What are the 4 types of data analysis?

The four main types of data analysis are:

1. Descriptive Analysis

Descriptive analysis is the most basic form of data analysis. It focuses on summarizing and describing the main features of a dataset, providing an overview of what has happened historically. The goal is to understand patterns and trends from the past.

Key Techniques:

• Summary Statistics: Measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation).
• Data Visualization: Creating charts, graphs, and dashboards to present the data (e.g., histograms, bar charts, pie charts).

Example:

• Use Case: A company might use descriptive analysis to understand how sales have fluctuated over the past year, providing a simple summary like "sales increased by 10% in Q2."

2. Diagnostic Analysis

Diagnostic analysis goes a step beyond descriptive analysis by investigating the reasons behind past performance. It aims to answer "why" something happened by identifying causes and correlations in the data.

Key Techniques:

• Drill-Down Analysis: Breaking down data into finer details to find the root cause of an issue.
• Correlation and Regression Analysis: Identifying relationships between different variables.

Example:

• Use Case: If sales dropped in Q3, diagnostic analysis could help find the cause, such as seasonal trends, changes in customer behavior, or issues with product availability.

3. Predictive Analysis

Predictive analysis uses historical data and statistical models to make predictions about future events. It aims to answer "what is likely to happen" by identifying trends and making forecasts based on data patterns.

Key Techniques:

• Machine Learning Algorithms: Such as regression, decision trees, and time-series forecasting models.
• Predictive Modeling: Creating models that forecast future outcomes based on historical data.

Example:

• Use Case: A company might use predictive analysis to forecast future sales based on historical trends, allowing them to adjust inventory levels or marketing strategies accordingly.

4. Prescriptive Analysis

Prescriptive analysis recommends specific actions or strategies to achieve desired outcomes. It aims to answer "what should we do" by analyzing data and providing decision-making support. This type of analysis often incorporates optimization and simulation models.

Key Techniques:

• Optimization Models: Mathematical techniques used to find the best course of action (e.g., maximizing profit or minimizing costs).
• Scenario Analysis: Simulating different scenarios to determine the potential impact of various decisions.

Example:

• Use Case: A logistics company might use prescriptive analysis to optimize delivery routes, reducing fuel costs and delivery times by recommending the most efficient routes based on current traffic conditions.

Summary of the Four Types of Data Analysis:

• Descriptive Analysis: Focuses on understanding what has happened in the past.
• Diagnostic Analysis: Focuses on understanding why something happened.
• Predictive Analysis: Focuses on forecasting what is likely to happen in the future.
• Prescriptive Analysis: Focuses on providing recommendations for what actions should be taken.

Each type of analysis serves a different purpose, and organizations often use all four in combination to gain a comprehensive understanding of their data and make informed decisions.