Four Steps to a Big Data Strategy
Someone once told me she didn’t believe in Gartner’s hype cycle. I found this really strange, because in my many years of covering technology, you could set your watch by the inevitability of its curve.
To me, the hype cycle seems pretty straightforward. Something new is created. The success stories roll out. Suddenly it’s super hot and everybody’s buying it. A year later, everybody freaks out about investing in the technology because they can’t make it work or they fear it was just a fad or both. Finally, IT figures out how to put this new or new-ish thing to work in a realistic way and it becomes status quo, often regardless of how well it is or isn’t implemented.
What’s fun and also annoying to watch is how this plays out in the tech press and blogs. It is often almost farcical in how it unreels, particularly as the hype cycle takes a dive.
This is why it’s annoying: I’ve noticed the article headline blurbs always seem way more cynical than the article actually is. Just like the earlier, over-hyped articles, these pieces come on strong, but generally, they don’t actually attack the technology itself, so much as the marketing and misapplication of the technology.
In fact, they’ll often outright acknowledge that the technology is a good and useful thing, but you’ll almost always be told not to “buy technology for technology’s sake.” Then you’ll be reminded that you can probably just use a more established tool—and, oh by the way, would you like to talk to their sales representative?
And that’s where we are with Big Data right now: It’s about to peak and slip into the trough of disillusionment. And that means the Big Data skeptics are coming out of the Interwebs to dis Big Data.
Lachlan James, communication manager at BI vendor Yellowfin, was beating the “Big Data is blasé” drum recently via TDWI’s LinkedIn page, complete with a link to a full blog post titled, “Not convinced about this whole Big Data thing? You’re not alone.”
To be fair, though, James and Yellowfin began to question Big Data last year, before the hype cycle took its dive. And last month, the company issued a press release touting CEO Glen Rabie’s contention that Big Data is actually hurting BI and analytics.
During a briefing with Claudia Imhoff, CEO Glen Rabie explained his position:
’What I was really aiming at is that I think it’s a step backwards in terms of the engagement model of the BI industry with its customers. And what I mean by that is it’s become a very technically complex conversation. So rather than being about the business, and what businesses do with their data, and how we’ll help them to make decisions, all we’re doing now is selling the virtues of technology. We’re not being very precise about when to use what.’
In short, he goes on to say, don’t invest in technology for technology’s sake. And stop confusing people.
The press release offers as further evidence Gartner’s recent statement that the confusion lies between the terms Big Data, BI and analytics, and this confusion is “blunting BI spend.”
Still, BI investments grew a nice 7 percent in 2012, although that is a slow-down from 2011’s 17 percent growth rate.
Well, okay. I can see Rabie’s point.
And, it’s true: The exaggerated hype claims about Big Data are probably just that—exaggerated claims. In “Big Data, Big Ruse,” Stephen Few argues against viewing Big Data as the salvation of the world.
“…Data wont’ save us; not even Big Data,” he writes. “We’re the protagonists in this story; information is only a resource.”
That’s true enough, although I’m not sure anyone ever meant that Big Data would literally cure cancer or save the world.
So what are we to make of the Big Data bashing going on? I’ve gathered a few worthwhile takeaways:
- It’s true: You may not need Big Data. Instead, maybe what you need is a way to clean up the data you have or better tools for analyzing it.
- Make sure you have a business use for Big Data. I’m sure you can find a way to put it to use, but the real question is whether you have a pressing problem that can only be solved with Big Data.
- If you’re failing at BI and data warehousing, now isn’t the time to move on Big Data. “We live in an age when 80% of data warehouse projects fail,” Ian Nicholson of BIReady states in the LinkedIn conversation. “So what makes Big Data different? All I see is bigger, costlier failures! We haven’t learned to walk and yet, now we want to run?”
That’s a fair point. If you don’t have a data quality and governance strategy, adding more data is probably a bad plan. Lay a firm data foundation before you bring in massive amounts of unstructured data.
