One of the advantages of having been around technology for a while is that you gain a perspective on trends. What younger people are seeing as new, looming directions for technology tends to be the latest wave of a past phenomenon, but if you haven’t seen it before, it looks unprecedented.

Big Data is just a trend. It’s nice to see the proliferation of data called out and identified as a potential problem and a distinct opportunity, but before anyone gets too wigged out about the sheer volumes and the potential challenges, you should consider the past.

There's Always Been Big Data

Back in the mid-1990s, I wrote for a magazine called Telephony. That venerable publication covered the telecom business and I was the technology editor, which meant I was saddled with some nitty-gritty issues that the senior writers didn’t want to deal with. One of them was the issue of data; telcos generated massive volumes of data about their customers. It was generally understood that analyzing that data could reveal information to reduce churn, increase loyalty, improve customer service response and, in general, make a big difference in how telcos related top their customers.

The problem? There was so much data and few companies knew how to attack it. Every day, more piled up; every day, more went unexamined. Ultimately, the telcos became focused on customer acquisition, not retention, and an invaluable resource went to waste.

A later stop in my journalism career at VARBusiness again had me pegged as the senior editor of technology and again, I covered areas that had other staffers flummoxed, found bored or otherwise were disinterested in. One I took with great glee was the storage beat. This was the early 2000s, when the price of memory chips had begun to plummet, allowing businesses of all types to collect and store ever-increasing amounts of data. Data mining became an interesting subset of my coverage, but I saw many businesses become mentally swamped by the sheer volume of data, especially customer data. They’d become victims of paralysis by analysis, almost literally; not knowing where to start caused executives to hem and haw and finally back-burner analysis in favor of other, more familiar activities.

So, here we are again. Once more, we’re talking about the vast amount of data we have to work with and again, many speak of it as a gigantic, seemingly sinister and ultimately unapproachable challenge. It’s viewed as a threat, not as the opportunity it really is. This time, we’re calling it something: Big Data. Sort of like “Bigfoot,” or “Big Brother,” or the “Big Sleep,” it does not carry a welcoming connotation, does it?

Here’s the thing: It’s not really about the data today. For as long as we’ve been collecting and tabulating data through electronic means, we’ve always thought we had a lot of data. Data has always been big. What’s really big today, and what should be foremost in your mind, is analysis.

There Hasn't Always Been Smart Analysis

We’re really in the age of Smart Analysis. The challenge of looking at the issue this way is that it requires you not to focus on the amount of data you have to work with, but on the really important things, like what you want to learn from the data.

Analysis requires tools and technology, but it also requires some very non-technological things, like a focus on your customers, in the case of CRM, and an understanding of what you’re hoping to discover. When you start to do that, you can start reducing the size of the data you’re working with by tossing out irrelevant data.

For example, at a recent Gartner event, Big Data was placed on a list of trends next to “the Internet of Things,” a term given to the increasing ability of devices to deliver data automatically to manufacturers, vendors or users. Let’s say your refrigerator went on the fritz and sent a message to the vendor that you might be a candidate for a new refrigerator. That’s a direct action that would not require much analytical power. However, if you’re the vendor you might want to compile similar data and correlate it against customer purchasing patterns to discover loyalty patterns.

In these cases, you have an established idea of what you’re trying to discover, and thus your analysis isn’t of an impossibly large amount of data, it’s of a discrete set or sets of data. If you can identify those sets, your analytical task is reasonably simple.

While there’s always a possibility of a totally unexpected bit of information emerging from a random analysis of a vast quantity of data and the emergence of new analytical technology will make it easier to digest data in enormous quantities. It’s unlikely you’ll be doing random analyses with no objectives in mind. That makes no business sense.

So, stop thinking about Big Data and start thinking about Smart Analysis. Think about what you want to learn, combined with the right technology and a lack of fear of the data itself. It has always been the recipe for capitalizing on the data you capture. The businesses who are able to do that today will succeed in the era of Big Data because they realize this is not an exercise in data collection, but in analysis and understanding of what has been collected. 

Editor's Note: You might be interested in other articles by Chris Bucholtz, including
Exploding 3 Social CRM Myths that Paralyze Business Leaders.