Data analytics is the process of studying data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organisations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.
As a term, data analytics principally refers to an assortment of applications, from basic business intelligence, reporting and online analytical processing to various forms of advanced analytics. In that sense, it’s similar in nature to business analytics, another umbrella term for approaches to analysing data — with the difference that the latter is positioned to business uses, while data analytics has a broader focus. The expansive view of the term isn’t universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating Business Intelligence as a separate classification.
Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimise marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals – all with the ultimate goal of boosting business performance. Depending on the particular application, the data that’s analysed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a combination of internal systems and external data sources.
We build our software’s to incorporate data analytics to audit the data for managers in the form of reports.