Best practices for data quality in data warehouses

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process.

The true measure of an effective data warehouse is how much key business stakeholders trust the data that is stored within. To achieve certain levels of data trustworthiness, data quality strategies must be planned and executed.

It’s clear that data quality ultimately determines the usefulness and value of a data warehouse. But achieving high-quality data is no small task, especially in larger enterprises. This guide offers best practices for any data professional or leader who wants to learn how to optimize data quality in their organization’s data warehouses.

Data quality is a crucial part of data governance that guarantees organizational data is fit for purpose. It is the metric that measures usability when it comes to processing and analyzing a dataset for other uses. Data quality dimensions include consistency, completeness, conformity, integrity and accuracy.

A data warehouse is a large store of data amassed from a vast range of company sources; it is mainly used for decision support. A data warehouse is a non-operational system that merges data from operational systems and delivers optimized data for users. This type of data storage solution can deliver a single source of truth to an organization.

To ensure that trustworthy data is available, organizations should implement frameworks that capture and streamline data quality issues automatically. Both data cleansing and data profiling can be helpful at this point in the process.

Since data cleansing involves analyzing the quality of data in a data source to determine whether or not to make changes, data cleansing should happen early in the data integration process to flag data issues. Data profiling should also be a part of these frameworks because it is a pillar of building confidence in data. It helps organizations understand their business needs further and assess the quality of their data to uncover any gaps.

Data cleansing and data profiling should work hand in hand to ensure that flaws revealed during profiling are addressed during the data cleansing process. These data quality frameworks may require an upfront investment. Despite the potential costs, organizations should assess and consider making the investment based on the expected long-term benefits to the data warehouse.

Proactive measures do not guarantee safety from bad data. When bad data bypasses proactive measures and is reported by business users, such bad data needs to be investigated to ensure that user confidence is maintained. These investigations need to be prioritized.

Failure to investigate data quality shortcomings in a data warehouse will lead companies to deal with recurrent errors. Continuously correcting these kinds of data errors can be complex and time-consuming in the long run. Therefore, organizations should seek to identify errors and prevent similar errors from recurring in the future.