Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.

Aligning KPIs with your data team’s goals is essential for clarity and motivation. When everyone knows what success looks like and how it’s measured, it creates a sense of purpose. Tools that support data quality monitoring and metrics collection can help track those KPIs effectively and ensure your team is on the right path.

Data observability provides visibility into your pipelines by tracking key metrics like row counts, duplicates, and ingestion patterns. It acts as an early warning system, helping teams catch volume anomalies before they affect dashboards or ML models. By using a robust observability platform, you can ensure that your data is consistently complete and trustworthy.

Define clear success KPIs that support ROI, such as improvements in SLA compliance, reduction in ingestion latency, or increased data reliability. Using data observability dashboards and pipeline health metrics can help you monitor progress and communicate value to stakeholders. It's also important to set expectations early and maintain strong internal communication.

Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.

Sifflet enriches your data lineage tracking by visually mapping out your dbt models and how they connect across different projects. This is especially useful for teams managing multiple dbt repositories, as Sifflet brings everything together into a clear, centralized lineage view that supports root cause analysis and proactive monitoring.

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.

Data lineage tracking is essential for understanding how data flows from storage to dashboards. When something breaks, Sifflet helps you trace it back to the storage layer, whether it's a corrupted file in S3 or a schema drift in MongoDB. This visibility is critical for root cause analysis and ensuring data reliability across your pipelines.