While i would implement strategies to standardise data formats, measure data quality; i would also want to implement defence at border. Meaning; the inconsistencies that we capture while measuring data quality (exceptions) will be forced back to source systems with an automated workflow, and track for fix to re-ingest. This is the best way to fix the DQ issues and increase the confidence or trust on data. Avoid fixing DQ issues at a consumer level as it will introduce inconsistent versions of data when consumed from different systems.

Perform attribute analysis while analyzing the data values of each attribute concerning uniqueness, distribution, completeness Replace missing/null values, rectify incorrect ones,convert data sets into a common format Conduct data cleansing, deduplication to identify and remove duplicates The option,"Append Rows" is used when data is present in different databases Append Columns is a suitable approach when a company wants to add new elements to its existing data set In case of incomplete or missing records that need filling by looking up values from another database, follow "Conditional Merge" Conduct a final audit of data once the merging process is complete Challenges of data merging: Data complexity Scalability Duplication

Manter uma plataforma governada, com taxonomia padrão e indicadores de qualidade desde a aquisição, até a entrega do produto final, torna o ciclo de vida dos dados claro e mais simples de acompanhar, deixando o processo robusto e limpo.

My suggested steps would be as follows: 1. Load data from various sources as-is, no changes / no checks whatsoever. 2. Check the data for schema validation, and make sure they come together else raise a failure and pause the job 3. Keep a track by doing profiling of the data and keep a range in which data values, and data statistics should stay. if these are off then raise caution 4. Ideally map it to an ontology so there is consistency across sources before bringing them together. 5. When merging try to see for which data would take precedence when data is different and raise reconciliation issues. 6. Monitor quality at all levels using dashboards which would allow respective teams to identify and resolve specific challenges.

To ensure Top notch data quality while merging multiple datasets, - I firstly assess and profile the data. By doing so we can understand the structure, quality and data types of the datasets as well as understand if there is really need to merge the datasets. - Secondly standardise the formats, header and units. - Clean the datasets by having checks in place to handle Duplicates, Missing Values. - Use appropriate Joins, Aggregations or Unions depending on the requirements. - Use tools and leverage ETL platforms for automation. - Put in place data governance policies. - Test and Validate the final dataset and reiterate for improvement. - Implement automated processes to detect and resolve quality issues.

-Selecting a trusted source - setting up a Match and Merge model - cleansing the data to a standard format - setup threshold value for auto merge / manual reviews - user training should be conducted to correct data for manual reviews - data validation and regular audits will maintain the data quality

Attribute Mapping: Map fields from different sources to a common schema. This is essential when datasets have overlapping but not identical fields. Data Enrichment: If possible, enrich the data with additional fields from authoritative sources, enhancing the dataset’s overall quality. Uniform Formats and Units: Standardize data types, formats (e.g., date formats, numerical precision), and units across datasets. This prevents mismatches and misinterpretations during the merging process. Naming Conventions and Labeling: Ensure consistent naming conventions, so fields with the same meaning have the same name and structure. Identify Data Quality Issues: Look for missing values, duplicates, inconsistencies, and outliers in each dataset.

The cornerstone of data quality lies in the effective management and governance of source data. Ensuring data integrity at this initial stage is paramount to driving downstream consumption. In the business context, where data is often sourced from multiple systems, a robust data catalog and semantic layer become indispensable. The technical aspects of data governance and homogenization are managed within the integration layer. This encompasses attribute validations, rationalization of missing data, and a workflow for data correction in the source system of record. By implementing these measures, we can achieve a unified and simplified data platform that enhances usability, drives quality, and fosters trust among business consumers.