AI Analytics Platform
SparkBeyond Discovery autonomously analyzes complex data, surfacing powerful features and breakthrough insights in minutes.
Seamlessly integrate external data into your search space for a better grip on the real influencers of outcomes, and get a holistic perspective of your business reality.
Interact with features and insights in natural language, allowing a deeper alliance between analytics and business stakeholders.
AI-driven automation allows you to focus on solving problems, rather than joining data and coding features manually.
Automatically connect disparate and complex datasets without writing a single line of code.
See the bigger picture by connecting our curated library of external datasets including maps, Wikipedia, demographics and weather.
Discover powerful yet understandable features and drill down to explore the metrics of any particular feature.
Automatically uncover insights in your data landscape in explainable natural language, and share with stakeholders to take action.
Use built-in Auto-ML to train, tune and productionize models built on-platform with the included enterprise-grade 'Prediction Server', accelerating time-to-value.
Pinpoint the best locations to yield the most optimal customer experience and business results.
Analyze anomalies and subtle signals in large amounts of data to identify criminal activity.
Use granular customer insights to accurately predict the most effective product or service.
Anticipate changes in consumer preferences to tailor promotions, prices, and products for each customer.
Identify the right potential customers, shape the right message and pinpoint the best delivery methods.
Accurately identify customers at the highest risk of lapse for effective, personalized retention campaigns.
Analyze operational management — supply chain, logistics, field — and determine new factors that affect performance.
Accurately forecast equipment failures, and recommend action before faults and breakdowns cause costly delays.
Nearly all existing models have been invalidated by the coronavirus pandemic. They will presumably be invalidated again as the situation evolves as the world recovers. Even after recovery, it is unlikely that we will return to where we started before the crisis began. So how does that affect our data models?