Data analytics is the science of analyzing raw data in order to draw conclusions about the information they contain, to uncover patterns, and extract valuable insights from it. Many data analytics techniques and processes have been automated using computer algorithms to prepare raw data for human consumption.

Depending on the workflow stage and data analysis requirements, there are four main kinds of analytics – Descriptive, Diagnostic, Predictive and Prescriptive.

Technically, data analysis is a process of cleansing, prepping, transforming, modeling and processing data with the goal of discovering meaningful information, making informed conclusions and supporting decision-making.

Data analytics helps enterprises optimize their business or operational performance.

Data analytics requirements vary by business and industry. Examples of common types of analytics include customer analytics, marketing and sales analytics, supply chain analytics, product and IoT analytics, procurement and spend analytics.

Data Scientists and Data Analysts use data analytics techniques in their research, and businesses use it to help identify data correlations and make more informed decisions. Data analysis helps enterprises better understand their customers, evaluate their marketing campaigns, personalize content, create customer loyalty strategies and develop better products.

Business analytics tools are types of application software that retrieve data from one or more business systems and combine it in a repository, such as a big data lake or data warehouse, to be retrieved, reviewed and analyzed on demand.

Great tools also encompass management of data from edge-to-core-to-multicloud to drive business processes and improve business outcomes with more effective decision making and enhanced customer experiences.