Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability.

By combining speech-to-text with BigQuery's analytics capabilities, you can understand things like customer sentiment and recurring product issues.

Accelerate building data apps in Snowflake and Streamit with Fivetran.

As the cybersecurity threat landscape continues to evolve globally, organizations operating in the financial sector are seeing regulations shift to address the associated risks, and none may prove more impactful than the European Union’s (EU) Digital Operational Resilience Act (DORA). This regulation aims to strengthen the operational resilience of financial entities (FEs), and their third-party information and communication technology (ICT) providers.

An effective data governance strategy is crucial to manage and oversee data effectively, especially as data becomes more critical and technologies evolve. This strategic approach to data governance aligns with findings from a McKinsey survey, suggesting that companies with solid data governance strategies are twice as likely to prioritize important data — leading to better decision-making and organizational success.

New BigQuery functions monitor skew between training and serving data, and monitor drift in serving data over time, all using SQL.