Get better answers and faster results at scale with ClearScape Analytics®

 In data analytics, scale refers to the ability to handle growing volumes of data, increasing numbers of users, and more complex analytic workloads without degradation. For ClearScape Analytics®, scaling means vertical and horizontal growth—processing bigger models, training many models concurrently, and supporting vast data volumes—while maintaining performance and cost efficiency. 

The core components include: 

1. Data preparation and feature engineering: Efficient data transformation, cleaning, and feature extraction at large scale 
2. Model training and selection: Scalable model training engines that support many models (or large models) concurrently 
3. Model deployment and scoring: Real-time or batch inference integrated with business systems 
4. ModelOps/lifecycle management: Monitoring, retraining, drift detection, governance, auditing 
5. Interoperability and integration: Ability to import, use, and manage models built with external tools (Bring Your Own Model/Bring Your Own Analytics) and integrate with other systems 

 Analytics capabilities typically include: 

• Descriptive analytics: Summarizing historical or current data (such as reports or dashboards) 
• Diagnostic analytics: Examining the "why" behind outcomes or patterns 
• Predictive analytics: Forecasting future outcomes using statistical or ML models 
• Prescriptive analytics: Recommending actions based on predictions and constraints 
• Streaming/real‑time analytics: Processing events and producing insights continuously 
• Advanced analytics and AI/ML: Including time series, anomaly detection, natural language, embeddings, and generative models