The data analytics process consists of several key phases that help transform raw data into meaningful insights for decision-making. The first phase is data collection, where relevant data is gathered from various sources such as databases, sensors, or user input. Next comes data cleaning, which involves handling missing values, removing duplicates, and correcting errors to ensure quality. This is followed by data exploration or exploratory data analysis (EDA), where analysts use statistics and visualizations to understand data patterns, relationships, and anomalies.

The fourth phase is data transformation, where data is structured or reformatted for analysis — this may include normalization, encoding, or aggregation. After preparing the data, analysts perform data modeling, applying statistical or machine learning models to uncover trends or make predictions. Finally, the interpretation and reporting phase presents the findings through dashboards, charts, or written reports to guide decision-makers.

Each phase is crucial, and skipping one can reduce the accuracy or impact of the final analysis. Mastering this structured approach ensures reliable outcomes across industries such as finance, healthcare, and retail.

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