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8/3/2012
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How To Find Strategic Advantage From Big Data

Few top-level executives understand the changes necessary in data-gathering and decision-making processes well enough to make big-data migrations a real priority.

12 Top Big Data Analytics Players
12 Top Big Data Analytics Players
(click image for larger view and for slideshow)
Being able to collect more data on customers and shovel it into an analytic engine is only half the capability most companies need to develop, however, according to Larry Kanter, a forensic accountant and president of Kanter Financial Forensics in Dallas.

Good analysis and decision-making depend on the quality of data, not quantity, Kanter said.

Without tools or expertise to eliminate minutiae or select only relevant classes of information, the only alternative most companies would have is to hire forensic accountants or other data experts to spend hours or days filtering big data by hand--a process far more expensive, imprecise, and un-repeatable than using software designed to do the same thing in a fraction of the time, without the risk that perceptual biases will warp the analysis.

Fortunately, according to venture capitalist, author, and tech-entrepreneur Dave Feinleib, there is a rash of big data apps on the way to do just that. Some are designed to gather and analyze data generated by machines; others are designed to collect and contextualize data from Facebook and other social networks.

The fastest-growing categories, however, are those designed for specific vertical markets such as healthcare and finance, and those aimed at collecting and analyzing data for specific departments within the organization, such as marketing, IT, and customer service.

Though many are traditional on-site apps, most are appearing as online apps built on cloud platforms with the computing power to analyze huge data sets as well as the algorithms to find useful knowledge within masses of data, according to Raj De Datta, CEO of BloomReach.

"BDAs [big-data apps] don't just repackage your data in a cool interface or offer productivity improvements in data scalability, they harness the world's data to deliver you a better outcome--like more revenue," according to De Datta.

BDAs, publicly available at subscription prices rather than the full cost of licensing and implementing sophisticated analytics on-premise, could make big-data analysis available to vast numbers of mid-sized companies that lack the will or expertise to build their own tools.

In business it's almost always better to make informed decisions using hard data than to use experience and intuition, but most companies have only begun to shift toward data-driven decision-making, according to a study released last month by Capgemini.

Both enterprises and mid-sized companies have difficulty making that move, Capgemini found.

Of 607 business managers, 90% said they would have made at least one major decision differently during the past three years if they'd had access to better data.

Nevertheless, more than half of respondents said big data is not considered strategic at the highest levels of their companies, despite the quality of decisions that can be made based on it.

That doesn't mean top executives aren't looking for better data with which to make better decisions, according to Capgemini's South East Asia CEO Deepak Nangia.

It only means that the organization's data-handling practices are not prepped to give decision makers the data they need when they need it to make better decisions, which would lead them to see decisions based on big data--and therefore big data itself--as a strategic advantage worth pursuing, Nangia said.

The challenge for big-data advocates--as it is with many promising but early-stage technologies--is to demonstrate quickly the benefit of building complex data sets and analytics to help their companies make better decisions, and get top managers to agree to make the investment even without the heavily analyzed data that might help push them in the right direction, Castro said.

"There's still a lot of convincing to be done," he said.

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HM
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HM,
User Rank: Strategist
8/9/2012 | 2:05:53 PM
re: How To Find Strategic Advantage From Big Data
Kevin, good article. An enterprise-ready solution worth looking at is HPCC Systems which provides a single platform that is easy to install, manage and code. Their built-in analytics libraries for Machine Learning and integration with Pentaho for great BI capabilities make it easy for companies to understand the meanings and patterns hidden in complex information and extract the value for taking action on real-time insights. For more info visit: hpccsystems.com
jjoseph021
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jjoseph021,
User Rank: Apprentice
8/8/2012 | 8:14:29 PM
re: How To Find Strategic Advantage From Big Data
Thanks for the interesting article. Over the past year "big data" has turned from an interesting term to a catch-all phrase that seems to be used for everything including the kitchen sink. some of the people in the article seem to be saying that they are considering whether big data is important to them or not, but I think it's helpful to make a distinction between big data as a type of data and a specific big data project. The bottom line is that big data isn't a choice. I think every company should view big data as a short term critical business element. If you look at big data as data that goes beyond the limits of traditional database and BI technology in terms of how large the set can be or how diverse the data can be or how frequently that data can change, then there is no question that big data is strategic and important. Of course, not ALL big data is important and organizations are going to have to evaluate projects based on their business value. It's clear that thereG«÷s still a lot of headway to be made when it comes to tapping into big data. Telecom is a good example of how big data is important and adds value - some companies that we work with in telecom are bringing data together from 60 or more data sources and doing it in a fashion that they can make real-time decisions on potential fraud threats and ad hoc analysis of revenue data to improve their operational processes. They have been doing that for years and getting significant value (hundreds of millions of dollars worth) from those projects, but now we look at it and say it's a "big data" project. But the fundamental problem being solved there is using "data discovery" capability to solve a problem G«Ű allowing users to bring together a wider array of stakeholders to explore diverse data types much more quickly, and at a much lower cost, than traditional means. In doing this, the people who need information to make strategic decisions can get much closer to relevant data. It lets people explore every possibility without going in to the analytics process with preconceived ideas about likely outcomes that can sometimes mask alternate, valuable truths hidden in the data. I talk about this a bit more, here: http://bit.ly/MOtDkr. - John Joseph, Lavastorm Analytics
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