When MHI released its inaugural Annual Industry Report in 2014, more than 450 survey respondents said Big Data analytics was their top strategic supply chain priority. The anticipated outcome of more deeply investigating both internal and external information collected by their systems was that it would would boost efficiencies via predictive analysis. Just five years later, the 2018 MHI Annual Industry Report found that just 19% of the 1,100 surveyed industry leaders have predictive analysis in place, but 82% will adopt it in the next five years.
Bolstered by the competitive advantages already realized from expanding data analysis initiatives, companies are now looking deeper into so called “dark data.” Defined by Gartner as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing),” dark data could include customer information, e-mails, log files, financial statements, image files, previous versions of documents. The opportunities are enormous. Indeed, IBM says dark data comprises 80% of Big Data.
What does this mean for your company?
In a recent article in MHI Solutions Q4 2018 issue, “Shedding Light on Dark Data,” several experts weigh in on the risks and opportunities, as well as why companies should care.
Having data in the dark gets in the way of businesses functioning properly, says Brian Vecci, technical evangelist at Varonis. “Any CEO will tell you the more you know about how your business runs, the better off your business is going to be. That’s particularly true for companies interconnected within a supply chain. The reverse is also true—when much of your business is running completely in the dark, that’s a huge risk.”
Further, both security and competitive advantage are threatened. As Joe Vernon, senior manager in the Supply Chain Technology practice at Capgemini, explains: “Since dark data touches so many aspects of supply chain—from procurement to operations to transportation—if a competitor hacks your data and discerns a hidden pattern that you weren’t aware of, they might use it against you.”
The experts also discuss how to figure out which types of dark data will be useful and which can be deleted, as well as how machine learning (ML) and artificial intelligence (AI) tools can be leveraged to glean useful information that leads to timelier and less risky decision making. ML utilizes algorithms to analyze and discover patterns that humans are incapable of seeing within information such as dark data. Knowledge gleaned from the patterns can then be used by AI systems to make decisions.
Applying algorithms via ML that fuels AI will dramatically speed up the process of determining whether or not relevancy exists within a specific subset of dark data, says Carlton Sapp, research director focused on data and analytics for technical professionals at Gartner.
“Using a data-driven approach like ML and AI allows a quick analysis without actually having to consume much of that dark data,” he explains. “If those algorithms are applied early in the dark data life cycle—as it emerges from a sensor for example—that enables a faster determination of whether or not the information needs to be collected or stored.”
To read the entire article and learn more about the potential benefits of dark data to your supply chain operations, click here.