Kensu: How can data teams scale up by going beyond "data quality"?
Over time data teams have been stacking up data sources, pipelines, and products to answer a growing flow of user requests. Data quality solutions, usually scanning data at rest, have been adopted to maintain quality and avoid friction with the stakeholders.
While data teams can quickly deploy such platforms, they now present some limits that stop them from scaling up their activities: no information about the context, no synchronization with data usage, no continuous validation, etc. To solve this, a new category of solution that goes beyond data quality has emerged: Data Observability at the source..
Join this session and discover how data teams can go a step further in managing their data quality by being able to observe their data in real-time in their environment and what are the benefits of this new approach.
This session will explore the main differences between data quality and observability. Our expert will also detail how the data teams can benefit from adopting an approach that is not limited to scanning data at rest but is synchronized with data usage, takes into account the context, and follows the data application lifecycle.