We gather relevant data from various sources, such as databases, APIs, spreadsheets, or external sources. We deal with all forms of data including structured, semi-structured, or unstructured data formats.

Raw data often contains errors, inconsistencies, missing values, or duplicates. Data cleaning involves identifying and rectifying these issues to ensure data integrity and accuracy.

Collected data is transformed into a suitable format for analysis. This may include data normalization, aggregation, or filtering to enhance its quality and usability.

EDA involves conducting preliminary analyses to understand the data's characteristics, patterns, and relationships. Techniques like data visualization, statistical summaries, and correlation analysis aid in uncovering insights.

Once the data is explored, the next step is to develop a statistical or machine learning model. This involves selecting an appropriate algorithm, training the model using historical data, and evaluating its performance.

After the model is built, the obtained results need to be interpreted to extract meaningful insights. This involves analyzing the model's predictions, identifying trends, and drawing conclusions that align with the business objectives.

The insights derived from the data analysis process are used to support decision-making. This step involves presenting the findings to stakeholders, recommending actions based on the analysis, and considering potential risks and benefits.

Data analytics is an iterative process, and continuous monitoring is essential to evaluate the effectiveness of the decisions made. Monitoring allows for refining models, updating data sources, and incorporating new insights to improve future analyses.