In 2012, Gartner updated its big data definition to include high volume, high velocity and high variety information assets — the three Vs — that are so large, so unwieldy that “they require new forms of processing to enable enhanced decision making.” Ever since, big data has been called revolutionary, transformational, necessary, unnecessary, a pain and, for lack of a more blunt term, just dumb.
“Over the last few years, we’ve worked with a number of progressive plant managers and manufacturing engineers who are interested in working with data more strategically,” said Jon Sobel, co-founder and CEO of Sight Machine, which focuses on solving manufacturers’ critical problems by bringing big data to the factory floor. “They would often go upstairs and hit a wall, with questions coming back at them like, Do we need this? Don’t we have this already? Why don’t we take a look at everything before we do anything?”
No, in 2016, big data is still very much tucked in the manufacturer’s toolbox, and it should be.
“In the last six months, there has been a real urgency and noticeable leaning forward by a meaningful number of companies,” Sobel said. “Some Tier One auto companies that are working on incubators in Silicon Valley spent a few weeks looking at solutions and are moving quickly. … So many companies have a mandate: find me digital startups and let’s get going.” But as much as many manufacturing executives want to just get started, projects that analyze — rather than just collect — data still take time to plan and roll out.
Can your factory floor benefit from efficient big data? Start your analysis with these five questions.
How do you define big data?
This is the first question and, at least during the initial stretch of planning and implementation, the most important for almost every company, regardless of industry.
“I think there is a challenge with people’s perception of what big data is,” said Lisa Disselkamp, a director in the HR transformation practice of Deloitte Consulting who has implemented big data in her solutions and her books. “Is it just tons of data? Complex data? External data? Macro data? What we see with data is the bigger it is, the more it actually hides information.