Data Transformation

Data Cleaning: Ensure standardization of extracted data, addressing issues like missing values, duplicates, and inconsistencies to guarantee data consistency and accuracy.

Data Integration: Aligning data formats, resolving schema differences, and consolidating diverse data sources, skillfully integrating the data into a cohesive format, facilitating a unified analysis framework.

Data Enrichment: Enhancing value of extracted data by adding critical information or context which includes deriving metrics, implementing business rules, or incorporating insights from external data sources, enriching the data’s analytical potential.

Data Aggregation: Perform data aggregation and summarization, essential for insightful analysis involving processes like computing key metrics, grouping data by relevant time periods, or summarizing data at various levels of detail to support decision-making processes.