Deep analytics is the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets that may contain not only structured data but also unstructured and semi-structured data.
Deep analysis involves precisely targeted and sometimes complex queries on data sets that may be measured in petabytes and exabytes, often with requirements for real-time or near-real-time responses.
Because real-time analysis of such large data sets can require distribution of the workload over hundreds or even thousands of computers, deep analytics is often associated with cloud computing. The workload distribution may be managed through some framework such as MapReduce. Approaches to speeding up the process of querying data include the use of columnar databases and in-memory analytics.
The financial sector, the scientific community and the pharmaceutical and biomedical industries have conducted deep analytics for some years. In recent years, the practice has become increasingly common within the enterprise, as the amount of corporate data produced has increased -- and with it, the desire to extract business value from that data.
The voluminous amount of data produced within an organization is sometimes referred to as big data; deep analytics is sometimes referred to as big data analytics.
See also: IBM’s Watson supercomputer, unstructured data, text mining, business intelligence/business analytics (BI/BA), Enterprise search, enterprise content management (ECM)