3. Data Profiling: Perform data profiling to assess the quality and completeness of the existing data. Identify any inconsistencies, anomalies, or missing data that may impact reporting accuracy.
4. Data Documentation: Create comprehensive documentation of the data sources, including data dictionaries, metadata, and data lineage. This documentation will serve as a reference for both technical and non-technical stakeholders.
5. Data Validation: Implement data validation checks to ensure data accuracy and reliability. This involves verifying data against predefined business rules and validation criteria.
6. Data Transformation and Cleansing: If necessary, design and execute data transformation and cleansing processes to ensure that data is in the required format and free of errors.
7. Data Modeling: If data modeling is required, design appropriate data models (e.g., dimensional models for data warehousing) to structure the data in a way that aligns with reporting needs.
8. Data Integration: Ensure that data from various sources is integrated effectively, allowing for a holistic view of the business. Data integration may involve ETL (Extract, Transform, Load) processes.