Great question! If you're constantly dealing with missing values, duplicates, or inconsistent formats, a data observability platform can be a game-changer. It provides real-time metrics and data quality monitoring, so you can detect and fix issues before they impact your reports or decisions.

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

We're excited because MCP allows us to build intelligent, context-aware agents that go beyond alerts. With MCP, our observability tools can now support real-time metrics analysis, dynamic thresholding, and even automated remediation. It’s a huge step forward in delivering reliable and scalable data observability.

That's a great distinction to understand! Technical data quality focuses on things like accuracy, completeness, and consistency—basically, whether the data is structurally sound. Business data quality, on the other hand, asks if the data actually supports how your organization defines success. For example, a report might be technically correct but still misleading if it doesn’t reflect your current business model. A strong data governance framework helps align both dimensions.

Root cause analysis is a critical part of data reliability, and we’re making it smarter. Instead of manually sifting through logs or lineage graphs, Sifflet uses AI and metadata to automate root cause detection and suggest next steps. Our observability tools analyze query logs, pipeline dependencies, and usage patterns to surface the 'why' behind incidents — not just the 'what.' That means faster triage, quicker resolution, and fewer surprises downstream.

Our 2025 vision is all about pushing the boundaries of cloud data observability. We're focusing on deeper automation, AI-driven insights, and expanding our observability platform to cover everything from real-time metrics to predictive analytics monitoring. It's about making data operations more resilient, transparent, and scalable.

Sifflet’s data catalog provided a clear and organized view of jobvalley’s data assets, making it much easier for new team members to understand the data landscape. This significantly cut down onboarding time and helped new hires become productive faster.

Great question! Data observability plays a key role in GDPR compliance by giving teams real-time visibility into where personal data lives, how it's being used, and whether it's being processed according to user consent. With an observability platform in place, you can track data lineage, monitor data quality, and quickly respond to deletion or access requests in a compliant way.