Detailed data volume analysis is known as data analytics.

In order to be able to analyse large data volumes, known as Big Data, it is necessary first of all to define data extraction, indexing and storage.

Fundamentally, data may have been generated from a variety of sources, have different data formats, or even consist of real-time data. Even today, companies often do not realise how valuable generated data is. Data analytics assesses precisely this value. Data analytics gives you the opportunity to generate optimisation potentials using an improved insight into the infrastructures. In this way, facts can underpin decisions. This means: Hidden patterns or correlations are detected and can be used to optimise corporate divisions and business processes.

Its maturity

The medium-term objective in using data analytics is to develop from a reactive into a proactive organisation. Reactive use of data embraces raw data extraction, data refining and data mining, in other words the systematic application of methods to recognise different patterns and trends in the data sets and then display these. By means of the methods implemented, companies receive a classification and segmentation of their data which helps create future prediction of various scenarios, amongst other uses. The actual data analytics task begins after successful data mining. With the aid of software solutions, the analysed data can now help process future scenarios in an automated manner, or incorporate potential external factors before these arise in the fields of Operational Intelligence and Automation.

With Controlware, we concern ourselves with solutions in the fields of:

• Operational Intelligence
• Log Monitoring
• Business Performance Monitoring
• Service Monitoring
• Reporting and Visualisation