2019 Thinking Points - Restaurant Data

The following content stems from a presentation by Dave Bennett, CEO of Mirus at the Mirus User Conference (MCON) in 2018.

Next Level Thinking

Every useful piece of data is connected to other bits of information in the restaurant business. All of the things a restaurant does every day are related to serving its customers. It makes sense that the data created in the process is also connected. Not all of the connections are clean and easily understood, though. Just the opposite. There are certain business artifacts that support this confusion of data. They are the ﬁnal “systems of record” where everything ends up at the end of the business process.

The most obvious of these artifacts is the Proﬁt and Loss Statement and the Balance Sheet. These ﬁnancial documents are the sum of all the checks, time punches, invoices and expenses that take place in a restaurant company in a given period of time. They are examples of data that are directly related to the ﬁnancial statements. However, you can also link data indirectly to the P&L, such as customer comments, store inspections, reservation comments, and loyalty program registrations. The relationship to the P&L and data like customer comments, for example, are not necessarily immediate. There might be a period of time between cause and eﬀect. If you see an increase in customer complaints, it might lead to reduced sales, but not right away. Examining the indirectly related measures in relation to data feeding the P&L and Balance Sheet can reveal problems early, when they're likely more easy to resolve.

The P&L and Balance Sheet are not the only artifacts that are the ﬁnal “system of record”. Sometimes they’re hidden in ﬁle drawers, spreadsheets, and sometimes in formal systems. Understanding these connections is key to unlocking the value of integrations. As you add sources to your data warehouse you expand the number of indirect and direct relationships that exist between the different pieces of data. Everything is connected.

The Information Learning Curve

The information learning curve is a simple concept of how to measure a company’s use of data in relation to other companies. Companies with low use of data are looking at last week’s sales and check count, as an example. The tools they use are simple, likely Excel. Companies in the middle are looking at yesterday’s results, or maybe even today’s activity. They're using reports from their POS and/or back oﬃce vendor, but still using Excel to cut and paste the data into the ﬁnal reports to management. Companies that are high up on the information learning curve have invested in an enterprise data warehouse that is designed to take information from across the company and put it into one model for analysis. For these companies, the human eﬀort and potential for errors using cut and paste techniques is no longer acceptable. The data must be accurate, complete, and ﬂexible to allow for analysis in unstructured ways. These companies are looking to improve the performance of their restaurants by using data to pinpoint what changes are working, and where changes are needed.

Artificial Intelligence and Our Future

You have probably read stories about how robots will be working in restaurants directly serving customers. AI will have a big impact on the restaurant industry as it will in most every industry. The concept that a computer can learn by itself over time when fed enough data is mind boggling but its a reality. Businesses need people who can ﬁnd patterns and relationships inside the data so they can better compete for market share and proﬁtability. The future is ﬁlled with data, and anyone who knows how to sift through it will be highly demanded. How will you you learn and get better at data mining? It’s about studying data. Looking for patterns. Identifying relationships. Deconstructing the details to separate the good from the bad.