Fast Walking Wins the Big Data Race

We all know the saying: “learn to walk before you run,” as it’s applicable to so many things in everyday life. This sage advice is equally relevant in the business world. Over the years, I’ve been in countless meetings where the project lead advises this approach when rolling out new hardware or software platforms.

Theoretically, this sounds like the right approach. Learn the basics before moving to more complex and complicated capabilities. But these rules no longer apply in the world of Big Data. Someone could pass by you pretty quickly at a crawl, and a slow stroll won’t propel your business forward if you can’t keep pace with other facets of the organization.

Slow strollers are easy to spot as they’re the ones on the sidelines waiting for everyone else to go first. They typically have the mindset that they’ll learn from someone else’s mistakes by following their steps closely. They’ll also carefully examine every data nook and cranny before taking action just to ensure they clearly understand the business problem at hand.

Data scientists often embody the characteristics of a slow stroller as they are driven by the need to have a complete data set before jumping in. Of course, that never occurs since data changes constantly. Meanwhile, early adopters run to the finish line armed with all sorts of advanced analytics and reporting tools to produce rapid data insights. Unfortunately, they don’t always know how to use these tools properly and can become quickly overwhelmed.

Bottom line: one group doesn’t move fast enough to keep up while the other expends too much energy running around. So, how do you get both sides in sync? A fast walk is the best speed at which to enlist the broadest support to tackle Big Data problems. I see it time and time again—the broadest adoption comes from an extended group of stakeholders that each ponies up one or two things they’d like to know about the business as part of an umbrella data management strategy.

Broader alignment around common goals and objectives is the key to getting everyone moving at the same pace. Additionally, the success of all projects around data management and analytics is gauged by how many people use it because it lowers TCO while increasing ROI.

That’s why taking a cross-functional approach to Big Data analytics is the best bet for extending your criteria for success.

Not only does everyone have skin in the game, you’ve opened a dialogue between different groups even before the project got off the ground. Most important, when different groups come together all at once to tackle a data project, you start to see the beginnings of a cultural change. And, you end up having the most impact and the greatest benefits with the least friction throughout the organization. All it takes is a seed of an idea and a commitment to fast walking your way to a solution.

One of my favorite fast-walking examples involves a multi-national pharmaceutical company that wanted to use predictive analytics to rapidly detect batch-processing issues during drug manufacturing. The company hoped to better understand the movement of materials and batches throughout the production process so they could predict batch failure much earlier in the process.

The initial goal, led by a group of “runners,” was to perform predictive analytics against drug batches so they could pull them faster if problems were identified. To accomplish this, however, they needed to shorten reporting cycles as reducing them by even a day would make a major difference in being able to pull a potentially bad batch faster.

The early adopters next enlisted a cross-functional team of slow strollers struggling with enterprise reporting. Together, they fast-walked to a solution that integrated up to 20 different data sources while shrinking the reporting window from six days to four hours. By embracing modern data management technologies, they took advantage of advanced quality-control analytics, root-cause analysis and validated reports to mitigate risk and save millions. In fact, every time this team predicts a batch problem a day early, the organization saves half a million dollars in lost overhead and operational costs.

Along the way, they have led a major cultural change characterized by a stronger focus on quality improvement and product safety. For that reason and so many others, I’m a big fan of fast walking as the most effective way to align organizational stakeholders in the never-ending quest to win the Big Data race.

What’s your big data speed? Connect with me on Twitter at @joschloss to share your thoughts.

About the Author: Joanna Schloss