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Machine intelligence enhances employee performance by automating increasingly complicated workflows and developing cognitive agents.
FREMONT, CA: The advanced analytics journey continues with machine intelligence. The goal of cognitive systems is to automatically comprehend concepts and relationships from data, learn independently based on data patterns and previous experiences, and extend what humans or machines could accomplish on their own. Companies can easily access untapped nontraditional data sources using computer vision, pattern recognition, and cognitive analytics. They can be images, audio, and video files, information from IoT devices, and raw web data to gain insights that lead to better business decisions and experiences. Moreover, machine intelligence provides algorithmic capabilities that enhance employee performance, automate increasingly complex workloads, and develop "cognitive agents" that mimic human behavior.
Safety analytics
Analytics helps to reduce safety risks in health and safety. Data from various sources such as employee time and attendance, employee leave, previous incidents, weather incidents, and so on can identify and profile high-risk employees. By leveraging knowledge from past experiences, such as safety incidents and near misses, safety incidents are predicted. Information from past experiences is shared to prevent safety incidents in error-prone situations.
Monitoring trade
Detects and minimizes trading risk and costs by detecting fraud, abuse, and other issues. Trading analytics helps detect fraud, market abuse, supervisory failures, and other threats. Potential trading issues are detected using structured and unstructured analytics. Data analytics tools and technologies perform network analysis, trading pattern recognition, cluster analysis, and risk scoring. Monitoring algorithms help watch for rules and threshold violations, while unstructured analytics evaluate text and voice communications.
Management analytics for inventory
Anomalies in user behavior are identified with advanced analytics. A root cause analysis identifies errors in process cycle movement (in transit between shipping locations), violations of duty segregation, and excessive inventory consumption and adjustments resulting in abuse, misuse, or fraud. Analytics are customized based on the regional preferences and activities for a deeper insight into inventory movements. Cost leakage is reduced, inventory changes can be better understood, and reporting can be improved with advanced analytics.
