Descriptive Analytics

Descriptive analytics is a field of statistics that focuses on gathering and summarizing raw data to be easily interpreted. Generally, descriptive analytics concentrate on historical data, providing the context that is vital for understanding information and numbers. The field is used across a variety of industries and needs, and can cover a diverse range of purposes, from inventory tracking to benchmarking yearly revenues and sales.

What are the outcomes of descriptive analytics?

Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes. The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes.)

What is the difference between descriptive and predictive analytics?

Descriptive Analytics uses Data Aggregation and Data Mining techniques to give you a knowledge about past but Predictive Analytics uses Statistical analysis and Forecast techniques to know the future. In a Predictive model, it identifies patterns found in past and transactional data to find risks and future outcomes. See the full article for more details.

What is descriptive modeling?

Descriptive modeling is a mathematical process that describes real-world events and the relationships between factors responsible for them. The process is used by consumer-driven organizations to help them target their marketing and advertising efforts.

What is descriptive data?

Descriptive statistics are numbers that are used to summarize and describe data. The word “data” refers to the information that has been collected from an experiment, a survey, a historical record, etc.