See also Business Intelligence 2.0

Real-time business intelligence is the process of delivering information about business operations as they occur.

In this context, real-time means a range from milliseconds to a few seconds after the business event has occurred. While traditional business intelligence presents historical data for manual analysis, real-time business intelligence compares current business events with historical patterns to detect problems or opportunities automatically. This automated analysis capability enables corrective actions to be initiated and or business rules to be adjusted to optimize business processes.

Contents 1 Latency in real-time systems 2 Real-time Business Intelligence Architectures 2.1 Event based Real-time Business Intelligence 2.2 Real-time data warehouse 2.3 Real-time server-less technology 2.4 Process-aware Real-time Business Intelligence 3 Application areas 4 References

[edit] Latency in real-time systems

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All real-time business intelligence systems have some latency, but the goal is to minimize the time from the business event happening to a corrective action or notification being initiated. Analyst Richard Hackathorn describes three types of latency:

• Data latency; the time taken to collect and store the data
• Analysis latency; the time taken to analyze the data and turn it into actionable information
• Action latency; the time taken to react to the information and take action

Real-time business intelligence technologies are designed to reduce all three latencies to as close to zero as possible, whereas traditional business intelligence only seeks to reduce data latency and does not address analysis latency or action latency since both are governed by manual processes.

Some commentators have introduced the concept of right time business intelligence which proposes that information should be delivered just before it is required, and not necessarily in real-time.

[edit] Real-time Business Intelligence Architectures

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[edit] Event based Real-time Business Intelligence

Real-time Business Intelligence systems are event driven, and use Event Stream Processing techniques to enable events to be analysed without being first transformed and stored in a database. These in- memory techniques have the advantage that high rates of events can be monitored, and since data does not have to be written into databases data latency can be reduced to milliseconds.

[edit] Real-time data warehouse

An alternative approach to event driven architectures is to increase the refresh cycle of an existing data warehouse to update the data more frequently. These real-time data warehouse systems can achieve near real-time update of data, where the data latency typically is in the range from minutes to hours. The analysis of the data is still usually manual, so the total latency is significantly different from event driven architectural approaches.

[edit] Real-time server-less technology

The latest alternative innovation to "real-time" event driven and/or "real-time" data warehouse architectures is MSSO Technology (Multiple Source Simple Output) which removes the need for the data warehouse and intermediary servers altogether since it is able to access live data directly from the source (even from multiple, disparate sources). Because live data is accessed directly by server-less means, it provides the potential for zero-latency, real-time data in the truest sense.

[edit] Process-aware Real-time Business Intelligence

This is sometimes considered a subset of Operational intelligence and is also identified with Business Activity Monitoring. It allows entire processes (transactions, steps) to be monitored, metrics (latency, completion/failed ratios, etc.) to be viewed, compared with warehoused historic data, and trended in real-time. Advanced implementations allow threshold detection, alerting and providing feedback to the process execution systems themselves, thereby 'closing the loop'.

[edit] Application areas

• Algorithmic trading
• Fraud detection
• Systems monitoring
• Application performance monitoring
• Customer Relationship Management
• Demand sensing
• Dynamic pricing and yield management
• Data validation
• Operational intelligence and risk management
• Payments & cash monitoring
• Data security monitoring
• Supply chain optimization
• RFID/sensor network data analysis
• Workstreaming
• Call center optimization
• Enterprise Mashups and Mashup Dashboards

This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations where appropriate. (February 2008)

[edit] References

• Colin White - Now is the Right Time for Real-Time BI
• Colin White - Building the Smart Business:In-Line Real-Time BI
• Seven Successful Rules for real-time data warehousing