Descriptive statistics, such as the average or median, may be generated to help understand the data.

Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.

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In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.

Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results.

A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy.

Data are collected and analyzed to answer questions, test hypotheses or disprove theories.

Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." The CRISP framework used in data mining has similar steps.

Data initially obtained must be processed or organised for analysis.

For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.

Once processed and organised, the data may be incomplete, contain duplicates, or contain errors.