Don't dive in head first, says Fed-focused data analytics and cloud provider.
Big Data Analytics Masters Degrees: 20 Top Programs
(click image for larger view and for slideshow)
So your organization is ready to take the big data plunge -- perhaps invest in an on-premise Hadoop platform, or use a cloud service to mine social media streams? Whatever your goals, it's important to ease into big data and avoid being overly ambitious at the start.
That's according to Mare Lucas, chief marketing officer and big data evangelist for GCE, a cloud services provider with a large footprint in the in the government realm. Its GCE Big Data and Analytics Cloud, a scalable platform for storing and managing data, provides tools for consumer-friendly data search and analysis. GCE's data/cloud platform is also the framework for U.S. General Services Administration's (GSA) USASpending.gov, a government site with nitty-gritty details, including contracts, grants, direct loans and payments, on how the Feds spend the taxpayers' money.
"We're trying to get these massive troves of information that everybody talks about to a place where people can do something with it," Lucas told InformationWeek in a phone interview. Planning a big data platform can be stressful for enterprises, said Lucas, who recommended these essential actions to help ease the anxiety.
Organizations that have traditionally used relational databases may be reluctant to try new big data platforms such as Hadoop. But inertia isn't the best approach. "You've got to take a first step and get started," Lucas advised. "Start with a chunk of data and see what you can do with it." For instance, a retail business might take three months of sales data, put it in one of these tools, and start seeing what you can learn.
2. Avoid fear.
Change is scary -- particularly the type of change that's may be expensive to implement and a potential career-killer. Lucas said, "There's that natural 'oh my gosh, what is it? Do I have to change everything to get to it?'"
Not necessarily. A big data platform can coexist peacefully beside a traditional relational database. "For the time being, these are complimentary things," Lucas pointed out. "You're going to have your transactional systems that run your business, but now you have this other way of getting to [new] data."
3. Find balance.
"Most people have found a way to have a cloud model coexist with their legacy systems. They strike a healthy balance," said Lucas.
This model can work for big data as well. "People don't know what to do. They get worried that one [system] would replace the other," said Lucas. "There's more of an 'I-should-do-it-too' factor with big data than there was with the cloud."
The appeal of big data differs somewhat from that of cloud computing, however. The latter's appeal revolves mostly around cost savings. The former, however, is attractive for a wider variety or reasons, including marketing and sales improvements, as well as the ability to find value in information that's often too unstructured for traditional data management platforms to process effectively.
"Enterprises often have value-rich data that has been locked up in business intelligence tools and transactional databases for a very long time," Lucas said.
This year may bring a greater "consumerization" of big data as well, but that doesn't mean the end of skilled data scientists who are trained to slice and dice information. "I'm sure there'll still be people around who are data scientists -- those who can do deep analysis," said Lucas. "But lots of times, people don't need that deep analysis. Sometimes they have straightforward questions they want to ask of their data."
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business wonít wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.