates and
miscommunication between the pricing department and the sales teams. As a result, it was having
trouble meeting specific targets. Thanks to Big Data science and prescriptive pricing guidance, it was
able to identify specific attributes imperative in determining customers’ willingness to pay. Thanks to
these insights, the company realized that a sizeable number of contracts were priced far below market
price. Not only has it seen some really impressive data, it's also seen a reduction in customer
terminations and improved statistics.
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8. Big Data applications are the future of Big Data because they provide the execution
arm of technology, not just insights. One example of this type of application is price
guidance, which is a technique that delivers a set of pricing recommendations to the
sales person, including details on deals most likely to close, products that are most
likely to sell, and prices that are most likely to win. It hides most of the back-end
complexity associated with Big Data and price optimization.
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