There’s good news and bad tied to the fact that information technology gets more affordable as it matures. The good news is that more companies can access more data. The bad news is that there are more companies that don’t know what to do with all that data.
A new study documents that concern. “Industrial Internet Insights for 2015,” just released by GE and Accenture, surveyed 250 executives across eight industrial sectors. It found that fewer than a third (29 percent) of them are using big data analytics across their company for predictive purposes or to optimize their business. That must be driving the other two-thirds nuts because many of them believe they could lose their market position in the next one to three years if they don’t adopt big data capabilities. The vast majority of them (93 percent) are already seeing new competitors using big data to differentiate themselves. This makes big data analytics a top priority for them.
Sure, many companies have invested in automated material handling and logistics systems and are enjoying the benefits of better information tied to equipment maintenance and fewer breakdowns, but mastery of big data means getting the big picture, and that’s a benefit that many have yet to enjoy. Along with the delivery of their new systems came data delivery by firehose. They can’t keep up with all the data being delivered and don’t even know what to do with it. That was one of the most surprising findings of this study for C.V. Ramachandran, managing director of strategy for Accenture.
“One of our experts in data analytics said that today we use data analytics to generate hypothesis vs. proving hypothesis. It used to be that analytics was all about trying to prove what we thought we knew."
—C.V. Ramachandran, managing director of strategy for Accenture
“I thought people would be getting stronger on the predictive side of things,” he told me. “The fact they were still in the analysis phase in most companies leads me to believe there’s more insight needed at these companies. One of our experts in data analytics said that today we use data analytics to generate hypothesis vs. proving hypothesis. It used to be that analytics was all about trying to prove what we thought we knew.”
Today, apparently, we don’t know what we know.
Thirty-five percent of the companies in the Accenture study that have big data capabilities use them for analysis, vs. only 13 percent that do any kind of predictive analytics. The rest of them either are just connecting to their systems or monitoring them. That results in a wealth of data, but a poverty of information.
“We need people who are smart about their industries and businesses to work with the data analysts to ask the right questions,” Ramachandran concluded. “The prize at the end of the rainbow is that we finally know what we thought we knew. That will drive efficiency and revenue generation.”
A couple weeks ago I met an executive who echoed the collective sentiments expressed by colleagues who participated in the Accenture study. Jeff Gallinat, senior vice president of supply chain operations for Cisco—a PROVIDER of technology that generates big data, mind you—told attendees at The Material Handling Industry’s (MHI’s) annual meeting in San Diego about how his company consolidated its warehousing into multi node distribution centers but now needs a closer connection to its operations and partners.
“We have nodes in our network where 40-60% of the variable cost is energy. We’re wasting it every day because we simply haven’t done the basics to connect things in different ways. Data is still easier to handle but it requires more expertise in fields that are just being developed."
—Jeff Gallinat, senior vice president of supply chain operations for Cisco
“Of all the things we do from a customer experience and quality point of view as a hardware manufacturer, the number one opportunity we have to substantially change the perception of our company is around the delivery [of our products],” he said. “There’s an increasing level of commoditization of those services, and some companies may say that’s good, but we want more agility and interconnection with our [logistics] partners and the processes going on between us and them. We want nearly real time information, more machine-to-machine conversations so we can be differentiated. Many of these things have already been developed between us and our customers but we see huge opportunities for RFID or some kind of tracking technology to better manage [contingencies]. We’re still reactive. There’s also a huge opportunity to save operating cost. We have nodes in our network where 40-60% of the variable cost is energy. We’re wasting it every day because we simply haven’t done the basics to connect things in different ways. Data is still easier to handle but it requires more expertise in fields that are just being developed.”
The best career advice young parents can offer their kids is “become a data scientist.” Come to think of it, that’s pretty good advice for innovative logistics professionals too. But if you’re one of those and stuck in a company suffering from analysis paralysis, you’re probably feeling that condition’s major side effect: innovation frustration. Neal Thornberry, Ph.D., developed a 7-step treatment for that condition. We just posted a gallery to help guide you and your executive team through it. Take a walk through it then let me know if you think it will help. Start HERE.