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?

Necessary? Unnecessary? Just dumb?

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.”

Before you dive into six, 12, even 18 months of preparation, be sure you’re asking the right questions of the data — not what’s easy, but what’s going to be actionable — and be sure to challenge your data team to break down the traditional data organization. Let the data tell the story, and don’t hide the problem.

According to John Nesi, vice president of market development at Rockwell Automation, the top 300 or so manufacturing companies are all trying to “get a handle on supply chain management with data,” while those in the heavy industries are just transporting data and monitoring it remotely.

“When I look at Caterpillar or (John) Deere and what they do with real-time monitoring of their equipment — satellite-driven tractors, remote mining — that data is fed into their data centers, and they’re trying to get a handle on it,” Nesi said. “I’m seeing that action occur, but it’s all hand-built and standards don’t really apply across the industry.

“When we look at data, we look at data in real time. We look at what happens in the control layer, we look at the analytic performance that can be accomplished at that layer. Where a lot of manufacturers want to look at data, it’s usually with respect to manufacturing performance systems.”

Who will actually make the big data decisions?

The structure of big data solutions is important. So is the structure of big data teams. The convergence of information technology and operational technology could open new proverbial doors on your floor, but it could just as quickly slam them shut.

Operational leadership can request that the company do more with its data, Sobel said, then wait patiently while IT joins the fray — perhaps building the solution itself, perhaps over several years — and trust dissolves. “It’s hard for the IT team to admit that it doesn’t know as much about what’s going on on the floor as it says it does.”

Implementing big data technology for the sake of something new is another avenue toward failure, especially when the effect on the factory is brushed aside or forgotten. “Everybody in the factory just feels like they’re lab rats for something of no use.”

Instead, consider a cross-cultural combination, perhaps with an operational lead like a manufacturing vice president of engineering, that receives approval from the IT department. “There has to be line-level leadership from both functions,” Sobel said. “Plant management has to believe this is useful. Otherwise, you get corporate mandates that are passively resisted.”