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November 27, 2012

More Road Paved for Big Data in the Cloud

By Marie Lingblom

We all know data is growing at astronomical rates, but there’s still confusion over Big Data. When it comes to Big Data in the cloud, there’s even less road traveled. This is changing.

Big Data first. Forrester defines Big Data as “techniques and technologies that make capturing value from data at an extreme scale economical.” In a recent Forbes article, Holger Kisker, a principal analyst with Forrester Research, emphasizes this definition helps clarify misconceptions.

Big Data, he says, is about a set of different technologies. It doesn’t equal Hadoop or in-memory computing as some have suggested, but rather may include them depending on the use. And that, says Kisker, is one of Big Data’s key challenges: What works well for risk management, for instance, may not work well for asset performance.

IBM and the City of Lyon, France, are piloting big data analytics technology aimed at providing city transportation engineers with real-time decision support to proactively reduce traffic congestion. The pilot includes IBM software, historical and real-time traffic data, analytics and algorithms to model conditions across the city’s network of roads, buses and trams.

The idea is to help city officials predict outcomes and analyze scenarios to resolve problems. Over time, the system “learns” from successful outcomes to fine-tune future recommendations for everything from a major accident or large sport event or concert.

Forrester says enterprises are reporting plans to adopt public and private cloud solutions for business intelligence. The maturity of cloud technologies and market understanding of its range of benefits, says Kisker, is driving it — and Big Data is poised to significantly accelerate that growth.

For an enterprise, most of the Big Data information comes from outside of the company — social media, Web, events, demographic data, etc. An organization may recognize the growing importance of social media, for instance, but face challenges when it comes to unlocking its potential.

Kisker singles out three good reasons why (depending on the use) Big Data can make a lot of sense in the cloud:

1. It requires a spectrum of advanced technologies, skills and investments. Do you really need/want this all in-house?

2. It includes huge amounts of external data. Does it make sense to move and manage all this data behind your firewall?

3. It needs a lot of data services. Focus on the value of your differentiated data analysis instead of Big Data management.

There are a number of Big Data solutions for the cloud in the market. Kisker points to some that address the technology with Big Data platforms including management and analytics technologies. Others focus on the Big Data service side, including data preparation, storage and a merging of different data sources.

A good place to start is figuring out what you want to do with the data. Many organizations enlist the expertise of data scientists, who can help uncover patterns in unstructured data and trends that reveal business opportunities. They can also support technologists by spotting business risks such as potential security threats.

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