If the usual curve holds, big data is about to lose a lot of its luster, but Kieran Taylor explains that its advocates need not worry.
It's been more than a year since the Obama administration launched the Big Data Research and Development Initiative. At the time, the excitement around big data was palpable. Six federal departments and agencies announced more than $200 million in new commitments to greatly improve the tools and techniques needed to access, organize and glean discoveries from huge volumes of digital data. Big data, it seemed, was destined to help solve some of the nation's most pressing problems in science, engineering, health and national security.
Fast forward a year, and the excitement around big data is still here -- but that might not last much longer. According to industry analyst firm Gartner Inc., big data is entering the "trough of disillusionment" phase of the Gartner "Hype Cycle." And therein may lie big opportunities.
The Hype Cycle comprises five key phases of a technology's life cycle:
- Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.
- Peak of Inflated Expectations: Early publicity produces a number of success stories, often accompanied by scores of failures. Some companies take action; many do not.
- Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver.
- Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.
- Plateau of Productivity: Mainstream adoption starts to take off, and the technology's broad market applicability and relevance are clearly paying off.
One factor that may be driving big data into the trough is the simple realization that managing big data environments is hard. Never before have organizations had to deal with so much data, generated from so many different sources, at such a dizzying pace. The velocity of data, or the rate at which information is "gulped" in big data systems, is mind-boggling. No entity produces, gathers and stores more data than the American government. This makes both the opportunities and the challenges of big data especially great for government agencies.
If we are indeed about to take the plunge into widespread disappointment and discontent, as Gartner says, does this signify that big data is dead? Absolutely not! It's just a predictable phase, and one through which other emerging technologies such as cloud computing and tablets have passed. Over the years, research has proven that entering the trough of disillusionment is actually a positive sign, signaling that a given technology is reaching a point of maturation.
On the other side of the trough of disillusionment in Gartner's Hype Cycle is the "Slope of Enlightenment," where some businesses continue to experiment and understand the benefits of a technology, even if it has become unfashionable for a bit. And finally, there's that "Plateau of Productivity," when the benefits become widely demonstrated and accepted. So, in short, there's likely to be a positive conclusion for big data down the road.
That said, an impending trough means that government agencies, particularly smaller agencies that lack the resources of larger ones, must be prepared to face a period of increased cynicism. Senior-level managers may be more doubtful and skeptical of investments. The question becomes: how can you best survive the trough of disillusionment and minimize the fall? After all, you don't want to get sidetracked and lose support for the huge contributions that big data can make to your IT.
Survivors of this stage will be those that successfully tackle head-on the real challenges to big data environments. These include determining what data should be kept and discarded; attracting big data expertise; and perhaps most importantly, maximizing the efficiency and speed of your existing resources. To this last point, big data applications and environments suffer from many of the performance challenges and bottlenecks that plague current distributed applications, putting ROI from big data projects at risk. In many cases, it's not a lack of resources making a job slow. It's an organization's inability to quickly and easily identify and fix performance bottlenecks. Here, application performance management (APM) approaches can help, since adding more servers may not necessarily solve the problem.
In summary, the looming backlash only shows that big data is here to stay. As the TechAmerica Foundation's report put it, "the impact of big data has the potential to be as profound as the development of the Internet itself."
Now is the time to position yourself to ride out the trough of disillusionment, by demonstrating to agency leaders the ongoing relevance of big data and the fact that it does more than just eat away at IT departments and budgets. There is real opportunity on the other side.
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