AI & Machine Learning
Fueling proprietary models with clean, time-series economic motion
Navigating data challenges in AI/ML:
The biggest challenges
Data cleanliness & latency
Spending excessive engineering time cleaning, normalizing, and structuring messy, fragmented, or high-latency regional data for ingestion.
Feature engineering gap
Lack of unique, non-lagging predictive features (signals) to integrate into models, limiting predictive accuracy (alpha).
Regional data access
Inability to source consistent, structured, historical data for underserved regional markets to ensure global model robustness.
Model validation
Difficulty finding independent, validated economic benchmarks to test and backtest the performance of proprietary forecasting models.
Integration friction
Complex, slow, or unreliable data delivery methods that inhibit continuous model retraining and real-time inference.
End-to-end AI/ML solutions:
Addressing key challenges with signals
Zero-friction ingestion
DataSivio’s core value is providing clean, normalized, and timestamped time-series datasets of economic signals, eliminating up to 60% of data cleaning and transformation work.
High-alpha signals
Access ready-made predictive features, including Company Trajectory & Sector Momentum Scores, and Risk Markers, ready for direct integration into classification and regression models.
Non-lagging risk indicators
Use granular signals like workforce decline, and leadership instability to train proprietary credit risk and distress models with significantly higher predictive power than traditional financial data.
Macro-motion benchmarks
Integrate the Market Pulse and Demand Signals (trade flow, logistics activity) as leading macro features into regional and global economic forecasting models.
Continuous model retraining
Utilize the Signals API for high-volume, low-latency data access, ensuring your models can be continuously retrained and deployed for real-time inference and alerting.
