Blog and Resources

Learn how automating data aggregation and normalization can enhance your data pipeline, freeing up valuable time for data scientists and driving better insights.
This article discusses the importance of lagged variables in predictive modeling, illustrating how they improve the accuracy of economic forecasts and analysis.
The labor market, through indicators like non-farm payrolls and the unemployment rate, is a key measure of economic health. Sectors such as manufacturing, construction, retail, financial services, and professional services provide valuable insights into economic conditions. By analyzing these trends, macroeconomists and business leaders can better assess economic growth and anticipate changes. However, looking beyond broad employment categories can yield more precise economic insights. One sector worth considering is truck transportation employment.
Starbucks released its Pumpkin Spice Latte (PSL) early this year on August 22nd, beating last year's launch and competitors. The decision aims to extend sales, stay ahead in the market, and leverage new leadership for heightened visibility.
In this blog post, Ready Signal highlights the critical importance of data quality through a real-world client example. Despite paying for commercial data, the client encountered significant issues with data misrepresentation that could have negatively impacted model performance. The post underscores the necessity of performing simple quality checks, such as examining null and zero values, to identify potential problems early. It also details Ready Signal's robust approach to data quality assurance, including thorough null checks, continuous API monitoring, and ongoing data verification to ensure accuracy and reliability.
Leading indicators are predictive metrics that offer valuable insights into future economic and business trends, enabling businesses to make informed decisions and strategically plan for the future. These indicators are crucial for proactive decision-making, strategic planning, resource optimization, and gaining a competitive edge. They also help businesses tailor their marketing strategies to predicted consumer behaviors and market conditions.
Forecasting accuracy is a critical element in decision-making processes for any business. In this video, DataRobot showcases the enhanced power of its AI platform when coupled with Ready Signal's external feature store. The result? A staggering 13% improvement in forecast accuracy in minutes. This video illustrates the immense potential of integrating external data seamlessly into predictive models. 
DataRobot AI Accelerators are revolutionizing data-driven decision-making, offering pre-built solutions that merge cutting-edge machine learning with domain-specific data. These accelerators simplify tasks from Hyperparameter Optimization to Time Series analysis, democratizing AI without extensive technical expertise. The partnership between Ready Signal and DataRobot has produced a unique AI Accelerator for Time Series Forecasting with External Data, integrating over 500 external sources. This collaboration automates critical steps, enhancing forecasting accuracy with weather, holidays, and social media data. Together, they represent a transformative force, optimizing marketing strategies and advancing time series forecasting in the era of data-driven decisions.
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