During the last year, NetPop interviewed hundreds of business professionals who utilize large, complex datasets as a significant function of their job. We chose our “analytic insiders” (respondents) from a broad range of functional roles and industry roots across the corporate landscape.
The goal? To help expose the ever-expanding elephant in the boardroom: Big Data. With this post (the first in a short series), we hope to share useful insights around current Big Data successes and challenges, giving readers a better grasp on how to take advantage of a much-discussed and potentially vital resource.
As you probably know, the “promise” of Big Data lies in its size and exponential growth. Almost 90% of the current data available digitally worldwide has been produced in just the last two years[i], and output is only expected to climb[ii]. The foothills of these data Alps – where the corporate world is gamely trying to plant its flag – has been growing as well. In that same two-year span, our insiders estimate the data they are handling regularly has increased by an average of close to 40%.
Yet, interestingly, we found that the collection and storage of data (without breaking the bank, of course) are no longer primary issues. The main challenges have now become ensuring data accuracy, integration, and effective (and timely) generation of insights.
This is an important lesson to keep in mind when confronted with the potential of Big Data. There are far more logistical considerations involved in the successful analysis and bottom-line application of data than many realize. That big informational mountain requires training to scale across the organization. And even when you reach the summit, your view of the future may still be cloudy. On the other hand, you’ll likely be better able to spot the next storm rolling-in, and that alone is certainly worth an investment.
Our recommendation: Take a pinch of salt with your helping of Big Data-hype and call us in the morning. You should also consider how much pointed primary research can complement Big Data findings, putting them in the proper context and illuminating underlying motivations. Understanding the why behind the data can make it infinitely more actionable.