In this video, I explain data transformation.
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Data transformation is a process used in computing and data management where data is converted from one format or structure to another. This process is essential in various data-related tasks, including data integration, data warehousing, data migration, and in the preparation of data for analysis and reporting. Data transformation can involve a range of activities, such as:

Data Cleansing: This involves correcting or removing erroneous, incomplete, or irrelevant data. It's an essential step to ensure the accuracy and quality of the data.

Data Normalization: This process restructures the data to reduce redundancy and improve data integrity. It involves organizing the fields and tables of a database to minimize duplication.

Data Conversion: It includes converting data from one format or data type to another, such as converting text data to numerical data or changing the format of date and time fields.

Data Mapping: This step involves defining how data fields from a source file or database are matched to the fields in the target database or file.

Data Aggregation: This process involves summarizing or combining data from multiple sources or records, often for analysis or reporting purposes.

Data Enrichment: Enhancing existing data by appending related information from external sources. This can include adding demographic information to customer records, for instance.

Data transformation can be performed manually or automated using software tools and scripts. It plays a crucial role in making data more usable and valuable for businesses and organizations, facilitating better decision-making, reporting, and data analysis.

Data Cleansing:

Example: A database contains customer records with some entries having missing postal codes. Data cleansing would involve identifying these records and filling in the correct postal codes, or removing or flagging these incomplete records.


Data Normalization:

Example: In a sales database, customer information is stored in both the 'Orders' and 'Customers' tables, leading to duplication. Normalization would involve restructuring the database so that customer information is stored only in the 'Customers' table and referenced in the 'Orders' table through a customer ID.
Data Conversion:

Example: A dataset contains dates in the format 'MM/DD/YYYY', but the target system requires dates in the format 'YYYY-MM-DD'. Data conversion would involve changing all date entries to the required format.
Data Mapping:

Example: During a migration of customer data from one CRM system to another, fields in the old system like 'FirstName' and 'LastName' might be mapped to 'First_Name' and 'Last_Name' in the new system. Data mapping defines these relationships so that data can be accurately transferred.
Data Aggregation:

Example: An e-commerce company might aggregate sales data from multiple sources (online, in-store, via partners) to calculate total sales per region. This involves summarizing detailed transactional data into a more aggregated form for analysis.
Data Enrichment:

Example: A marketing database contains basic information about prospects. Data enrichment could involve adding social media profiles, interests, and purchasing habits from external data sources to create a more comprehensive view of each prospect.
Each of these examples highlights how data transformation can manipulate and enhance data to make it more useful for specific purposes like analysis, reporting, or system integration.