Data analytics
Data analytics
Data analytics definition
Data analytics is the process of analyzing data (e.g., social media, sales figures, web traffic) to get insights, identify trends, and support decision-making. Data analysts use many techniques and tools to uncover patterns, trends, correlations, and other valuable information within large data sets.
Data analytics can be carried out by various professionals, including data analysts, data scientists, business analysts, market researchers, and economists. It is used across most industries to help inform decisions and understand specific trends.
See also: data-driven
Key types of data analytics
- Descriptive analytics looks at past events and trends to understand what happened.
- Diagnostic analytics explores the reasons for past events.
- Predictive analytics focuses on predicting future events and trends.
- Prescriptive analytics recommends the best actions.
Data analytics examples
- In retail, data specialists may analyze customer purchase history to identify popular products and trends. They may also use predictive analytics to forecast demand and personalize marketing campaigns based on customer preferences and browsing behavior.
- In the financial sector, data analytics helps detect fraudulent transactions by analyzing spending patterns. Data analysts may also use historical market data to predict stock prices — or analyze customer credit history to decide whether to lend them money.
- In healthcare, data analytics may help identify how likely someone is to develop a certain disease by looking at patient medical records. Additionally, data analysis may help optimize hospital resources based on patient admission rates.
- Marketing heavily relies on data analytics. Marketers may use data in various ways — from reviewing social media data to understand customer reactions to segmenting customers based on demographics. They may also conduct A/B testing on different marketing strategies to find out what’s most effective.