Gartner reported in September 2014 that 73% of respondents in a third quarter 2014 survey had already invested or planned to invest in big data in the next 24 months. This was an increase from 64% in 2013.
The big data surge has fueled the adoption of Hadoop and other big data batch processing engines, but it is also moving beyond batch and into a real-time big data analytics approach.
Organizations
want real-time big data and analytics capability because of an emerging
need for big data that can be immediately actionable in business
decisions. An example is the use of big data in online advertising,
which immediately personalizes ads for viewers when they visit websites
based on their customer profiles that big data analytics have captured.
"Customers now expect personalization when they visit websites," said Jeff Kelley, a big data analytics analyst from Wikibon,
a big data research and analytics company. "There are also other
real-time big data needs in specific industry verticals that want
real-time analytics capabilities."
The financial services industry is a prime example. "Financial institutions want to cut down on fraud,
and they also want to provide excellent service to their customers,"
said Kelley. "Several years ago, if a customer tried to use his debit
card in another country, he was often denied because of fears of fraud
in the system processing the transaction. Now these systems better
understand each customer's habits and the places that he is likely to
travel to, so they do a better job at preventing fraud,
but also at enabling customers to use their debit cards without these
cards being locked down for use when they travel abroad."
Kelly believes that in the longer term this ability to
apply real-time analytics to business problems will grow as the Internet
of Things (IoT) becomes a bigger factor in daily life.
"The
Internet of Things will enable sensor tacking of consumer type products
in businesses and homes," he said. "You will be collect and analyze data
from various pieces of equipment and appliances and optimize
performance."
The process of harnessing IoT data is highly complex, and companies like GE are now investigating the possibilities.
If this IoT data can be captured in real time and acted upon,
preventive maintenance analytics can be developed to preempt performance
problems on equipment and appliances, and it might also be possible for
companies to deliver more rigorous sets of service level agreements
(SLAs) to their customers.
Kelly is excited at the prospects, but he also
cautions that companies have to change the way they view themselves and
their data to get the most out of IoT advancement.
"There is a
fundamental change of mindset," he explained, "and it will require
different ways of approaching application development and how you look
at the business. For example, a company might have to redefine itself
from thinking that it only makes 'makes trains,' to a company that also
'services trains with data.'"
The service element, warranties,
service contracts, how you interact with the customer, and what you
learn from these customer interactions that could be forwarded into
predictive selling are all areas that companies might need to rethink
and realign in their business as more IoT analytics come online. The end
result could be a reformation of customer relationship management (CRM)
to a strictly customer-centric model that takes into account every
aspect of the customer's "life cycle" with the company -- from initial
product purchases, to servicing, to end of product life considerations
and a new beginning of the sales cycle.
Credit: Techrepublic
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