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 a strong expertise in statistics, are called data scientists.
No, data analytics is a general term for any type of processing that looks at historical data over time, but as the size of organizational data grows, the term data analytics is evolving to favor big data-capable systems.
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.
Today the field of data analytics is growing quickly, driven by intense market demand for systems that tolerate the intense requirements of big data, as well as people who have the skills needed for manipulating data queries and translating results.