Real time business intelligence

Real time business intelligence is the process of delivering information about business operations without any latency. In this context, real time means delivering information in a range from milliseconds to a few seconds after the business event. While traditional business intelligence presents historical information to users for analysis, real time business intelligence ("BI 2.0") 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.

Latency in real time systems
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 analyse 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. Traditional business intelligence and business activity monitoring by comparison only seek to reduce data latency and do 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.

Real Time Business Intelligence Architectures
* 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.

* 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 rage from minutes to hours out of date. The analysis of the data is still usually manual, so the total latency is significantly different from event driven architectural approaches.

* 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 does away with 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.

* Process-aware Real time Business Intelligence

Is sometimes also considered a subset of Operational intelligence and is also identified with Business Activity Monitoring, this 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'.

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
 * Call center optimization