Big Data Isn’t So Big: Dispelling the Biggest Myths About Big Data in the QSR

3 Big Data Myths to Put to Rest for QSRs

There’s no question that big data holds big potential for today’s businesses. For several years, large enterprises in early adopting sectors have been unlocking the transformative power large data sets provide. But what about the small businesses, who make up some 99.7% of U.S. employer firms1? What about QSR franchisees, who also stand to reap big benefits from employing big data in the right way?

Let’s start at the beginning. What is big data? For many, it feels like a large, nebulous black hole of information, where numbers flow in, but nothing seems to come out. A dictionary definition2 seems to corroborate this impression: big data is “data sets, typically consisting of billions or trillions of records, that are so vast and complex that they require new and powerful computational resources to process.”

Sure, there’s no question that big data is big. But does it have to be?

At first, the big data conversation focused exclusively on industry giants with the deep pockets and expansive teams necessary to handle mass quantities of information. QSRs, often operated by franchisees with the same size and budgetary constraints as other small business owners, were largely overlooked when it came to discussing big data’s role.

This is no longer the case. Today, big data has made its way from Wall Street to main street, delivering big business benefits to the small business community. QSR franchisees are no exception. Small business owners that have embraced big data have overwhelmingly reported that adopting the new technology was not a heavy lift, with 80% stating3 that technological deployment was easier than they anticipated.

In the quick service restaurant, data abounds, and today’s franchisees are in an optimal position to harness all of this information to build new engagement, efficiency, and effectiveness.

Big data is closer, easier, and more affordable for your QSRs than you might think. In the journey to embrace big data, let’s first separate myth from fact and put some common big data myths to rest.

Myth #1: Big data is too expensive for me.

Since its onset, big data has been portrayed as too massive to successfully implement without the most advanced solution on the market—which, not coincidentally, usually ended up being the most expensive. But with the proliferation of cloud-based QSR technologies, sophisticated doesn’t inherently equate to expensive. In fact, big data capabilities are now being baked into many of the solutions you’ve come to know and love.

Take the point of sale. The point of sale rings up hundreds of transactions a day, making it a trove of data insights that, if unleashed, have the power to transform QSR performance.

The right point of sale will act as a strategic centerpiece of your QSR by collecting transactional data, normalizing this data at scale, and making it available and actionable to key stakeholders and complementary technology solutions. All of this is built into the system from the onset, so QSRs that strategically select their point of sale provider can lay the groundwork for data-driven excellence without incurring additional costs.

Myth #2: Big data doesn’t apply to me.

With big data, it’s easy to overthink it, or get bogged down in the weeds trying to identify the most useful pieces of information. But, in the QSR environment, the applications of big data abound, and are right at your fingertips.

Consider, for a moment, your kitchen’s prep routine. When managers are tasked with determining prep and thaw numbers based on their best estimate for traffic for the day, preventable food waste, which can rack up to 19 cents per meal4, often occurs.

But with a kitchen management system that harnesses historical point of sale data, predictive analytics can be leveraged so prep numbers are data-driven and as accurate as possible. This allows QSRs to adjust based on seasonality, day part, and day of week—ultimately mitigating waste and cutting down on pricey food excesses. Apply this data-driven edge across labor management, inventory management and digital signage—now that’s big.

Myth #3: Big data is too complex for me to manage.

Just because big data is big, doesn’t mean it is complex. Solutions that act like sieves instead of funnels, sorting information in a fashion that is purposeful and actionable, have the ability to radically simplify big data—no matter the complexity of the original data source. In fact, the data-driven restaurant has the ability to simplify a myriad of operational tasks that, were it not for big data, would be time consuming and complex.

Consider labor management, for example. Anyone who has attempted to schedule restaurant shifts manually knows the complexities of creating a schedule that takes into account all of the variables—from demand patterns, to employee preferences, regional and national labor laws, and overtime. Yet, a labor management solution that harnesses big data can generate an optimized schedule with the push of a button, leaving no variable unconsidered. Not only does a data-driven solution have the ability to dramatically cut under- or over-utilization of staff, it also frees up valuable manager cycles for other high-value tasks.

SICOM: Making Your Big Data Dreams a Reality

From understanding your customers better, to measuring that new LTO, to stopping food waste in its tracks, big data has the power to capitalize on some of your QSR’s biggest opportunities and overcome some of its most significant challenges. The truth is that big data is the way of the future of QSRs, and your QSR can begin embracing its benefits today.

SICOM’s integrated solutions are leveraging data collected from across all areas of your restaurant to power better customer experiences with greater efficiency than ever before. Contact SICOM to learn how you can bring the benefits of big data to your QSR.

Your QSR Technology Partner 

Big data is here to stay. Learn how you can put it to work. 

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Joel Hoffman
March 29, 2018

With extensive experience in harnessing technology to improve a broad range of restaurant KPIs and enhance franchise profitability, Joel specializes in the delivery of data-driven solutions for the back of house.

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