How Big Data Works For Hospitality

By Sasha Poljak, CEO, Nimble Software Systems

Predictive analytics eliminate the guesswork from staff scheduling.

How do you, as a Detroit sports bar manager, staff for the evening shift when the #1 ranked Ohio State Football team is playing #4 ranked Michigan State on an unusually warm and sunny Saturday afternoon in December, following the previous Friday’s DJIA drop of 700 points which came as a result of the consumer confidence report reflecting the worst sentiment in five years, predicting a long recession and corporate layoffs?

While there are so many moving parts in a dining establishment, labor is the second largest concern. Up until very recently, any effort to optimize labor was a dangerous guessing game. Management was left alone with semi adequate tools and no visibility to real data to create shifts, manage exceptions, and react to unexpected changes. 

Not any more; enter Big Data!  Today, management is able to take the guess work out of staff scheduling by taking the historical sales data, future business forecasts, as well as various environmental factors such as weather, financial news, and special events, and put that data through computing engine parameters in order to create the most optimized staff schedule. Apps that leverage machine learning also consider local labor rules, staff availability, preferences, performance indicators, and staff ranking.  The result is the most optimal, suggested staff schedule based on true data, rather than “tribal” guess work.  Subsequently, management is left to only deal with the exceptions.

The beauty of this process is that the staff is also involved by having access to easy-to-use mobile tools where they can set their preferences and availabilities, pick up and drop shifts, request time off, and communicate with management in real time.

Where does the data come from?  The historical sales data can seamlessly flow from either a “legacy,” or a number of modern, cloud-based POS systems, EPSON Cloud’s ecosystem, or simply be manually imported. The environmental factors can be linked from any source capable of a modern data exchange.  Willingness of today’s tech and content providers to build open and robust API platforms is turning this recent “pipedream” into a reality.

I am not a restaurateur; I am simply a patron and an admirer. As many do, I frequent both fine dining and casual, QSR establishments. Most of the time, I ask myself the same questions:  How do they do it?  How does the management maintain such service levels, without driving themselves out of business?  What happens when the unexpected hits?  How do they create a harmony out of perceived chaos? 

I am excited to see the recent development and deployment of the “smart” applications in the restaurant industry.  They are a result of relentless cooperation between ventures developing them and the hospitality industry participants needing them to improve their bottom line.

You may just be able to enjoy that Ohio State Vs Michigan State game without sweating the balance between service levels and cost over runs. Cheers to that!

Sasha Poljak is the CEO of Nimble Software Systems, Inc., an employee time management software company that helps clients optimize their workforce.