Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality. Data observability has been all the rage in data management circles for […]
Data Trustability: The Bridge Between Data Quality and Data Observability
If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. You might not even make it out of the starting gate. So, what can you do to ensure your data is up to par and […]
How to Leverage Machine Learning to Identify Data Errors in a Data Lake
A data lake becomes a data swamp in the absence of comprehensive data quality validation and does not offer a clear link to value creation. Organizations are rapidly adopting the cloud data lake as the data lake of choice, and the need for validating data in real time has become critical. Accurate, consistent, and reliable […]
How to Architect Data Quality on Snowflake
Without effective and comprehensive validation, a data warehouse becomes a data swamp. With the accelerating adoption of Snowflake as the cloud data warehouse of choice, the need for autonomously validating data has become critical. While existing Data Quality solutions provide the ability to validate Snowflake data, these solutions rely on a rule-based approach that is […]