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In order to understand enterprise customers better and make better and quicker business decisions, in-memory analytics is implemented especially on CRM (Customer Relations Management) settings.
By technical definition, real-time analytics is the use or the capacity to use all available enterprise data and resources when needed. It normally consists of dynamic analysis and reporting based on data entered into a system less than one minute before the actual time of use.
An example of real-time analytics is ActivePivot which is a robust tool and powerful object-based Online Analytical Processing (OLAP) tool that provides real time analytics capabilities through its transactional engine and multi-threaded processing capabilities.
Real-time push technology which powers most real-time analytics tools allow for timely decision making as based above since the aggregated data from simultaneous multiple sources enables fast navigation and slice and dice filtering.
Most real time analytics tools also allow queries to run continuously in order to generate real-time alerts on Key Performance Indicators (KPIs) set by the tools or the companies themselves. As a result, faster and better decisions are made using complex business logic.
In a CRM model, real-time analytics can support instant refreshes to corporate dashboards to reflect business. In a data warehouse setting, real-time analytics supports unpredictable, ad hoc queries against large data sets. In scientific analysis usage, it can be used to track a hurricane’s path, intensity and wind field hours in advance.
Real-time analytics is also known as real-time data analytics, real-time data integration, and real-time intelligence.