Real-World Data for Training AI Models

Artificial intelligence (AI) can be leveraged to understand patterns and use this to supply information used for strategic business decisions. Leading businesses and their data teams are leveraging artificial intelligence to improve demand planning and forecasting accuracy, and these businesses are seeing results. But in order to build effective AI systems and ML models to perfect your forecasting methods, it's essential to invest in large amounts of data and the right types of data, which really means quality data. And it doesn’t stop there. In the rapidly evolving landscape of artificial intelligence, the key to smarter decision making lies not just in the training data we feed into our models, but in the real-world context it can tap into once these models are trained. Allowing for more accurate and dynamic understanding to ensure the models recommendations are able to navigate volatility. The world is dynamic, and the models that give us a competitive edge need to be as well.

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 likely obvious to forecasting experts and data gurus, a Gartner survey run during a 2023 Data & Analytics Summit found that 33% to 38% of organizations reported failures or delays in AI projects due to poor quality data. 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.

AI systems demonstrate an unprecedented capacity to process vast quantities of data rapidly. However, they often lack the ability to understand the dynamic and fluid nature of real-world events. Traditional datasets, even the most comprehensive ones, can't keep pace with the constantly changing nature of event data. Think about changed dates, changed start and end times for events, cancellations and more. Even some of the largest events can get changed last minute.

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There are various challenges data teams face when using external data sources and training AI models, but two notable issues are data diversity and data quality. PredictHQs event data mitigates these issues so your team can focus on fine tuning AI systems and ML methods to improve demand prediction. 

Data diversity 

Artificial intelligence systems use algorithms to assess datasets, identify demand patterns and use propensity models or forecast aggregators to start making predictions. To prevent data bias and forecasting errors, it’s essential to provide systems with a breadth of data. PredictHQ uses hundreds of external data sources and proprietary data to ensure models have the depth and diversity needed to identify demand patterns. We cover 19+ event categories, allowing AI models to learn from a variety of data. 

Data quality 

The quality of data plays a huge role when training AI models. If your models are ingesting non-standardized, duplicative, spam data, artificial intelligence systems won’t be able to accurately identify patterns. PredictHQ’s data processing system aggregates, standardizes, dedupes, and filters millions of raw data points into a single format. PredictHQ’s data scientists have built 1,000+ machine learning models to ensure the quality and accuracy of our forecast-grade data. Our AI-driven data quality comes from aggregating, deduping, and cleansing data from thousands of sources. This ensures that you’re training your AI models with high quality and accurate data. 

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AI models used in forecasting 

When intelligent event data is paired with internal data sources, teams can build effective AI m that can be used to improve forecasting accuracy. Popular AI systems used in forecasting are neural networks, expert systems, and belief networks.

• Neural Networks enable artificial intelligence machines to recognize and learn patterns in supply and demand. 
• Expert Systems replace any judgement by creating ‘knowledge’ stores in if-then rules.
• Belief Network based systems are structured in a tree format. The nodes represent variables and the branches the conditional dependencies between variables.