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Data Analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.[1] Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.
The difference between data analysis and data analytics is that data analytics is a broader term in which data analysis forms a subcomponent. Data analysis refers to the process of compiling and analyzing data to support decision-making, whereas data analytics also includes the tools and techniques used to do so.
These steps and many others fall into three stages of the data analysis process:
* evaluate
* clean
* summarize.
