Who’s afraid of Big Data?

Who’s afraid of Big Data?

March 8

The emergence of Big Data as a tech issue follows a familiar pattern, one reminiscent of the Y2K “crisis” and its ensuing mayhem. The IT sector once again gins up another buzzword that goes mainstream, sending the marketplace into a lather. Major corporate CIOs and free-range pundits alike say whether you ignore or use it, proceed at your own peril. They’re right about putting said data to work—but not necessarily about who’s at risk or who will benefit.

Sweeping changes are afoot in the data landscape, leaving small and medium business (SMB) owners and managers to wonder if they’ll still be standing in the aftermath. Don’t panic. You may actually have the upper hand—if you understand the difference between data and information. Data isn’t actionable; information is. In a marketplace evolving faster than anyone can keep up with, smaller businesses are far more likely to have the agility to respond to new opportunities in a timely manner.

They just can’t be afraid of Big Data.

Though “bad” may be implied in the name, Big Data is most likely to turn bad only if you ignore it. Still, approach with due caution.

For starters, you’d think an industry sector built on precision, e.g., IT, would have a precise definition of Big Data. And you would be wrong. At least, there isn’t a definition that can’t be shaped or shaded to fit whatever agenda the speaker or writer is pushing (full disclosure: mea culpa). Instead of trying to quantify accumulated data, the best way to understand what’s actually big here is by taking a functional view, examining the systems and processes involved to see if and where size matters.

The nub of the matter is the difference between the constantly growing amount of data collected and amount of data actually used to conduct business. Though collected, data outside a department’s or individual’s immediate use is ignored. Pencil-pushers need dollar figures, tool-pushers need dimensions. Odds are excellent you have untapped resources lurking in some archived database, document warehouse, or other data storage resource linking those two activities together in ways you never considered.

What really makes data capital-B big is the unfiltered, unstructured nature of that data. As an article in the March 2013 Harvard Business Review says, “[T]here is a difference between numbers and numbers that matter. This is what separates data from metrics…A successful organization can only measure so many things well and what it measures ties to its definition of success.”

Creating that distinction—data from metrics—takes some serious computation and is not for the faint of CPU. It really should be called Big Data Processing. It’s about finding and creating order from chaos. Seeing patterns, processes, and relationships otherwise obscured by the fog of business. Once you’ve recognized those dynamics, you can use measurements—metrics—as the basis for making reality-reinforced informed decisions. To sum up, once armed with metrics, you’re ready to turn your data into actionable information.

Before you get too comfortable, though, be aware getting the metrics is just the first act in a three-part play. Enter analytics. If you don’t do the analysis, why bother sorting data in the first place?

Analytics, yet another anxiety-inducing, catch-all buzzword, has its roots in statistics. Be glad there are people perhaps better than you in this arcane mathematical field because good statistical analysis will show you your business’s signal-to-noise ratio (hint: if it doesn’t drive your business forward, it’s noise) with a degree of precision not otherwise available using other techniques.

For SMBs, there’s never been a better time to get those metrics, run the analytics, and move to the head of the pack. Panicky owners and managers might point to the layers and varieties of expertise needed for analytics, not to mention acquisition costs. They can relax because Big Data truly is a job for somebody else: outsourcing finally makes sense. It’s time to find a service provider with the computing horsepower, connectivity, and industry familiarity to learn how to extract even more juice from your business. Outsourcing does it with minimal impact on your daily workflow and often at a price that will give you a refreshing pause.

A good analytics service will also help you do more with the data you already have. They can probably append your data with public and proprietary metrics for better granularity and applicability. Consider predictive modeling (uh-oh, another buzzword), which is now gaining visibility, if not considerable traction. Doctors and public health officials have been using it for years under the heading “epidemiology.” It can involve pathogens and their genomes, weather patterns, and demographic shifts. When epidemiological predictive modeling works as it should, your flu immunization turns out to have been well worth it. For Mr. and Ms. SMB, analytics can provide a strong depiction of likely future market conditions, the ostensible final act in our Big Data drama.

Our protagonist SMB genuinely shines in Act 3 where Big Data transforms into Big Decisions. Large corporations may have wallets and market influence to match, but their stability-seeking nature can prevent them from engaging in disruptive business practices—unless you consider barriers to change disruptive.

You get to write the rest of the story from this point on.

Yogi Berra once famously said, “When you see a fork in the road, take it.” You can do that—if you’re not afraid of Big Data. You can even see the fork before you get there.

Photo Credit: Jimee, Jackie, Tom & Asha via Compfight cc