Use Case: Data engineering & analytics platform

IoT (Internet of Things)

The platform is useful for IoT (Internet of Things), such as:

• Predictive Maintenance – Platform trains machine learning model and with a click deploys models into production-ready environments. It enables interpretation of sensor data for: Life cycle estimation, Open maintenance windows, Fault classification, Warning-alerts.

• Asset Monitoring – Platform’s built-in charting library helps visualizing data with single click on any data source. Select the chart type, drag & drop the parameters for quick visualization of asset data. Useful for: Holistic visualization, Historical data pool, Real-time reporting.

• Edge Intelligence - Platform trains machine learning model. Target the variables & features – with one click, deploy the solution into production environment to start immediate results. Useful for: IoT solutions, Improve connectivity, Automation, Maximize customer experience.

Retail

The platform is useful for Retail industry, for aspects such as:

• Demand forecasting – Platform assists in demand forecasting by taking a data-centric and model-driven approach such as qualitative forecasting, time series analysis, causal model using its data automation tools. Useful for: Internal & external business data-driven models, ML based self improving models, Profit & revenue focus demand prediction, Warning alerts.

• Price Optimization - Platform’s analytics tools assist in AI-based data-driven growth engine to predict at what selling price an individual client is positively going to take the offering. The price analytics engine allows retailers to set optimized pricing for entire portfolio of offering.

• Recommendation Engines - Platform leverages filtering – cognitive, item-to-item, collaborative & hybrid information around user profiles to recommend to the users items which suit them the most. Useful for: Up-selling & cross-selling, demographic & behavioral patterns, go-live with AI recommendation, Speed & accuracy, Personalized content.

• Supply Chain Analytics - Platform’s machine learning models deal with large dynamic data sets. ML models get integrated into warehouse automation & supply chain planning. Procurement also benefit from adaptability of ML in constantly changing market demand.

Banking / Financial Industry

The platform is useful for Financial industry, for aspects such as:

• Credit Default Risk - Platform’s machine learning tools enable to integrate and fine-tune client-related data to enhance credit risk management and its analytics. Useful for: credit risk mitigation, credit risk monitoring & analysis, Self improving credit scoring model.

• Personalized Banking - Platform equips bank’s AI-based personalized banking tools with data engineering and ready to plug in data models. Leverage all existing data to build data intelligence to deliver relevant content, offers, and personalized experiences.

• Fraud Detection – Platform’s machine learning used for fraud detection system by creating models that have intelligence to properly classify transactions as either legit or fraudulent, based on transaction details - transactional data, behavioral patterns & anomalies.

• Customer 360 - Platform’s machine learning tools learn from customers & employee interactions, which are augmented by data scientist inputs to achieve the best results and insights. Build a distinct customer profile. Useful for: Customer 360 degree valuable insights, Real-time customer analytics, Customer-centric models, Granular customer segmentation.