Descriptive analytics

From Canonica AI

Overview

Descriptive analytics, a subset of business intelligence, involves the use of historical data to understand changes that have occurred in a business over time. It is the examination of data and statistics to gain insight into the past. This form of analytics is the most common method that businesses leverage to make primary and immediate business decisions.

Definition

Descriptive analytics is a statistical method that involves the collection, classification, summarization, and interpretation of historical data to identify patterns and trends. It is the simplest class of analytics, one that allows businesses to condense big data into smaller, more useful nuggets of information.

Purpose

The primary purpose of descriptive analytics is to form a clear understanding of past behaviors and how they might influence future outcomes. The main goal is to accurately summarize the total dataset in a clear and understandable way, which can then be used to create actionable insights.

A group of analysts examining data on computer screens.
A group of analysts examining data on computer screens.

Methodology

Descriptive analytics involves various forms of data aggregation and mining. These processes can include simple tabulation and queries, descriptive statistics to identify patterns and relationships among various data, and more complex statistical analyses.

Applications

Descriptive analytics can be applied in various sectors such as finance, healthcare, telecommunications, retail, and more. It is used to track key performance indicators (KPIs), sales trends, customer behavior, and operational performance.

Limitations

While descriptive analytics can provide valuable insights into the past, it has its limitations. It does not provide reasons why a particular event occurred, nor can it predict future events or outcomes.

Future of Descriptive Analytics

With the rise of big data, the role of descriptive analytics in business is becoming more significant. As the volume of data continues to increase, the demand for tools and techniques to make sense of this data is also growing.

See Also