From the Blogosphere
Don’t Call #BigData Dead | @CloudExpo #IoT #AI #ML #MachineLearning
Is Big Data falling into Gartner’s dreaded trough of disillusionment?
By: Jeffrey Abbott
Feb. 1, 2017 05:00 PM
You can call me biased, or out of touch, but over the past two years, I’ve been reading articles and blogs about how Big Data is going away, dying, or already dead. So what changed?
Is Big Data falling into Gartner’s dreaded trough of disillusionment? Did someone discover that predictive analytics could have a butterfly effect and change the course of history and hence, we should abandon these voodoo analytics practices? Did we figure out that we actually don’t have enough data to call it “Big” data? Did we already finish analyzing all the data and we’re all done? Or… are the people calling it dead really just a bunch of marketers who are looking move on to the next new thought leadership topic, so they write one final blog to make one final impression just before they abandon the topic? Bingo. I’m looking at you… industry pundits, thought leaders, and analysts.
These people are making misleading and even damaging claims that could influence organizations to make bad decisions. Big Data is not dead. Big Data is not going away. It’s getting bigger, more diverse, and more critical for organizations that want a competitive advantage. Okay, there, I said it.
So why all the talk about doom and gloom?
Let’s back up to… circa 1997. The World Wide Web is taking off. The “net” is a battle ground for thought leadership. People quickly discover that sticking the letter “E” in front of a name makes it sell like hotcakes. Email and e-commerce become prevalent. Internet tradeshows and conferences pop up everywhere. Homer Simpson starts his own ISP called “Compu-Global-Hyper-Mega-net” after discovering that they now have the Internet for computers!
So why are there no “Internet” tradeshows now? Why does nobody’s business card today say “Internet Specialist”? Did the Internet die? Of course not. It simply became so pervasive that it needed to be broken down to the next levels of granularity and specialization.
Flash forward 10 years to 2008. We’re still recovering from the dot-com crash, and getting ready for the next economic collapse. We’ve now realized that there needs to be a sound business model behind an online business and people are rethinking how the Internet can do more than host online brochures and basic e-commerce. And so this word “cloud” starts popping up everywhere. Nobody knows what it means. Every wannabe thought leader, marketer, and analyst is fighting to prove that they know what is it and why they uniquely have the skills, processes, and technologies to harness its power. Cloud becomes the new buzzword battleground. NIST even finds it necessary to develop and standardize a complex definition of cloud, and then 15 versions and several years later, it’s done, and… nobody cares. In the end, cloud was a bad name for a concept that people already understood. We could have just said… “it means it’s like Hotmail so nothing is stored on your computer” – good enough. Done. But cloud has had impressive stickiness as a term. We now have public, private, and hybrid cloud – which are all valid things. From a terminology and thought leadership perspective, we’re still coming up with new ways to confuse our customers into thinking that they are missing out.
So… Big Data. Another horrible name for something people already understood. After years of confusion, now the commoner can even explain it – analyzing lots of data to help make better decisions – got it thanks bye. To get here, we went through years of senselessly complex definitions, such as the 3 Vs definition (Volume, Variety, and Velocity) coined by Doug Laney of Gartner. Then of course, other analysts felt the need to call that definition inadequate by adding more Vs (Validity, Veracity, Value, Visibility, etc.), which was extremely helpful… said no one ever. And some vendors are also trying to persuade us that they do more than the now standard “descriptive, predictive, and prescriptive” analytics, by adding new terms such as “explorative, diagnostic, and cognitive” analytics. In our consulting organization, we take a different approach – we talk about the 4-Ms, because it’s actionable and much easier to remember (Make Me More Money).
Today, our tech analysts, fellow marketers, and thought leaders are hungry for the next revolution and eager to call Big Data dead, so they can say “I said it first!” Some are saying that Big Data, as a topic, is being replaced by “machine learning”, and that is the new shiny penny. So all you need to do is a simple Find/Replace on all your Big Data messaging. Seriously? Come on.
Machine learning, although cool, new and full of potential, is not a replacement for data analytics – it’s a use case, or an instantiation of it. Others are saying that Big Data is dead and we’re moving to IoT (Internet of Things – which btw, from a terminology perspective, is already showing signs of cracks, with people saying “no it’s now about the Internet of Everything …yawn). IoT can be considered a use case for Big Data. Or, Big Data is the cause of the IoT phenomenon. Or, IoT is what fuels Big Data. Take your pick. We get so wrapped up in the terminology, that we’re missing the more important point that Big Data, as field, is actually expanding, with new specialty areas that touch a wider range of industries and use cases. When things become this pervasive, it’s easy to consider them old, or even irrelevant. Snap out of it!!!
Unfortunately, these terms, and their current popularity as thought leadership topics, wind up influencing major strategic business decisions when irresponsible people start calling Big Data dead. The misinformation floating around the interwebs is unfortunately influencing major businesses to invest, or not, in Big Data analytics, for all the wrong reasons. Analytics is not going away – it is just getting started. Data is not going away – it is growing exponentially. The business opportunities from data and analytics are not going away – we’re just skimming surface.
Go ahead and talk all you want about your new terms and buzzwords to sound fresh and differentiated. You can call me out of touch, but I’ll be busy helping customers use data to make better decisions, create smarter products, and deliver more value. Last I checked – that was still in style.
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