How can you maintain data accuracy in a multi-geography data warehouse?

1 Define a global data model

One of the first steps to maintain data accuracy in a multi-geography data warehouse is to define a global data model that covers the common data elements, attributes, and relationships across all the geographies. A global data model can help you achieve consistency, interoperability, and integration of data from different sources and locations. It can also help you avoid data duplication, redundancy, and conflicts. You can use a data modeling tool or a standard notation, such as the Entity-Relationship Model (ERM) or the Unified Modeling Language (UML), to create and document your global data model.

2 Implement data validation and transformation rules

Another step to maintain data accuracy in a multi-geography data warehouse is to implement data validation and transformation rules that can check, correct, and convert data from different geographies to the global data model. Data validation and transformation rules can help you ensure data completeness, correctness, and compatibility. You can use various techniques and tools, such as data profiling, data cleansing, data mapping, data standardization, and data enrichment, to perform data validation and transformation. You can also use a data integration platform or a data quality framework, such as Apache NiFi or Talend Data Quality, to automate and monitor your data validation and transformation processes.

3 Choose an appropriate data warehouse architecture pattern

A third step to maintain data accuracy in a multi-geography data warehouse is to choose an appropriate data warehouse architecture pattern that can support your data quality goals and requirements. A data warehouse architecture pattern is a design that defines how data is stored, organized, accessed, and processed in a data warehouse. There are different types of data warehouse architecture patterns, such as the centralized, decentralized, federated, hub-and-spoke, and cloud-based patterns. Each pattern has its own advantages and disadvantages in terms of performance, scalability, security, governance, and cost. You should evaluate the pros and cons of each pattern and select the one that best suits your multi-geography data warehouse scenario.

4 Establish data governance and stewardship policies

A fourth step to maintain data accuracy in a multi-geography data warehouse is to establish data governance and stewardship policies that can define and enforce the roles, responsibilities, standards, and procedures for managing and using data across different geographies. Data governance and stewardship policies can help you ensure data accountability, transparency, compliance, and quality. You can use a data governance framework or a tool, such as DAMA-DMBOK or Collibra Data Governance Center, to design and implement your data governance and stewardship policies. You should also involve the relevant stakeholders, such as data owners, data stewards, data consumers, and data regulators, in your data governance and stewardship initiatives.

5 Monitor and measure data quality metrics and indicators

A fifth step to maintain data accuracy in a multi-geography data warehouse is to monitor and measure data quality metrics and indicators that can assess and report the status and trends of data quality across different geographies. Data quality metrics and indicators can help you identify and resolve data quality issues, such as data errors, anomalies, outliers, and inconsistencies. You can use various methods and tools, such as data quality audits, data quality dashboards, data quality scorecards, and data quality alerts, to monitor and measure your data quality metrics and indicators. You should also define and communicate your data quality objectives, targets, and thresholds to your data quality stakeholders.

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?