Better data management with predictive analysis

Data management for business performance is growing area of concern. Businesses generate ever increasing amounts of data. This data is central to the performance of a business. Where it is well used, it can boost revenue and increase efficiency. However, for this to happen, a business must find ways of analyzing the data it generates to derive actionable information.

Predictive analytics of data have emerged to address this concern. They seek to determine patterns and trends within the informational context of business operations through constructive analysis of data. Integration into the database management of a business helps this cause further. Predictive analytics are geared towards innovation within the business context. This is managed through concentrated efforts to understand trends within the business environment and larger market concerns. It can be undertaken in three areas of business operation.

First, it is performed within the context of communication between the business and its customer base. Second, it is also performed within the context of the data accumulated by ongoing activity and concerns. Lastly, it is performed on information generated through research and development operations. The data must be stored in an easy to access format for proper analysis to be performed.

This has been eased through the use of hybrid data storage. It combines flash storage with traditional storage solutions to make data access easier and effective. MapReduce applications have eased the administration of large databases. This is achieved through the use of dispersed system resources for data management.

The MapReduce platform breaks down complex queries from the end user into simple logical tasks. The breakdown of the query into simple tasks is undertaken by a central node computer and is termed mapping. The resultant tasks are then assigned to worker nodes dispersed throughout the network. The worker nodes process the tasks on the central database. The resultant answers from processing are then transmitted along the central node. This node then compiles a final answer, and this is termed the reduce function. The final answer is the one transmitted to the end user terminal.

MapReduce applications have eased predictive analysis by allowing the centralized network resource to undertake parallel and simultaneous processing of data. This system eases the overall difficulty of processing a query within a large database, and results in higher responsiveness, thereby reducing the latency of the system. Any business seeking a competitive edge over competitors should therefore invest in these systems.