Five Common Data Warehouse
Issues | Podcast

Many organizations invest heavily in data warehouses but struggle to achieve the expected value. In this episode of Weaver: Beyond the Numbers, Nitya Vashishtha and Morgan Page explore the five most common data warehouse problems and strategies to resolve them.

Key Points:

• Data integrity issues arise when multiple data sources are not properly unified.
• Poor data quality leads to inaccurate reports and unreliable insights.
• Data security must balance accessibility and protection with modern encryption and access controls.

Nitya highlights data integrity as the first challenge. She emphasizes the significance of unifying data from various sources and the importance of using robust extract, transform and load (ETL) tools. These allow your business’ data to be integrated and analyzed more effectively.

Even if data sources are unified, the quality of data is a major factor in the success of a data warehouse. This can be achieved by implementing a strict cleansing validation system. This, according to Nitya, should act as a benchmark for maintaining the quality of the data. “A well-structured data warehouse is the backbone of any successful analytics strategy — but only if it’s built and maintained correctly,” said Nitya.

With increasing cybersecurity risks, businesses must secure their data without limiting accessibility. This has become crucial in 2025 and beyond. Businesses can address this by implementing encryption, masking methodologies and access controls in their daily operations. Morgan highlights how modern security frameworks allow for row- and column-level permissions, ensuring that users access only what they need.

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