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Frequently asked questions

What best practices should I follow when planning for data quality monitoring?
Start by defining data validation rules and ownership early in your architecture. Use observability tools that support proactive monitoring, anomaly detection, and root cause analysis to catch issues before they affect downstream systems or business decisions.
Can non-technical users benefit from Sifflet’s Data Catalog?
Yes, definitely! Sifflet is designed to be user-friendly for both technical and business users. With features like AI-driven description recommendations and easy-to-navigate asset pages, even non-technical users can confidently explore and understand the data they need.
How does Sifflet help optimize Data as a Product initiatives?
Sifflet enhances DaaP initiatives by providing comprehensive data observability dashboards, real-time metrics, and anomaly detection. It streamlines data pipeline monitoring and supports proactive data quality checks, helping teams ensure their data products are accurate, well-governed, and ready for use or monetization.
Who should be the first hire on a new data team?
If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.
How can decision-makers ensure the data they receive is actionable and easy to understand?
It's all about presentation and relevance. Whether you're using Tableau dashboards or traditional slide decks, your data should be tailored to the decision-maker's needs. This is where data observability dashboards and metrics aggregation come in handy, helping to surface the most impactful insights clearly and quickly so leaders can act with confidence.
How is Etam using data observability to support its 2025 strategy?
Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.
Does Sifflet store any of my company’s data?
No, Sifflet does not store your data. We designed our platform to discard any data previews immediately after display, and we only retain metadata like table and column names. This approach supports GDPR compliance and strengthens your overall data governance strategy.
How is Sifflet using AI to improve data observability?
We're leveraging AI to make data observability smarter and more efficient. Our AI agent automates monitor creation and provides actionable insights for anomaly detection and root cause analysis. It's all about reducing manual effort while boosting data reliability at scale.
Still have questions?