How to set up a new data analytics function for a restaurant

By Olukorede Balogun

For a proper response to the ever-increasing need for data-driven decision-making to be able to optimise operations and revenue in business the world over, setting up a tailor-made data analytics (DA) function for a restaurant is critical. In addition to being of tremendous benefit to the restaurant, it enhances overall efficiency and profitability. As the world economy constantly evolves, leveraging a DA function set up by a restaurant gives businesses a competitive edge, enhances customer experience, and drives business growth.

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Setting up a Data Analytic (DA) function at a restaurant to achieve the goals above requires having to partially extend its IT footprint into the cloud to provide consumption-based (PAYG) cost-efficient storage for the restaurant data, thereby eliminating capex-heavy on-premises environments. In addition, it enhances the use of auto-scaling to meet demands at peak periods without excess capacity provisioning, improves productivity by empowering self-service users, and provides access to applications on any device from any of the business locations, leveraging the connectivity architecture of a cloud service provider (CSP).

When considering the possible CSP, the most appropriate for a restaurant is the Google Cloud solution because it is relatively user-friendly with a comparatively simpler interface as well as providing the benefit of an enormous commitment to uptime of 99.99 percent, and any plunge lower than this monthly uptime, as provided by the Google Cloud Compute Engine SLA, will amount to financial credit to the restaurant. It is helpful to ease the burden of users in managing the physical infrastructure, as all sorts of device maintenance and updates are performed by Google.

Google Sheets, provided by Google Cloud, is a budget-friendly application software option for analysing a restaurant’s data because it provides a simple and cost-effective solution that aligns with the budgets of a restaurant, especially on small and large scales, and allows for data import from various sources, including social media platforms and transactional databases, which are the typical sources of data for restaurants. It has built-in functions and add-ons that enable the restaurant to perform basic analytics, such as trend analysis, sentiment analysis from social media comments, and financial calculations based on transactional data.

Data collected from these sources is needed for carrying out analysis by Google Sheets SW and is collected into Big Data by using Application Programming Interfaces (APIs) provided by social media platforms or third-party services to pull data directly into your Big Data environment. The restaurant will develop ETL pipelines to extract data from transactional databases or flat files, transform it into a suitable format like JSON, and load it into BigQuery. Since the data will be stored on a SQL server, SQL Server Management Studio will be used to format the data, while Google Cloud Dataflow will be used to extract, transform, and load data into a data warehouse or data lake for analysis.

A restaurant may use Amazon Redshift Data Warehouse, which is a cloud-based warehousing solution offered by Amazon Web Services (AWS). It is appropriate because of its performance, integration, and scalability capabilities to accommodate the potential growing data needs of a restaurant’s DA function in the future, in addition to aligning with the budget and long-term cost-effectiveness. Local Area Network (LAN) and Wide Area Network (WAN) are required for the restaurant’s business if there are more than one location to efficiently operate and manage its operations.

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This requires some components that will be connected to the internet, provided by the Internet Service Provider (ISP). Verizon Business, an ISP, provides fibre internet with symmetrical upload and download speeds that range between 200 Mbps and 940 Mbps, delivering ample speeds at cost-efficient pricing and slower plans that go perfectly with budget. The potential risks of data exposure when connecting networks to the internet are mitigated by installing a firewall that provides security by examining not just the source but also the port numbers, destination IP addresses, and protocol type of each packet going through a preliminary network to determine whether to allow the packet or block it.

The set-up of a proper DA function for a small or medium-scale restaurant typically requires between four and six months to complete if resources are promptly provided. For the first year of DA set-up, opex and capex are usually reasonable because infrastructures are subscribed to by various providers and not acquired on-premises. For subsequent years, Opex will typically remain the same amount, while any increase will be the potential cost of fixing existing assets.

Olukorede Balogun; Managing Director, Peculiar Depictions Ltd.

B.Sc Economics, M.Sc Information Technology and a Fellow of several professional institutes.