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DATA ANALYTICS.
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DIAGNOSTIC ANALYTICS
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In-depth root cause analysis for operational issues including drill-down reporting. Identification of unusual data patterns (anomalies) that may indicate issues requiring attention.
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For industrial machinery, diagnostic data analytics helps prevent costly breakdowns by analyzing real-time sensor data from sources like vibration and temperature. It identifies anomalies and pinpoints the root cause of potential issues, such as a failing bearing or clogged filter, before they escalate. This allows for proactive, condition-based maintenance, significantly reducing unscheduled downtime and optimizing operational efficiency.
PREDICTIVE ANALYTICS
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Use of historical and current data, combined with statistical modeling and machine learning, to forecast future outcomes. It helps organizations anticipate trends, behaviors, and events, enabling proactive decision-making.
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A retail company can use predictive analytics to forecast demand for specific products based on historical sales data, seasonal trends, and even upcoming promotions. This allows them to optimize inventory levels, preventing both overstocking and stockouts. By anticipating customer preferences, they can ensure popular items are always available, leading to increased sales and customer satisfaction.
PRESCRIPTIVE ANALYTICS
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Prescriptive analytics not only forecasts future outcomes but also recommends specific actions to achieve desired results. It goes beyond prediction to suggest optimal solutions and decisions.
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A shipping company uses prescriptive analytics to optimize delivery routes for its fleet. By considering real-time traffic, weather, fuel costs, and package priorities, it recommends the most efficient path for each driver. This directly minimizes fuel consumption and delivery times, improving profitability and customer satisfaction.