How do you manage data quality when your data sources change or contain errors?

1 Assess your data sources

Before you load data into your data warehouse, you need to assess the quality and suitability of your data sources. You should identify the types, formats, and frequency of the data, as well as the potential issues or risks that could affect the data quality, such as missing values, duplicates, outliers, or inconsistencies. You should also document the data sources and their characteristics, as well as the business rules and requirements for the data.

2 Implement data quality checks

Once you have assessed your data sources, you need to implement data quality checks at different stages of the data warehouse process. You should perform data quality checks at the source level, the extraction level, the transformation level, and the loading level. Data quality checks can include data profiling, data cleansing, data validation, data reconciliation, and data auditing. You should also use tools and methods to automate and monitor the data quality checks, such as ETL tools, data quality software, or data quality dashboards.

3 Handle data changes and errors

Even if you have implemented data quality checks, you may still encounter data changes and errors that could affect your data warehouse. For example, your data sources may change their structure, format, or frequency, or they may contain errors or anomalies that are not detected by the data quality checks. To handle these situations, you need to have a strategy and a process to identify, notify, and resolve the data changes and errors. You should also have a backup and recovery plan to restore the data warehouse in case of major data issues.

4 Establish data governance and standards

Data quality is not only a technical issue, but also a business and organizational issue. Therefore, you need to establish data governance and standards to ensure that the data warehouse meets the needs and expectations of the stakeholders and users. Data governance and standards can include defining the roles and responsibilities of the data owners, custodians, and users, setting the data quality goals and metrics, creating the data quality policies and procedures, and enforcing the data quality rules and controls.