Data Management Challenges for Restaurants

As part of the project we are developing with Mastercard Strive, we seek to provide solutions to small food businesses with appropriate interfaces to get quick answers from their own data. Below we present the main challenges we have identified during work sessions with business owners and workers, with whom we are co-creating a tool that allows them to make the most of the data they have historically collected.

Diversity of Data Sources 

Data fragmentation represents one of the biggest operational challenges for restaurants. On a daily basis, these businesses face the complex task of managing information stored in different formats and locations, such as spreadsheets, cloud services, POS systems, and production forms.

This variety of sources makes data integration difficult, which refers to the process of combining and harmonizing multiple sources into a unified and coherent format that can be used for various analytical, operational, and decision-making purposes. Without a system that integrates all this information, these objectives are delayed and lose precision, affecting the restaurant’s ability to respond to potential opportunities or losses at the right time.

Information dispersion creates significant obstacles for decision-making. For example, when a restaurant wants to answer questions like “which products should be kept in the offering and which should be removed based on their movement?” or “how often should a product be ordered according to its sales quantity?” they must navigate between various systems to get a complete picture. Manual consolidation of this data not only consumes valuable administrative staff time but also significantly increases the margin of error in analyses.

Data Quality and Accuracy 

The lack of integration between restaurant management systems forces manual tasks and repetitive processes, which generates inconsistencies that affect information quality and compromise analyses, potentially leading to wrong conclusions about the information.

For example, when a restaurant manages its sales in a POS system and inventory in Excel sheets, it must manually unify all this information. During this process, it’s common to find problems such as:

• Product names written in different ways (‘Coca-Cola’, ‘Coca Cola’, ‘CocaCola’)
• Quantities recorded in different units (grams vs. kilos)
• Dates in different formats (DD/MM/YY vs. MM/DD/YY)
• Order duplication when consolidating orders from multiple platforms

This problem, along with the lack of trained personnel or staff dedicated exclusively to data management, makes data management difficult, added to the costs and time required. Often, those who handle the information lack the necessary training to perform these tasks efficiently, aggravating data quality problems.

Leveraging Historical Data 

As a food business grows, so does the amount and complexity of its data, which represents a significant challenge for business managers. Making the most of this accumulated historical information is not an easy task, as it requires specific skills and advanced technological tools to transform it into useful knowledge.

Having the ability to answer crucial questions such as: Which recipes have increased in cost due to rising raw material prices? What personalized promotions can be designed for a customer based on their consumption history? These require efficient data management that allows for operational traceability and quick and accurate comparative analysis.

Real-Time Access 

In their daily operations, restaurants usually review their data partially week by week or generate reports at the end of the month. These reviews cover key aspects such as revenue obtained according to payment methods used (cash, card, QR), the incorporation of new products into inventory, and detailed recording of recipes and sub-recipes necessary for dish preparation.

The lack of periodic integration of data from different systems is a challenge because the data is often time-lagged and provides a limited and static view, rather than an updated panorama, making it difficult to make informed decisions in real-time and reducing the business’s ability to adapt to operational or market changes. By implementing processes that minimize inconsistencies and optimize information consolidation, dynamic performance analysis is facilitated. This allows for agile, precise decisions based on updated data, improving operational efficiency and business responsiveness.

Data Interpretation and Analysis 

Most restaurants lack specialized data analysis personnel, which makes it difficult to transform information into concrete decisions. This limitation is not just technical: dedicating time and costs to data interpretation represents a significant investment that many businesses are not prepared to assume.

Data management challenges are a daily reality for restaurants, and at Datasketch, we perfectly understand this need. That’s why we’ve developed a solution designed to transform the way restaurants manage their information. Our app integrates multiple data sources (try the free trial version) in one place, automates processes to minimize errors, and leverages data history to identify patterns that drive strategic decisions.

You don’t need to be a data expert or make large investments to convert your information into intelligent actions that will drive your business growth.