What are the best ways to normalize data during the ETL process?

Normalizing data during the ETL (Extract, Transform, Load) process involves organizing it to reduce redundancy and improve efficiency. Utilize database normalization techniques like breaking data into smaller tables, minimizing duplicate information, and establishing relationships between tables using foreign keys. Standardize data formats and ensure consistent naming conventions. Employ data validation and cleansing procedures to enhance accuracy. Normalize hierarchical data structures with appropriate keys and indices. Regularly update and maintain the normalized structure to adapt to evolving business requirements. These practices enhance data integrity, optimize storage, and streamline analytics in the ETL pipeline.

Standardize formats for key attributes like names, addresses, dates. This helps ensure data consistency post-load. For example, convert all date formats to YYYY-MM-DD. Clean and validate data by checking for completeness, duplicate records, inconsistencies, etc. Fixing issues early avoids quality problems later. Normalize numeric and string values to remove special characters. For numeric measures, you may normalize units as well (e.g. convert everything to base units). Break down concatenated fields like full names and addresses into atomic units and normalize each element separately. This aids in better searchability. Create lookup tables and master data files for standard attribute values that can be reused across systems.

I find that there is a surprising number of BI analysts out in the wild lack experience with normalization, as they're often provided with read-only dataset access. I aim for third normal form when creating data models. Joining tables as needed allows me to create only necessary data and minimizes inconsistency and duplication. I ensure correct storage of data types, including dates in DateTime fields and decimal values in decimal fields, which saves time and avoids casting errors and additional processing.

Normalize unstructured data fields by applying structure and consistency. For example, parse and categorize free-text survey responses with sentiment analysis or entity extraction tools. Apply business rules and validation checks catered to the domain or subject area. Use functional dependencies between attributes to test for data accuracy. Filter out unused legacy attributes or columns not needed upstream. Apply horizontal partitioning to split wide tables into manageable units grouped by context.

Aggregate transactional data to higher levels if the analytics need is for aggregate reporting. Similarly, explode summarized records to lower granularity if needed. Generalize classifications by rolling up low-frequency values into higher-level categories. For example, group retail brands by major parent company. Enforce integrity constraints early to limit insertion of duplicate or incorrect data. Constraints also help optimize join and aggregation performance later. The core goals remain making data consistent, accurate and structural while also shaping it to meet the analytical use case requirements downstream.

To tackle data quality issues in BI, I'd implement ETL data cleansing strategies: replacing missing values with defaults or averages, removing/merging duplicates, and validating data against set rules. This includes automating cleansing processes for efficiency and accuracy, and continuously monitoring and refining data quality post-cleansing for reliable BI insights.

Ensure conformance to organizational data standards or glossaries. Align attributes and definitions to existing enterprise metadata frameworks where feasible. Normalize unstructured data fields by applying structure and consistency. For example, parse and categorize free-text survey responses with sentiment analysis or entity extraction tools. Apply business rules and validation checks catered to the domain or subject area. Use functional dependencies between attributes to test for data accuracy.

Define rules for handling nulls, invalid formats, duplicates etc. that get uniformly applied across the pipeline. Add metadata and technical keys to manage data lineage and integrate records between systems. Paying attention to normalization early on results in cleaner data post-load that requires less downstream efforts for data quality rework and maintenance. It enables easier reporting and analysis as well.

In addition to the fundamentals, ensure robust error handling, leveraging data profiling for insights, and embracing metadata for documentation and collaboration. Design the ETL process for scalability, accommodating growing data volumes, and prioritize continuous monitoring and performance tuning to adapt to evolving data patterns. These considerations enhance the efficiency and adaptability of data normalization.