Data Validation

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Ensuring data accuracy by verifying values against known parameters or external sources. For example, checking customer email addresses for validity.
* **Data Transformation:** Converting data into a usable format. This could involve standardizing units, converting currencies, or changing data types.
* **Data Imputation:** Filling in missing values using appropriate methods, like using averages or medians.
* **Data Reduction:** Simplifying complex data into more manageable forms, such as aggregating data or creating summary statistics.

A significant challenge in this phase was dealing with inconsistent data entry. For example, different brother cell phone list customer service agents might use different abbreviations or phrases to describe customer issues. We addressed this by developing a standardized taxonomy for categorizing customer complaints.

**Phase 4: Data Structuring and Storage**

Once the data was cleaned, we structured it into a relational database. This involved creating tables, defining relationships between data points, and ensuring data integrity. A well-structured database is essential for efficient data retrieval and analysis.