Data analytics typically includes four interconnected types: Descriptive, Diagnostic, Predictive, and Prescriptive analytics, each answering a key business question. Descriptive analytics explains what happened using reports, dashboards, KPIs, and visualizations. Diagnostic analytics explores why it happened through root cause analysis, data mining, regression, and anomaly detection. Predictive analytics focuses on what might happen next by applying machine learning, classification, and time-series models to historical data. Prescriptive analytics recommends what actions to take using optimization models, simulations, decision trees, and AI. Additional approaches include cognitive analytics for unstructured data, qualitative and quantitative analysis, clustering, and hypothesis testing. These concepts are practically covered in Data analytics training at H2K Infosys, helping learners build job-ready, industry-relevant analytical skills.
The four types of data analytics
-
Descriptive Analytics: Analyzes historical data to understand what has happened in the past. It provides insights through reports and visualizations.
-
Diagnostic Analytics: Identifies the cause of past events by analyzing historical data, helping to understand why something occurred.
-
Predictive Analytics: Uses statistical models and machine learning to forecast future outcomes based on historical data and trends.
-
Prescriptive Analytics: Recommends actions based on data analysis, suggesting possible outcomes and optimizing decisions to achieve the best result.
These analytics types help organizations make informed decisions.
If you are planning to learn Data Analytics and looking for the Best Best Data Analytics Training then, my suggestion is to contact Uncodemy. They are one of the best Data Analytics Training institute .
The four types of data analytics are descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics helps explain past trends, while diagnostic analytics identifies the causes behind those trends. Predictive analytics uses data to forecast future outcomes, and prescriptive analytics recommends the best actions to take.
The four types of data analytics are descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics summarizes past data to show "what happened," while diagnostic analytics explains "why it happened." Predictive analytics forecasts future outcomes using historical data, and prescriptive analytics suggests actions based on insights and optimization. These types help businesses turn data into actionable insights.
The four types of data analytics are:
-
Descriptive Analytics
-
Purpose: Answers “What happened?”
-
Example: Monthly sales reports, website traffic summaries.
-
Use: Summarizes past data to identify trends and patterns.
-
-
Diagnostic Analytics
-
Purpose: Answers “Why did it happen?”
-
Example: Investigating a sudden drop in website visits.
-
Use: Digs deeper into data to find causes or correlations using techniques like drill-down, data discovery, or data mining.
-
-
Predictive Analytics
-
Purpose: Answers “What is likely to happen?”
-
Example: Forecasting next month’s sales or predicting customer churn.
-
Use: Uses statistical models and machine learning to forecast future outcomes based on historical data.
-
-
Prescriptive Analytics
-
Purpose: Answers “What should we do?”
-
Example: Recommending the best pricing strategy to maximize profit.
-
Use: Suggests actions or decisions by evaluating different scenarios using optimization and simulation.
-
Each type builds on the previous one, progressing from understanding the past to guiding future decisions. Thank you so much for sharing this information.
The four types of data analytics help understand past data, find reasons for results, predict future outcomes, and support better decision-making.
Descriptive Analytics: Descriptive analytics explains what happened in the past. It uses simple reports, charts, and numbers to show results clearly. For example, it shows last month’s sales or website visits.
Diagnostic Analytics: Diagnostic analytics explains why something happened. It looks deeper into the data to find the reasons behind a result. For example, it helps understand why sales increased or decreased.
Predictive Analytics: Predictive analytics tells what is likely to happen in the future. It uses past data and patterns to make predictions. For example, it can predict future sales or customer demand.
Prescriptive Analytics: Prescriptive analytics suggests what action should be taken. It helps choose the best decision based on data. For example, it can recommend the best price or marketing strategy.