The Evolution of Big Data
March 22, 2013
Dave Spenhoff, VP of Marketing, Infochimps
Over the past several years, Big Data has gone from being a somewhat obscure concept to a genuine business buzzword. As is often the case with buzzwords, when you dig a little deeper you find that many people have substantial misconceptions about what Big Data is, where it came from and where it is going.
Here are a few things that might surprise you about the evolution of Big Data:
- There are more “failures” out there than you’d think. We’re bombarded with the hype, but the reality is that this is still an early technology. As people are unfamiliar with the tech components of Big Data, they’re often prone to thinking that they can jump in and do everything themselves. However, the task of streaming and analyzing batch, near-real-time and real-time data in a comprehensible form is beyond the capabilities of most in-house IT departments, and will require outside expertise.
- It is an evolution, not a revolution. The topic of Big Data has exploded so quickly onto the media landscape that it’s easy to get the impression that it appeared from nowhere and is transforming business in a whirlwind. While the ultimate impact Big Data will have on business cannot be underestimated, its ascension has been much more incremental than media coverage might lead you to believe. Its earliest stages began more than a decade ago with the increasing focus on unstructured data, and since then companies have been steadily experimenting with and building capabilities and best practices. It’s important to make the distinction between evolution and revolution because viewing Big Data as revolutionary may lead to the temptation to dive in headlong without a real plan. The smart course of action involves identifying a very specific business challenge that you’d like to address with Big Data, and then expanding and iterating your program step-by-step.
- Big Business doesn’t yet ‘get’ Big Data. You’d think big enterprises would have captured a 360-degree view of their customers by now. But they haven’t, and evidence of this abounds in the sloppy marketing outreach efforts that everyone experiences on a daily basis. Two essential changes need to happen in order for enterprises to truly get a handle on Big Data: 1) corporations need to break down departmental silos and freely share customer insights organization-wide; and 2) they must start bringing the apps to the data, rather than bringing their data to the apps. Companies have been reluctant to embrace the cloud for sensitive, proprietary data due to security and other reasons. However, we now have the ability to build apps in virtual private clouds that reside in tier-4 data centers, eliminating the need for the expensive, risk-laden migrations that have stood in the way of enterprises’ ability to adopt effective Big Data strategies.
- Housing your own data is too cost-prohibitive. The old ways of doing things simply won’t work for Big Data — it’s just too big. While 10TB of legacy infrastructure costs in excess of $1M to store, the data warehouse for any significant company is going to be way past 20 TB. The math isn’t difficult — housing your own data is super expensive. There’s no way that companies like Facebook and LinkedIn, for whom customer data is lifeblood, could have done it without leveraging the cloud. More and more, enterprises are discovering that they can achieve analytic insights from Big Data by deploying in the cloud.
- Hadoop alone won’t do it. Although Hadoop gets 80% of the Big Data attention, it’s only 20% of the solution. Predicting customer behavior is kind of like shooting a bullet with another bullet, and is going to require much more than a historical data perspective. Sure, Hadoop gets most of the press these days, but in order for enterprises to gain a truly customer-centric view they’ll need to tie together historical, real-time and near real-time data through a single, user-friendly interface that enables them to analyze and make decisions in-flight.
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