Data analytics is the practice of systematically examining data to draw meaningful insights, make predictions, and support decision-making. It involves various steps, such as:

1. Data Collection: Gathering data from different sources, including databases, surveys, web scraping, or sensors, depending on the business needs.

2. Data Cleaning: Ensuring that the data is accurate, complete, and free from errors. This step may involve removing duplicates, handling missing values, or correcting inconsistencies.

3. Data Analysis: Using statistical tools and techniques to explore data, identify trends, correlations, and patterns, and make sense of large amounts of raw information. Tools like Excel, SQL, Python, R, or specialized data analysis software are commonly used.

4. Data Visualization: Presenting the analyzed data in easy-to-understand formats like graphs, charts, and dashboards, which help stakeholders interpret the results and make informed decisions.

5. Predictive Analytics: Using historical data and statistical algorithms to make predictions about future outcomes. This is commonly used for forecasting sales, trends, or customer behavior.

Data analytics can be broken into several types, such as:

• Descriptive Analytics: Focuses on understanding past data to summarize what has happened.
• Diagnostic Analytics: Helps understand the reasons behind past trends and events.
• Predictive Analytics: Uses historical data to predict future outcomes and trends.
• Prescriptive Analytics: Provides recommendations for actions based on data analysis.

It is applied across various industries like healthcare (predicting patient outcomes), marketing (targeting ads), finance (fraud detection), and operations (optimizing supply chains). By leveraging data analytics, businesses can improve their performance, minimize risks, and enhance customer experiences.

Data analytics is crucial for turning raw data into actionable insights, and it plays a key role in driving growth and innovation in today's data-driven world.