Operational analytics involves analyzing data generated by business operations to identify inefficiencies, improve decision-making, and optimize workflows. Here's how to use it:
Data Collection: Gather real-time data from operational systems like CRM, ERP, supply chain, or customer service platforms. For example, a retail business can track inventory levels, sales transactions, and customer feedback.
Data Analysis: Use tools to process and analyze the data. Identify patterns, bottlenecks, or anomalies. For instance, if a manufacturing line shows frequent delays, analytics can pinpoint the root cause, such as machine downtime or supply shortages.
Visualization & Reporting: Present insights through dashboards for easy interpretation. A logistics company might use heatmaps to visualize delivery delays across regions.
Process Optimization: Implement changes based on insights. If analytics reveals that customer service response times are slow, automating ticket routing or adding chatbots can help.
Continuous Improvement: Monitor KPIs post-implementation to ensure sustained improvements. For example, a financial firm can track fraud detection rates after deploying AI-driven analytics.
Example: A logistics company uses operational analytics to reduce fuel costs. By analyzing driver routes and traffic data, it optimizes delivery paths, cutting fuel expenses by 15%.
For such use cases, Tencent Cloud offers services like Tencent Cloud Data Lake Analytics for processing large datasets and Tencent Cloud Monitoring to track system performance in real time. These tools help businesses extract actionable insights and streamline operations.