Artificial intelligence (AI) is defined as the simulation of human intelligence and decision making in machines. In turbulent business environments that many organizations are currently in, AI systems can be leveraged to understand business patterns and use this to supply information used for strategic decisions. Specifically, emerging AI systems are being used by data teams to improve forecasting accuracy, and businesses that are adopting these approaches are seeing results. But in order to build effective AI systems and ML models to perfect your time series forecasting methods, it's essential to invest in the right data.
Data challenges when adopting AI systems
To effectively train artificial intelligence systems for demand modeling, teams must think about the data they’re using. While that sounds obvious to forecasting experts and data gurus, a McKinsey report found that only 33% of organizations are effectively using internal and external data to take advantage of AI capabilities.
It’s easy for businesses to leverage historical sales data and seasonality trends. But there are a vast amount of external factors that cause unexplained demand anomalies that significantly impact the bottom line.
Event data provides insight into many external demand anomalies. You have the ability to train AI models to learn the patterns of events causing business disruptions and pivot strategies around inventory management, pricing, labor optimization, and more.