So many companies of all shapes and sizes across every industry you can think of have big data on their minds. Business owners are scouring the Web, looking for social media experts to help them out, and SEO companies that can get them the visibility they want online. But all of that falls short of meeting any company’s bottom line if they either aren’t using data to make crucial decisions, or even worse, if they have invested in big data gathering and storage without also investing in data scientists.

Now you’re thinking, “Seriously, scientist?” We’re not talking about lab coats and graduated cylinders. But it’s important that you take this title seriously, otherwise you might wind up with the wrong contractors or employees on hand. There is a difference between someone who can gather your data and someone who can fully analyze it and make sense of it so you can use the information gathered to make more money, spend less to get there, and ultimately get a better return on investments you make everywhere from purchasing, distribution, marketing, and more.

But today, with the world of big data whirring and whizzing by almost faster than we can make use of it, plenty of under-qualified people have added the ‘scientist’ moniker to their business cards. So how do you tell the difference between a real data scientist and a poser? Because there is no “data scientist” degree (yet) other variables will help you tell who’s legit from who’s vying for the job without the proper skill set. For example, one of the immediate giveaways is that a data scientist who knows his or her stuff will demonstrate a solid understanding of strategy from the ground up. They will offer up better ways to gather data and give you an analysis of where you currently stand. If you haven’t started to use big data at all yet, a true data scientist can walk you all the way through the process, and then stay on board to help you make sense of every byte and bit that comes through. A true data scientist will be able to answer questions you have quickly and with ease.

You’ll want to ask them for specifics and ask for goals to be met on a set schedule. For example, every first Monday of the month all the raw data about sales converted into an easily understandable analysis that can be presented to board members, or a biweekly report about where things stand with loss prevention. Any scientist that can’t provide this is either not equal to the task or… you’re not providing them with the data they need to get the job done. If you feel the latter might be the case, do everything you can to get them what they need. If it’s a matter of budget, sit with your scientists to discuss your finances and how you can get them what they need to do their job on your budget.

The good news is that a lot of buzzwords in the big data world represent unnecessary (at least for now) tools of data management that likely won’t concern your business endeavor, at least not until you have a little more big data gathered, and a little more experience dealing with your data scientists. Let them advise you as to what you need in terms of big data housing and management, but don’t let them be the decision makers. Ultimately, the decision has to be in your hands, but to make the right choices you need to understand what your data analysis team needs to do their job right. Manage them well and respect their level of expertise. Don’t let them trample you, and trust your instinct when it comes to hiring the right people. At the end of the day in a world of big data you don’t entirely understand, there will have to be at least a modicum of mutual trust. Try to build that by understanding big data yourself and by asking questions. The only dumb questions are the ones that aren’t asked, right? In the world of big data, this adage is truer than ever.

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