What exactly is high-quality data? How is it used? What is its purpose? These may be some of the questions that come to mind. After more than 20 years of experience, we’d like to share the answers we have come to.
High-quality data in logistics refers to information that is accurate, complete, consistent, reliable, and current. This includes data on orders (order number, delivery date, delivery address), transport data (vehicle type, load capacity, license plates, operator data), tracking and monitoring data (real-time location, goods status, estimated date and time of delivery), cost information (transport, storage, or handling), customer satisfaction data (satisfaction level, service quality), among others.
High-quality data is mainly used to make informed decisions and improve efficiency in the supply chain; it also helps reduce costs, increase productivity, increase customer satisfaction, and increase end-to-end supply chain visibility and transparency. Stopping work with incorrect, duplicate, fragmented, and outdated data helps to optimize and automate logistical tasks; imagine managing only 10% of all your trips because 90% is automated with reliable data! High-quality data is a guarantee for accurate decision-making, it contributes to regulatory compliance with data quality standards and allows for more accurate and predictive forecasts.
Because we know that implementing high-quality data in your supply chain requires a systematic and strategic approach, at Recurso Confiable we have a real-time logistics visibility platform called Control Track that collects data from various sources and then, through the implementation of Artificial Intelligence and advanced Machine Learning algorithms, can standardize, process, clean, analyze and interpret it, resulting in the identification of trends and patterns with clear and concise visualizations that facilitate decision-making. We establish key performance indicators (KPIs) to measure data quality, we monitor and adjust processes to ensure continuous quality and we carry out periodic audits to guarantee accuracy. Our team is trained to live a data culture oriented towards quality and thus maintain and update systems and tools regularly, adjust processes as necessary and work for the continuous improvement of data quality.
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