Steps for data preparation are:

• Gather data: The data preparation process starts with obtaining the correct data. This can originate from a current data catalogue or can be appended ad-hoc.
• Discover and assess data: After assembling the data, it is essential for each dataset to be identified. This step is about learning to understand the data and knowing what MUST be done before the data becomes valuable in a distinct context. Discovery is a big task but can be done with the help of data visualization tools that assist users and help them browse their data.
• Clean and verify data:
  Even though cleaning and verifying data takes a lot of time, it is the most important step, because this step not only eliminates the incorrect data but also fills the rifts. Significant tasks here include:
  • ELIMINATING alien data and outliers.
  • Filling in missing values.
  • Adjusting data to a regulated pattern.
  • Masking private or sensitive data entries.
    After cleaning the data, the mistakes that we came across during the data development process have to be examined and approved. Generally, an error in the system will become apparent during this step and need to be fixed before proceeding.
• Transform and enrich data:
  Transforming data modernizes the arrangement or value entries to reach a well-defined result or make the data more quickly recognized by broader viewers. Improving data refers to adding and CONNECTING data with other SIMILAR INFORMATION to provide deeper insights.
• Store data:
  Lastly, the data can be collected or channeled into a third-party application like a business intelligence tool-making technique for processing and analysis.

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