Be right, not fast. Think business, not IT. Don't worry about dirty data. A big data guru shares contrarian advice about the worst whoppers.
Forget about the cautions and clichés, the easy generalizations and the dark warnings. The big data domain is rife with persistent myths that block progress -- or worse, get people headed in the wrong direction.
Taking on some of the big data realm's worst whoppers, Arnab Gupta, CEO of analytics platform provider Opera Solutions, insists that successful big data projects aren't boil-the-ocean, years-long IT infrastructure projects in the mold of data warehouse deployments. Rather, they must be focused business initiatives. Here's his take on five leading myths.
Myth 1: Big data is the next paradigm, and if you don't make a change right away you'll get left behind. Not so fast, says Gupta. It's this kind of thinking that gets people deploying Hadoop clusters and stockpiling data before they have any idea what they want to do with the information.
"The problem with first vs. last thinking is that you assume that if you're first, you're going to get a competitive advantage, but that won't be the case if you don't focus business results that will give you a business advantage," Gupta explains.
Many big data initiatives seem to be experiments because people just aren't used to working big volumes and varieties of data. By starting with a specific known problem, you'll reduce the scope of change management and of pioneering required to get to a big data breakthrough.
Arnab Gupta, CEO, Opera Solutions
Myth 2: Big data is an IT problem. Closely related to Myth 1, this kind of thinking can get you in trouble. The danger in starting with IT experimentation is ending up with boil-the-ocean IT infrastructure projects. Avoid the trap of "build it and they will come" thinking.
"Most of the investments in big data projects have gone into information management infrastructure. If you start with the business use case, you may still be investing in infrastructure, but it will be for precisely the tools you need to solve a specific business need."
Myth 3: Our data is so messed up we can't possibly master big data. There's no doubt that enterprise data is often flawed, but data quality, master data management, and data governance tools have made it easier to clean up the mess. "The huge investments companies have made in data management are now paying massive dividends."
Where companies used to have to invent tools and come up with data management, data analysis, and data visualization systems on their own, they can now turn to packaged applications on all fronts. These tools have made it far easier to capture, clean, manage, and analyze information. So don't let fear of bad data become a mental stumbling block.
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 won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.