With any new hot trend comes a truckload of missteps, bad ideas and outright failures. I should probably create a template for this sort of article, one in which I could pull out a term like “cloud” or “BYOD” and simply plug in “social media” or “Big Data.”
When the trend in question either falls by the wayside or passes into the mainstream, it seems like we all forget the lessons faster than PR firms create new buzzwords.
Of course, vendors within trendy news spaces also tend to think they’re in uncharted waters. But in fact there’s actually plenty of history available to learn from. Cloud concepts have been around at least since the 1960s (check out Douglas Parkhill’s 1966 book, The Challenge of the Computer Utility, if you don’t believe me), but plenty of cloud startups ignored history in favor of buzz.
And it’s not like gaining insights from piles of data is some new thing that was previously as rare as detecting neutrinos from deep space.
Here are five history lessons we should have already learned, but seem to be doomed to keep repeating:
It wasn’t that long ago that every time a cloud project or company failed, some tech prognosticator would sift through the tea leaves and claim that the cloud concept itself was dead.
The same thing is happening with Big Data. According to a recent survey, 55 percent of Big Data projects are never even completed. It’s hard to achieve success if you don’t even finish what you started, yet many mistakenly believe that this means Big Data is bunk.
Not true. Plenty of companies are reaping the rewards of Big Data, analyzing piles of data to improve everything from marketing and sales to fraud detection.
“It reminds me of the Moneyball craze during the early 2000’s, when Major League Baseball teams started to figure out that statistics could be used to build a winning ball club, rather than relying on a scout’s stopwatch and gut,” noted Matt Fates, a partner with Ascent Venture Partners. “There was initial backlash against the ‘stat geeks,’ but today every team has an advanced statistics department that helps general managers make better decisions. This was bringing data, and insights, to bear on decisions in a way that turned conventional wisdom on its head. It was not ‘big data’, but it led to big changes. It never would have started had one GM not been open-minded about statistics. His success forced others to follow.”
Of course, some of the confusion stems from how indiscriminately the term Big Data is thrown around, since most of us don’t need Big Data per se, but rather just data analytics, which leads us to the second history lesson everyone is failing to recall:
People mean many different things when they use terms such as “cloud” and “Big Data.” Are you talking about virtualized infrastructures when you say cloud? Private clouds? AWS? Similarly, Big Data can refer to existing pools of data, data analytics, machine learning, and on and on.
The Big Mistake with the term Big Data is that many use the term to mask vague objectives, fuzzy strategies and ill-defined goals.
Often when people use these terms loosely it’s because they not only don’t really know what the heck the terms mean in general, but they also don’t know what they mean to their particular business problems. As a result, vendors are asked for proposals that are a poor fit for an organization’s cloud or Big Data challenges.
If your CEO or CIO orders you to start investigating Big Data, your first question needs to be the most basic one: Why, specifically?
If you can’t answer that question concisely, you’re in trouble.
If you’re the person tasked with building out a Big Data architecture, then it’s fine to focus on details that won’t matter to anyone who isn’t a data scientist.
If you’re a business user or non-data scientist, it’s best to just ignore all this noise. It’ll sort itself out soon enough. I’ve seen this phenomena repeat with everything from CDNs to storage to cloud computing and now Big Data. Engineers and product developers often fall prey to “if we build it, they will come” syndrome, ignoring the real-world pain points of potential customers in favor of hyping their technical chops.
When they fail to find real-world customers for the resulting products, they then set their sights on technical minutiae, since it couldn’t possibly be a flawed go-to-market strategy that was the problem in the first place.
Take the recent news that Facebook is making its query analysis software, Presto, open source. Is this a win for Hadoop or for SQL? Does it mark the end of Hive?
Okay, if you’re reading this, you’re probably an early adopter or you’ve already placed some Big Data bets, so it matters to you. But for the rest of the world, it’s not even on their radar – nor should it be.