How do you ensure data consistency across platforms?

1 Define a common data model

One of the first steps to ensure data consistency across platforms is to define a common data model for your data warehouse. A data model is a representation of how the data is structured, organized, and related in the warehouse. It should be consistent with the business requirements, objectives, and rules of the data warehouse project. A common data model helps to avoid data conflicts, duplication, and ambiguity, as well as to facilitate data integration, transformation, and validation.

2 Implement data quality checks

Another step to ensure data consistency across platforms is to implement data quality checks throughout the data warehouse lifecycle. Data quality checks are processes or rules that verify, correct, or reject the data based on predefined criteria. They can be applied at different stages, such as data extraction, loading, transformation, or reporting. Data quality checks help to identify and resolve data errors, inconsistencies, or anomalies, as well as to ensure data completeness, accuracy, and validity.

3 Use standard data formats and codes

A third step to ensure data consistency across platforms is to use standard data formats and codes for your data warehouse. Data formats and codes are the ways of representing and encoding the data in the warehouse. They should be consistent with the common data model and the industry or domain standards. Using standard data formats and codes helps to ensure data interoperability, compatibility, and readability across platforms, as well as to reduce data conversion and manipulation costs.

4 Document data definitions and metadata

A fourth step to ensure data consistency across platforms is to document data definitions and metadata for your data warehouse. Data definitions and metadata are the information that describes the meaning, context, and characteristics of the data in the warehouse. They should be clear, concise, and consistent across platforms, sources, and systems. Documenting data definitions and metadata helps to ensure data understanding, communication, and governance across platforms, as well as to support data analysis and reporting.

5 Monitor and audit data changes

A fifth step to ensure data consistency across platforms is to monitor and audit data changes for your data warehouse. Data changes are the modifications or updates that occur to the data in the warehouse due to business or technical reasons. They should be tracked, recorded, and verified across platforms, sources, and systems. Monitoring and auditing data changes helps to ensure data integrity, traceability, and accountability across platforms, as well as to detect and prevent data issues or breaches.

6 Review and update data policies and procedures

A sixth step to ensure data consistency across platforms is to review and update data policies and procedures for your data warehouse. Data policies and procedures are the guidelines and rules that govern the data management and usage in the warehouse. They should be aligned with the business goals, regulations, and best practices of the data warehouse project. Reviewing and updating data policies and procedures helps to ensure data compliance, security, and quality across platforms, as well as to foster data culture and awareness.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?