Big data applications: Real-world strategies for managing big data
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BOSTON -- Companies that fail to take advantage of the opportunities presented by "big data" management and analytics...
technologies can expect to fall behind the competition and possibly go out of business altogether, according to one IT industry analyst who spoke Tuesday at the Talend Connect conference.
The world is just getting started with big data technologies like Hadoop and MapReduce, and several obstacles – such as a dearth of skills and old-fashioned thinking about data -- continue to stand in the way of their adoption, said Jeff Kelly, a principal research contributor at Wikibon.org and a contributing editor at SiliconANGLE.
Big data is about looking forward, making predictions and taking action.
Jeff Kelly, principal research contributor, Wikibon.org
But, the analyst continued, companies that embrace the concept now are the ones who will lead the way in the not-too-distant future when entry barriers are not so high. Companies that exploit big data will gain the ability to make more informed decisions about the future and will ultimately bring in more money than those that do not.
"We have been doing research around big data for a couple of years [and] have come to the conclusion that big data is now the definitive source of competitive advantage across all industries," said Kelly, who is also a former SearchDataManagement.com reporter. "While I realize that is a pretty sweeping statement, there really aren't any industries that we can think of that aren't going to be impacted by big data."
The phrase "big data" is most often used to refer to the massive amounts of both structured and unstructured information being generated by machines, social media sites and mobile devices today. The phrase is also used to refer to the storage, management and analytical technologies used to draw valuable business insights from such information. Some of the more well-known big data management technologies include the Apache Hadoop Distributed File System, MapReduce, Hive, Pig and Mahout.
The benefits of becoming data-driven
Kelly was just one of several speakers at the Talend Connect conference who spoke about what it will take for businesses to become more data-driven in their decision making processes. Another was Tony Fraser, a partner and technology partner with Neo@Ogilvy, the performance marketing arm of Ogilvy & Mather, a large public relations and advertising firm.
"We basically do digital advertising, across everything that you can possibly imagine," Fraser said.
Speaking to the crowd, Fraser offered examples of how data-driven decision making has helped his company achieve success. In one example, the Paris Hotel and Casino in Las Vegas hired Neo@Ogilvy to help it attract more customers. Fraser's company accomplished this by first looking at all the positive things that customers had to say about Paris on social media and travel sites.
It turned out that many of the positive experiences of customers had everything to do with their view of the fountain at the Bellagio Hotel and Casino located across the street. Armed with that information, the NEO@Ogilvy team issued a new digital ad for Paris that featured a view of the fountain.
"As soon as that ad went out people started making more reservations," Fraser said.
Impediments to big data adoption
There is certainly no shortage of hype around big data management technologies, but actual adoption levels remain low for two main reasons, according to Kelly.
First, Hadoop and other big data technologies are extremely difficult to use and the right skill sets are in short order. Today, organizations often hire PhDs to handle the analytics side of the big data equation, and those well-educated individuals demand high salaries.
"The skills used to manage, deploy and monitor Hadoop are not necessarily the same skills that an Oracle DBA might have," Kelly said. "For instance, if you want to be a data scientist on the analytics side, you need to know how to write MapReduce jobs, which is not the same as writing SQL queries by any means."
The second major obstacle standing in the way of increased adoption centers on the notion that most companies currently lack the mindset required to get the most out of big data.
Most large companies today are accustomed to gaining business insights through a combination of data warehousing and business intelligence (BI) reporting technologies. But, according to Kelly, the BI/data warehousing model is about using data to examine the past, whereas big data technologies are about using data to predict the future.
"To take advantage of big data requires a shift, a very basic shift in some organizations, to actually trusting data and actually going where the data leads you," the analyst said. "Big data is about looking forward, making predictions and taking action."
Big data management for the masses?
As with all emerging technologies, big data management and analytics will eventually become more accessible to the masses -- or democratized -- over time. But some important things need to happen first, according to Kelly.
For starters, new tools and technologies will be needed to reduce the complexity associated with working with big data technologies. Several companies -- like Talend, Hortonworks and Cloudera -- are working to reduce big data difficulties right now. But, Kelly said, more innovation is needed to make it easier for users to deploy, administer and secure Hadoop clusters and create integrations between processes and data sources.
"Right now you need some pretty sophisticated skills around MapReduce and other languages, or SAS and others to be a top line data scientist," Kelly said. "We need tools that can abstract away some of that expertise so that you don't need to have a PhD to really explore big data."
The task of democratizing big data will also require a great deal of user training and education on topics like big data infrastructure, deploying and managing Hadoop, integration and scheduling MapReduce jobs, Kelly added.
"We really need to tackle the problem from both ends. One is to make the tools and technologies easier to use," he said. "But we also have to invest in training and education resources to help DBAs and business analysts up their game [and] operate in the big data world."