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Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. It is also used by scientists and researchers to verify or disprove scientific models, theories, and hypotheses.

As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting, and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar to business analytics, another umbrella term for approaches to analyzing data. The difference is that the latter is oriented 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 BI as a separate category.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts. It can also be used to respond quickly to emerging market trends and gain a competitive edge over rivals. The ultimate goal of data analytics, however, is boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics. In addition, it can come from a mix of internal systems and external data sources.

Data analytics vs. data science

As automation grows, data scientists will focus more on business needs, strategic oversight, and deep learning. Data analysts who work in business intelligence will focus more on model creation and other routine tasks. In general, data scientists concentrate efforts on producing broad insights, while data analysts focus on answering specific questions. In terms of technical skills, future data scientists will need to focus more on the machine learning operations process, also called MLOps.

4 Ways to Use Data Analytics

  • Improved Decision Making
  • More Effective Marketing
  • Better Customer Service
  • More Efficient Operations

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