Big Data Services to Make a Big Difference

• Storing data about business processes, finances, resources, customers, etc. for analytical querying and reporting.
• Corporate performance analytics.
• Revenue, cost and investment analytics.
• Predicting, forecasting, planning (performance, revenue, capacity, etc.) with all interdependencies.

• Collecting, processing and storing large volumes of operational data (transactional data, production process data, asset data, employee data, plans, etc.)
• Detecting deviations and undesirable patterns in a company’s operations (production processes, product distribution, etc.)
• Recognizing bottlenecks (equipment failure, resource unavailability, etc.), conducting cause-effect analysis.
• Forecasting (demand, capacity, inventory, etc.)
• What-if scenario modeling and operational risk management.

• Analyzing manufacturing data (equipment year, model, sensor data, error messages, engine temperature, etc.) to predict equipment failures and the remaining useful time in real time.
• Real-time monitoring of production processes, production equipment data, materials usage, etc., to identify factors leading to production time increase and delays for production optimization.

• Capturing, storing, and analyzing patient-related data (doctor notes, medical images, EHR/EMR data, R&D results, etc.).
• Real-time patient monitoring and alerting on trends and patterns requiring the doctor’s attention.
• Personalized care plans recommendations.
• Mining claims data to identify fraudulent activity.

Forecasting the supply demand, supplier risks, etc., to enable healthcare supply chain optimization and planning.

• Analyzing integrated transactional data, interaction events, customer behavior in real time, identifying complex AML transactions, creating advanced risk models, etc., to identify potential fraud and fraud patterns.
• Consolidating and analyzing data on assets and liabilities and conducting credit risk assessment, liquidity risk assessment, counterparty risk analysis, etc., to mitigate financial risks.

• Tracking and analyzing real-time sensor data (cargo state, location, etc.) to make the delivery process fully transparent and ensure high-quality delivery of sensitive goods.
• Analyzing driver behavior, maintenance needs, weather data, traffic data, fuel consumption data, etc., in real time to enable dynamic route optimization.

• Analyzing customer demographic data, data from mobile apps, in-store purchases, etc. to identify customer paths and behavior to optimize merchandizing, provide personalized product recommendations, discounts, etc.
• Forecasting customer demand, analyzing the key attributes of past and current products/services and commercial success of their offerings, and using ML-driven recommendations to create new products/services.
• Consolidating and analyzing data from social media, web visits, call logs, and more to personalize customer support, launch tailored customer retention campaigns, etc.
• Analyzing customer transactions, spend patterns, predicting future customer actions with ML models to assess customer lifetime value, target marketing and sales offers to your best customers, etc.

• Analyzing log and sensor data from different types of equipment in real time and putting analytics results into operations to facilitate predictive equipment maintenance.
• Analyzing drilling and production process data, data generated from seismic monitors, etc., to identify new oil deposits.
• Analyzing sensor and historical production data and building ML-based predictive models to measure well production and understand the usage rate.

• Analyzing the network usage trends and patterns and using sophisticated models to forecast areas with excess capacity and optimize the network capacity.
• Analyzing overall customer satisfaction, identifying customer churn patterns, and recommending the most relevant products/services to increase customer retention.