{"id":19862,"date":"2020-01-28T14:47:17","date_gmt":"2020-01-28T14:47:17","guid":{"rendered":"https:\/\/www.arimetrics.com\/glosario-digital\/descriptive-analytics"},"modified":"2026-09-15T10:00:47","modified_gmt":"2026-09-15T10:00:47","slug":"descriptive-analytics","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/descriptive-analytics","title":{"rendered":"Descriptive Analytics"},"content":{"rendered":"

\"DescriptiveDefinition:<\/strong><\/p>\n

Descriptive analytics<\/strong> is the set of techniques used to summarize and present observed data. It helps answer questions about what happened, how much, when, where, and how often within a defined period and scope.<\/p>\n

Its results may be expressed through counts, percentages, averages, distributions, changes, tables, and charts. It describes the available information, but it does not automatically identify causes or predict what will happen next.<\/strong><\/p>\n

It can be applied to historical or recent data. The distinction does not depend on the age of the data, but on the question: showing yesterday’s sales and summarizing visits from the last five minutes are both descriptive uses when they are limited to representing what was observed.<\/p>\n\n

How Descriptive Analytics Is Performed<\/h2>\n

The analysis begins with a specific question and a shared definition of the metrics. A summary can only be interpreted when its population, period, and calculation rules are known.<\/strong><\/p>\n

A common process includes these five stages:<\/p>\n

    \n
  1. Define the objective:<\/strong> establish which phenomenon is to be described and which decision or monitoring activity the result will support.<\/li>\n
  2. Define the data scope:<\/strong> specify sources, population, period, unit of analysis, segments, and inclusion or exclusion criteria.<\/li>\n
  3. Check quality:<\/strong> review missing values, duplicates, instrumentation errors, definition changes, and possible coverage biases.<\/li>\n
  4. Calculate summaries:<\/strong> obtain counts, proportions, measures of central tendency, dispersion, changes, or distributions suited to the question.<\/li>\n
  5. Present and interpret:<\/strong> use tables, charts, or a dashboard<\/a> and document the context and limitations needed to read them.<\/li>\n<\/ol>\n

    The mean does not always represent a dataset well. The median, percentiles, and distribution may be more informative<\/strong> when there are extreme values or groups with different behaviors. Likewise, a percentage change needs its reference value and absolute volume to avoid disproportionate interpretations.<\/p>\n

    Comparison also requires equivalent bases. Changing the time window, the definition of a user, or the way an event is recorded can produce an apparent difference that does not correspond to a real change in the phenomenon.<\/p>\n

    Applications of Descriptive Analytics<\/h2>\n

    Descriptive analytics is used in operations, finance, marketing, product, and customer service. Its purpose is to turn scattered observations into an understandable representation of the state and development of an activity.<\/strong><\/p>\n

    Common applications include:<\/p>\n