The challenge with Big Data analytics lies in its name — the data is so big that the truly valuable nuggets of information are often buried too deep. The data is too thick, and the ability to analyze it is too thin.
To make the most out of Big Data, manufacturing organizations need to pick the right metrics to track and measure. In that sense, you’re getting a thinner slice of data, but thicker insight because you’re analyzing the most valuable metrics.
When you focus on the most important data, Big Data has the potential to become what the Enterprise Irregulars post calls “tiny insights,” or “tangible bites of intelligence that help [you] make better decisions and improve outcomes. … Tiny insights inform massive decisions for business or important decisions for individuals.”
With Big Data analytics, it’s often best to start with a wide view before taking a narrower, more focused approach. For example, if you’re looking at a group of equipment, you might notice that specific bearings are starting to wear out. With that information, you’re able to focus on replacing those bearings before there’s a breakdown that leads to productivity losses.
Or, from a product innovation perspective, you could focus your efforts on understanding what’s resonating with customers. For instance, perhaps you built a front-end loader designed for heavy-duty use, but discover that 80 percent of your customers are using the equipment for light-duty work.
Big Data analytics holds great potential for manufacturers, especially when tied into end-use, environmental and location-based information. But it’s easy to become so overwhelmed by the volume of data available that you lose sight of what’s most important to measure.
As you work with Big Data, keep in mind that it’s not the amount of data that truly matters. What’s most important is what you do with the data.