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Solutions

Common Sifflet use cases

Democratize Data Quality

Make data quality everyone’s business

Enable real-time accessibility to data quality metrics throughout the entire organization, catering to both technical and non-technical users.

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How it works

Streamlined monitoring experience
  • Collect assets spanning the entire data lifecycle thanks to built-in integrations
  • Enable non technical users to create business-informed monitors thanks to an intuitive UI and to the Sifflet AI Assistant
Improved information accessibility
  • Access assets’ health status through the Data Catalog and lineage for de-risked data self-service
  • Get notified of upstream incidents directly on BI tools via a browser extension
BUsiness impact prevention

Gain proactivity, eliminate reactivity

Proactively address data quality issues to prevent disruptions.

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How it Works

Comprehensive monitoring
  • Set up a comprehensive monitoring coverage thanks to a large library of OOTB monitors
  • Create ML-based monitors for essential KPIs and benefit from robust alerting & simplified configuration thanks to historical data
  • Uncover complex data quality issues thanks to advanced capabilities such as multidimensional monitoring
Proactive data quality management
  • Get notified on data quality issues before there is a business impact
  • Proactively halt the propagation of data quality anomalies downstream with Sifflet Flow Stopper
  • Harness data lineage to pinpoint downstream dependencies of changes and impacted owners to enable a seamless experience for data consumers
Root Cause Analysis

Quickly identify the cause of data quality issues

Streamline your data team’s troubleshooting workflow with automated data lineage and monitoring, eliminating hours of investigative work to pinpoint issues’ initial points of failure.

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How it works

Decrease time to detection and resolution
  • Decrease average time to detection by seamlessly deploying all monitors at every stage of the data lifecycle
  • Decrease time to resolution with effortless identification of data quality issues’ origin through automated lineage and ownership definition
Cloud migration monitoring

Mitigate disruption and risks

Optimize the management of data assets during each stage of a cloud migration.

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How it works

Before migration
  • Go through an inventory of what needs to be migrated using the Data Catalog
  • Identify the most critical assets to prioritize migration efforts based on actual asset usage
  • Leverage lineage to identify downstream impact of the migration in order to plan accordingly
During migration
  • Use the Data Catalog to confirm all the data was backed up appropriately
  • Ensure the new environment matches the incumbent via dedicated monitors
After migration
  • Swiftly document and classify new pipelines thanks to Sifflet AI Assistant
  • Define data ownership to improve accountability and simplify maintenance of new data pipelines
  • Monitor new pipelines to ensure the robustness of data foundations over time
  • Leverage lineage to better understand newly built data flows
Data governance

Proactive access, quality
and control

Empower data teams to detect and address issues proactively by providing them with tools to ensure data availability, usability, integrity, and security.

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How it Works

De-risked data discovery
  • Ensure proactive data quality thanks to a large library of OOTB monitors and a built-in notification system
  • Gain visibility over assets’ documentation and health status on the Data Catalog for safe data discovery
  • Establish the official source of truth for key business concepts using the Business Glossary
  • Leverage custom tagging to classify assets
Structured data observability platform
  • Tailor data visibility for teams by grouping assets in domains that align with the company’s structure
  • Define data ownership to improve accountability and smooth collaboration across teams
Secured data management
  • Safeguard PII data securely through ML-based PII detection
Data mesh

Domain-based data discovery and monitoring for data mesh

Empower cross-functional teams to discover and monitor data.

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How it Works

Structured data observability platform
  • Consolidate data observability in a single platform for federated data mesh management
  • Establish domains that reflect business structure to tailor data visibility
  • Define data ownership to improve accountability and smooth collaboration across teams
Simplified and de-risked data self-service
  • Simplify data discovery and understanding with the Data Catalog and the Business Glossary
  • De-risk data self-service through integrated monitoring capabilities
Data products

Data observability for data products

Define, document, and monitor data products to facilitate safe data self-service.

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How it Works

Documented data products
  • Consolidate data assets into single data products and document them through custom tagging to break down silos between data teams
  • Use the Data Catalog and the Business Glossary to easily understand data products’ assets
  • Define data ownership to improve accountability and smooth collaboration across teams
Reliable data product SLIs
  • Monitor and assess the health status of data products using a large library of OOTB monitors
  • Customize health scores based on product-specific criteria
  • Retrieve historical data effortlessly to provide data consumers with SLIs
data as a producT

Build consumer-trusted
Data as a Product

Track and meet Data as a Product SLAs through comprehensive and proactive monitoring.

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How it Works

Measure SLIs for data consumers
  • Access a large library of OOTB monitors
  • Detect issues before there is an impact thanks to a built-in notification system
  • Retrieve historical data effortlessly to provide data consumers with SLIs
Simplify collaboration
  • Define data ownership to improve accountability and smooth collaboration across teams
Cost Observability

Cost-efficient data pipelines

Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.

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How it works

Data asset optimization
  • Leverage lineage and Data Catalog to pinpoint underutilized assets
  • Get alerted on unexpected behaviors in data consumption patterns
Proactive data pipeline management
  • Proactively prevent pipelines from running in case a data quality anomaly is detected
CUSTOMER STORIES

Sifflet ticked all of our boxes

In addition to monitoring the quality and accuracy of the data assets, we were also looking to ensure the reliability of the pipelines, observe schema changes and other metadata-related metrics.

Laurent Tachet des Combes
Head of Data, Meero
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With Sifflet we know what to do once the rule fails

We chose Sifflet for its wide offering. We’ve checked out other vendors from the space, and they have all data quality rules, but what we need is the next step. Being able to know what to do once that rule fails.

Logo NextBite
Ross Serven
Director of Data Engineering, Nextbite
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Sifflet allows you to find and trust your data

It is very convenient to have the data quality feature together with the data catalog. I think it’s a great combination: the data catalog allows you to find all the data you need and the data quality feature monitors our data flows, ensuring that the data we use is reliable at all times.

Marco Kleine-Böhme
VP Data & Analytics, jobvalley
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