Outside Insights | February 2014 | By Guest Author

Avoiding Big Data’s Big Headaches

How to find and leverage the data needed to succeed in the quick-service restaurant industry.

Quick service restaurants can use big data to plan their expansion efforts.
thinkstock
Bookmark/Share this post with:
Email this story Email this story
Printer-friendly versionPrinter-friendly version

Read More About

Within the quick-service restaurant industry, every executive has an interest in growth, innovation, and sustained success. This statement, while hardly novel, is more complex than many imagine or some care to recognize. Deciphering, dissecting, and analyzing the most relevant components—the information responsible for streamlining operations, enhancing productivity, improving morale, refining menu items, and customizing marketing initiatives—is a substantial undertaking, fraught with risks (of wasted money) and worsened by frustration (from individual managers). 

Finding that information, thanks to cloud computing and all manner of global resources, is one thing. But finding the talent to uncover that material—assembling specific teams for specific projects based on specific skills—is something else entirely. Versatility and flexibility are the traits a team must possess, the traits multiple teams (plural) must have to adapt to a rapidly changing workforce where data is essential.

The problems associated with the current system are exasperating precisely because companies know they need this talent, but they also understand that conventional tools do not reveal the insights necessary to prosper. Welcome to the paradox of an environment filled with riches—a wealth of data—and inadequate resources to mine this material. 

Ask this straightforward question: Does my company need this “solution,” and only this particular solution, or is there a better way to leverage the information at my disposal?

There is a superior way to resolve this matter (more about that below), but let me first reiterate a key point about data. Rather, allow me to explain why this sense of frustration is so intense and disruptive, since it speaks to what many professionals suspect and what additional information (from within a company) will confirm: that there are alwaysmore lessons an executive can learn by examining data in granular detail, revealing, say, patterns of behavior among specific groups of consumers in particular regions of the country, which will impact corporate messaging, promotions, pricing, and seasonal variation of dining options. In not so many words, executives know they—and we—can do better.

Again, the issue is a matter of having access to the right talent, to automate known information, so the same team (or a separate group of experts) can analyze the patterns and concealed data within unknown information. For example: That intelligence may explain a drop in sales at the end of a previous month because of a corresponding decline in discretionary funds among consumers. Or that data may reveal when and why promotional campaigns should start at the beginning of the month, resulting in greater profitability.

I write about this topic from my own personal experience as the founder and president of Practical Data Analytics, which helps companies access and apply business intelligence. And, for purposes of clarity, I choose to limit the principal words of this discussion to “data” and “information” for the sake of simplicity and, well, clarity, because I understand the verbal onslaught executives face and vendors present.

There is so much jargon, so many opaque terms and confusing ideas, that the proverbial landscape looks more like a junkyard than amber waves of grain; software firms, consulting agencies, dashboards, and key performance indicators (KPIs) pile upon each other, each with their own proposed solution, grafted (haphazardly) onto a company’s existing infrastructure, resulting in a cumbersome, pitiful machine—picture some metaphorical mechanical beast, reddened by malfunction and releasing clouds of smoke and steam—destined to implode in so many scattered pieces.

Issue a stop for the good of simplicity

Amidst this chaos, my advice to quick-service or casual dining businesses is simple: Stop. Before succumbing to the false urgency of sales pitches about data or information—before spending hundreds of thousands of dollars on new software and a complete overhaul of an existing IT framework—ask this straightforward question: Does my company need this “solution,” and only this particular solution, or is there a better way to leverage the information at my disposal? The respective answers to that question are “no” and “yes,” as in, there is a superior—and less expensive—approach to analyzing data.

Unless a piece of software or hardware is the culmination of years of research and development, and unless that same product or service has an established brand identity (for which consumers will pay a premium, and for which the cost of advertising and marketing that item factors into the overall price), technology should be cheaper, in dollars, than ever before. Indeed, it is more affordable today than at any other moment in history.

Get the right answers by asking the right questions

Throughout this process, as described above, there are two variables every executive should remember: Known information, such as revenues, transaction counts, same-store sales, average check totals, and menu mixes, which a customized application can automate, resulting in substantial savings in time and money; and unknown information, whose existence is a fact but whose precise definition (or practicality) is a bit of a mystery.

In that instance, there are indicators of activity or signals—things to spot—to decode a message. Translating these patterns may be the task of a single team or a group of teams, but the point is this: There will always be a need for a team, for some project (the demands of the marketplace substantiate this point), which means there will always be a need for talent. The challenge, as always, will involve identifying, screening, and gathering that talent.

I raise this issue not to confound readers, but to reaffirm my earlier comment about clarity. Successful solutions come from asking the right questions. Forming those queries—knowing what to ask and to whom there is a duty to answer these questions—is the best way for a company to gain the advantages it seeks. Put a different way, learning precedes intelligence and wisdom; the former, with its emphasis on investigation (and questioning) produces the latter (and the insight to make a brand more competitive).

Only the right tools and technology can turn data into useful information. The good news is that these assets are available for a fraction of the cost, compared with lesser options from a few years ago, thanks to cloud-based systems. Remember, however, that technology is a component, an important one, that requires talented professionals to maximize these capabilities. With the right skills, for the right projects, these teams can give managers the material they need. More than a single analyst can provide, real talent represents a multitude of strengths with a surplus of solutions.

Daniel Stiefel is founder and president of Practical Data Analytics.