The Huge Challenge of Big Data

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There are two little words that have huge implications for on-premise operations: big data. It’s how businesses big and small come to better understand their customers, track sales, make recommendations, create targeted marketing campaigns, implement successful promotions, enhance the customer experience, and tighten other aspects of their operations. Utilizing the POS, inventory, and marketing systems that are in place, restaurants can benefit from understanding the big data they collect every day.

Big data is most often described according to volume, velocity and variety, or the Three Vs. The volume of data available is staggering, so one must learn to analyze it in order to determine its relevance and value. Advances in technology have increased the speed at which data streamed, making it incredibly difficult process quickly enough for it to remain relevant and actionable. Finally, data is collected in a variety of formats, some structured and some unstructured, meaning that another challenge facing operators is data management. So, operators must ask themselves three questions:

  • How will we determine the relevance and value of the data collected?
  • How can we react quickly enough to data velocity?
  • How will we manage all of this data?

Business analytics software company SAS believes that two more characteristics are needed to truly understand big data. They are variability and complexity. Just as a restaurant peak times (daily, seasonal, event, etc.), so does data. SAS also takes the stance on data that it can get out of control if it isn’t connected and correlated.

It’s important to understand structured versus unstructured data (mentioned above). Consider structured data internal (generated inside the business) and unstructured data external (generated outside the business). An operation’s labor costs, accounting data, POS data, and supplier information are examples of structured data. Customer profiles, loyalty programs, social media posts, weather and traffic patterns, these are all types of unstructured data. Put together, both of these types of data reveal the what (structured data) and the why (unstructured data) of customers.

Collecting data serves two main purposes: prediction and improvement. Big data allows operators to predict customer needs, wants and behaviors, along with trends. In turn, if that data is used properly, menus can be changed, new markets can be better analyzed, and other changes that can reduce cost and therefore improve operations and the bottom line can be implemented.

Of course, data provides no benefit to an operator if it can’t be measured. That presents a problem for some businesses, because the inability to measure something leads to the inability to assume control over it, which it turn means it can’t be improved. Operators need to decide know where their data is coming from, how to identify what is actionable, how to track it, and how to optimize it. It may seem obvious, but it’s worth stating that an operation’s staff is responsible for most data collection. Inventory, overhead, and sales transactions are all sources of data that can be analyzed and used to improve operations by increasing sales and reducing costs. It is crucial that operators learn to use their POS systems to their utmost capabilities because they are a treasure trove of valuable information. Powerful POS systems collect massive amounts data and are capable of generating informative reports. As these systems improve, some are coming equipped with guest-check analytics. Many POS systems that do not have this capability built in are available as add-ons. Another improvement to POS systems and data collection is the cloud, which makes information accessible via tablet, mobile device, or networked computer. It behooves operators to fully understand their POS systems, receive training on them if necessary, implement the same training, and add additional beneficial features if available. If updated capabilities aren’t available, it may be time to look at upgrading to take advantage of cutting edge data collection and reporting technology.

Other advancements in technology are business intelligence (BI) software and monitoring software. BI solutions allow operators to achieve customer insights, improve staffing, determine profit margins, and track their inventory. Monitoring software, as the name implies, can monitor social media and be programmed to alert operators whenever their business is mentioned.

Data can help operators truly understand their customers and their customers’ needs, drill down on labor and other costs, acquire new customers, engage with them, and gain insight into them, and personalize the guest experience. However, it can be overwhelming attempting to gather and analyze all of that data. The best practices for tackling big data are to start small with bite-sized bits of data, have challenges that require solutions in mind, identify data collection needs, refrain from adopting too many avenues of data collection, find the solutions that best fit specific needs, ensure marketing departments and IT work together and communicate well with one another, and explain why big data collection is so valuable to the business.

It’s also important that operators understand the collection of information requires protecting customer privacy. Identity theft is an ever-present and increasing threat, so data must be collected according to regulations, laws, and current standards. Understanding the law, creating a privacy policy, creating a privacy policy notice, and adhering to the standards developed in-house will go a long way with customers in setting their minds at ease about data collection. Operators opting to use third-party data collection must investigate their adherence to laws and regulations, and their data-protection methods.

Big data can be overwhelming and comes with serious responsibilities, but its benefits certainly make information collection worth it.

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