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Partner, Sugi Hadiwijaya and OverlayAnalytics‘ Founder and CEO, Bryan Shupe, discuss how similar to challenges around kitchen management, data challenges are multi-faceted, from data compilation and interpretation, to conversion and utilization, and cultural integration with Nation’s Restaurant News.
Chaos in the kitchen. It’s a nightmarish experience for restaurant operators, ranging from incorrect orders, running out of ingredients, and understaffed kitchen personnel, which all leads to dissatisfied customers, exhausted staff members, poor decision-making, and shrinking margins. Chaos in data analytics can have similar consequences. Recent trends such as diverse sales channels (on-premises and off-premises) and customer feedback loops (field inputs, online reviews, surveys, etc.) create a multi-layered restaurant operating environment, turning data analytics into a necessity rather than an option. Similar to challenges around kitchen management, data challenges are multi-faceted, from data compilation and interpretation, to conversion and utilization, and cultural integration.
Data compilation revolves around migration and centralization. Restaurants operate in multi-platform IT environments where data typically resides in multiple places. This creates a need to incorporate a system that compiles data in one place — a central repository. A central repository refers to a collection of accumulated data from an organization’s existing databases over time. It’s the harmonization of records that creates a platform for activities, records, and transactions to be easily accessed, shared, and analyzed. It helps to detect current progress and identify room for future improvements and productivity. A well-managed central repository preserves data quality and integrity, as well as references to past events and records. A primary challenge restaurant operators face when utilizing a central repository is maintaining data integrity. At the onset of implementing a central repository, restaurant data analytics must analyze factors that may distort integrity such as uncommon and incomplete data population, and KPI definitions with embedded algorithms. Moreover, mere data accumulation over time may result in integrity degradation without proper management and caretaking efforts such as normalization, testing, and filtering.
Management needs to understand data interpretation and limitations on embedded algorithms when aggregating data. A simple example is guest count. One chain may capture a meal as one guest, while others may require servers to physically count guests. Chains may count non-meal orders (such as appetizers or drinks) as a portion (one-half or one-third) of a guest, while others count them as one guest if the orders were entered at the bar. There is no perfect algorithm. Instead, management should understand its limitations and embrace an open-minded attitude. Over time revisions may be required due to internal changes from operations, or external from customers or competition.