Realizing the Potential of Good Data

Make a list of the technology systems you presently utilize to run your business.  Add to that list types of systems you would like to implement.  This simple exercise is getting more complicated!

As restaurant technology (and technology in general) has changed over the last few years the landscape of systems you rely on to run your organization has expanded and changed as well.  From modern cloud POS software, to online ordering platforms, to third party delivery, to restaurant management, to loyalty, to proactive customer satisfaction there are lots of technology-based opportunities to improve business.

2019 Restaurant Technology TrendsThese modern systems provide restaurant organizations with new and robust data sources.  The data from each of these systems is important; however, real insight can only begin with the data from all of these systems working together.

But what are you looking for in this data?  Where do you start?  How do you get the data lined up in a way where information from all systems is easy to work with?  Is this Big Data?  All of this can very quickly add up to a case of data fatigue.

At Restaurant Analytics Delivered – Call us RAD – we love data! Multiple disparate sources constantly collecting information drive what we do – we help our partners realize the potential of their data and avoid data fatigue.  Below are the guiding principles we utilize to help our partners along their data journey.

  1. Focus on good data before big data

While Big Data is a definable concept, it is a lofty goal.  Like any challenge it can be overwhelming to think of the end point rather than looking for discreet solvable steps to get you there.

The first steps in your data journey should be aimed at creating a ranked and stacked list of data priorities and data complexities.

For example, transactional restaurant POS data is different than unstructured social data. Understand that both sources can be directionally correct while individual analysis of social data will not be as quantitatively accurate as detailed transaction analysis.

Look for small wins and big time-savers.

  1. Create a culture of analytics

We often find that there are multiple sources of truth for analytics within an organization.  Tied to the point above, a cultural shift often needs to happen to change perspective on what analytics are and how your organization uses them.

Start with a single source for basic operational metrics.  Discuss and challenge definitions for even basic data points that are regularly review.  Provide all levels of the organization are the same data to evaluate these metrics.

Buy-in from the top is critical, encourage executives to engage staff and discuss results.

  1. Track the right data for you

Restaurants have personality and their data does too.

While there are likely metrics you would like to track that many organizations look at, you may define them differently.  You may also include metrics that are not typical but are important to you.

Do not settle for reporting software that boxes you in to canned reports or metrics.  You need to be nimble, to be able to change your data definitions, and to create new metrics easily.

  1. Explore

All of this data is at your fingertips – now what?

Learn the systems and their granularity. Explore the source systems, ask questions, understand how you can find ticket level data as well as daily, weekly, and period-to-date summaries.

Have you wondered about attachment rates of certain products, or COGS efficiency with your LTOS?  Can you track traffic to labor efficiency throughout the day and against scheduled labor?  Now is the time to ask these questions and start drawing correlations.

  1. Evolve

By this point in the journey you will be ready to start getting into the buzzworthy topics of Big Data, Predictive Analytics, and Machine Learning.

If you have been guided through your data journey appropriately it won’t seem nearly as cumbersome as it did when you started.  You will have a strong sense of the types of data at your disposal, their efficacy within your data landscape, and how your data sources fit together.

Statistically relevant models can be developed for any number of purposes including identifying targeted marketing opportunities, better real estate selection, and ideal labor scheduling.

  1. Consider your data a tool, not the answer

Systems and technology will continue to change.  While RAD prides itself on being an adaptable partner that will help you integrate new systems and maintain data history as you grow, we believe true silver bullet lies within the restaurants at an operational level.  Trust your operators, and provide them with the tools to better their performance and the guests’ experience.

Visit our website and watch our video and learn more about RAD.