Apps and Big Data: How They Are Changing The World of Multi-Location Restaurants – Part I

You’ve surely seen the hopeful ads about for how Big Data can help cure cancer and stop deadly attacks, but you know what Big Data is really ideal for?

Multi-unit restaurants.

That’s right.

Oh sure, we’ll need Big Data to cure diseases and save the world, but Big Data excels at process optimization and workflow analytics that are exactly what we need to make multi-location restaurants more profitable and to solve problems that, before Big Data, seemed mysterious to managers.

Specifically, Big Data is ideal for:

  1. Gathering large amounts of data from an unlimited number of sources, a.k.a. ingestion.
  2. Detecting patterns in that data; and these patterns can be extraordinarily complex, such as comparing third shift revenues across 16 locations, while tracking the additional or subtraction of menu specials, viewed by server, by gender, and correlated to the local weather.
  3. Synthesizing the data into key performance indicators, in an unlimited array of data slices, which are limited only by your imagination in dreaming up how you’d like to see and compare performance.
  4. Presenting the data in special-temporal presentations (graphs and vectors) that offer actionable intelligence and trend spotting.

Too Academic? Nope. 

Does all of that sound a little too academic and abstract?

It isn’t. Let’s take a closer look.

Here is a short list of common inspection data points for a typical multi-location restaurant:

  • Cold potentially hazardous foods maintained at 41F or below
  • Food products not held, or sold past expiration
  • Food properly covered and protected
  • Frozen foods held solidly frozen
  • Fruits and vegetables properly washed prior to processing and serving
  • Hot potentially hazardous foods maintained at 140F or above
  • Walk-in cooler product temperatures maintained at 41F or below.

As the information is collected for each of these data points, the restaurant worker needs to identify themselves, note the actual temperature, note the time of the inspection, note the location of the data, and perhaps make a comment / take a photo.

Typically, this has to happen multiple times a day.  So, the inspections are potentially undertaken by many different people, all with varying degrees of skill.

Now, take these inspection items (and this sample list from above is just a fraction of the items that need to be inspected daily) and multiple them by the number of locations you are managing.  The complexity of consolidating and analyzing this data in a pre-Big Data world (especially if it were just written down on clipboards and thrown in a binder) make the usefulness of this data practically nil.  Fact is, data was collected only as a CYA exercise in case there was ever a problem or an inspection, and you needed historical data records to review.  But now that Big Data has come into play, this data can be collected, and algorithms written, to accomplish these following Big Data tasks…tasks that were nearly impossible to accomplish just a few short years ago:

  1. Gather the data in real time, with auto-triggers and alerts that can watch trends and predict problems before they occur or that allow you to dispatch a worker with remedial actions, e.g. manager gets a text when the fridge temp rises above 41F.
  2. View the data at the individual location level, the regional level, or the enterprise level, or slice and dice the data to just look at, say, third shifts, or just at certain managers, or just at certain individual indicators, like “food sold past expiration” in relation to desperate workers trying to keep food costs inline to cover up theft, e.g. VP of Ops gets notified in real time so he can alert an area manager to conduct an inventory. That is how you drive accountability into your organization.
  3. Correlate any number of location data points to sales, or even to outside sources like Yelp or Trip Advisor. If the bathroom is filthy and the inspections are missed (as indicated by a lack of data points), it should come as no surprise the customers stop eating at that location and are posting bad reviews, e.g. the fix is easy, once you know the cause of the problem.
  4. Use big data to identify the cost control issues in your bottom 20% of restaurants that are eroding profits chain wide, develop an operational fix, and direct your area managers to focus their efforts on fixing those issues.  Then use your data collection to track the success or failure of those initiatives.  That is the accountability management that is enabled by Big Data.

Stay tuned for part II later on this week. Follow us on Linkedin so that you don’t miss part II.

Tommy Yionoulis

I've been in the restaurant industry for most of my adult life. I have a BSBA from University of Denver Hotel Restaurant school and an MBA from the same. When I wasn't working in restaurants I was either doing stand-up comedy, for 10 years, or large enterprise software consulting. I'm currently the Managing Director of OpsAnalitica and our Inspector platform was originally conceived when I worked for one of the largest sandwich franchisors in the country. You can reach out to me through LinkedIn.

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3 Comments

  1. […] is part two. Part one was posted on Monday, click here to read part one if you haven’t read it […]

  2. Great stuff

  3. Thanks for your comment Michael. Glad you enjoyed it.

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