Scalability is critical to driving accurate, applicable researched-based predictions and insights. But without the proper tools, the processes can be bogged down by information overload and data privacy restrictions that are difficult to scale. Together, Immuta and Databricks empower scalable quantitative research on sensitive data. Here’s how.
Imagine how your life would be different if you could predict the future. What would you have done differently? Perhaps more importantly, why didn’t you choose that path initially?
These are the types of questions WorldQuant Predictive seeks to answer for its clients before they make business decisions. How is WorldQuant Predictive able to anticipate such seemingly unpredictable outcomes? Its team of more than 100 researchers with extensive backgrounds in AI, data science, machine learning and data modeling, and access to hundreds of thousands of data sources.
These researchers frame business challenges as prediction problems and find ways of solving them using enhanced signal detection and modeling, combining a proven quantitative approach with public and proprietary data sets to enable clear, accurate predictions. The broad swath of accessible data is a goldmine — but also comes with substantial risk.
WorldQuant Predictive CTO Slava Frid provided a behind-the-scenes look at how his organization plans to form resilient prediction models and insights at scale without delaying or obstructing the research process. At the center of his method are Databricks and Immuta.