Traditional weather data often falls short in providing the level of detail and accuracy required by modern businesses. Challenges include:
Low spatial resolution: Traditional weather data sources provide information at broad geographic scales, often missing critical localised variations. For industries like agriculture, logistics, and insurance, this low spatial resolution limits the ability to monitor precise weather conditions on a field, farm, or route level, making it difficult to adapt operations to local weather patterns and increasing the potential for costly disruptions.
Inaccurate predictions: Standard weather forecasting models often struggle to predict sudden, extreme weather events with high accuracy. This limitation can leave businesses unprepared for critical conditions such as flash floods, high winds, or unexpected frosts. For industries reliant on timely responses to extreme events, such as supply chain management and livestock health, inaccurate predictions can lead to lost revenue, increased risks, and diminished operational resilience.
Data accessibility: Access to real-time and historical weather data remains limited with many traditional providers. For businesses requiring immediate updates or historical trends for risk assessments and planning, delays or gaps in data access hinder timely decision-making. This lack of accessibility makes it difficult to adopt proactive strategies, particularly in weather-sensitive industries like renewable energy, where real-time data is vital to optimise energy production.
Data format limitations: Traditional weather data often lacks flexibility in format, making it challenging to integrate with existing business systems and analytical tools. Without seamless integration options, organizations face added time and cost burdens in converting and adapting data for their specific needs. In tech-driven industries such as insure-tech and fintech, the inability to embed weather insights into applications and workflows directly limits the utility and impact of the data.