Batch vs. Real Time Data Processing

Batch vs. Real Time Data Processing

Batch vs. Real Time Data Processing

Batch data processing is an efficient way of processing high volumes of data is where a group of transactions is collected over a period of time. Data is collected, entered, processed and then the batch results are produced (Hadoop is focused on batch data processing). Batch processing requires separate programs for input, process and output. An example is payroll and billing systems. In contrast, real time data processing involves a continual input, process and output of data. Data must be processed in a small time period (or near real time). Radar systems, customer services and bank ATMs are examples.

While most organizations use batch data processing, sometimes an organization needs real time data processing. Real time data processing and analytics allows an organization the ability to take immediate action for those times when acting within seconds or minutes is significant. The goal is to obtain the insight required to act prudently at the right time - which increasingly means immediately.

Complex event processing (CEP) combines data from multiple sources to detect patterns and attempt to identify either opportunities or threats. The goal is to identify significant events and respond fast. Sales leads, orders or customer service calls are examples. Operational Intelligence (OI) uses real time data processing and CEP to gain insight into operations by running query analysis against live feeds and event data. OI is near real time analytics over operational data and provides visibility over many data sources.

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The goal is to obtain near real time insight using continuous analytics to allow the organization to take immediate action. Contrast this with operational business intelligence (BI) -  descriptive or historical analysis of operational data. OI real time analysis of operational data has much greater value. For example, Rose Business Technologies designs and builds real time OI systems for our retail clients to optimize customer service processes.

The ROI is improved customer satisfaction and reduced churn. OI is used to detect and remedy problems immediately - often before the customer knows of the problem. Real time OI is used in customer service centers for customer experience optimization. Recommendation applications can assist agents in providing personalized service based on each customer's experience.



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