DATA OBSERVABILITY FOR INSURANCE

Insurance runs on trust and speed. Get both with full visibility into your data.

Stay compliant, accelerate claims & stop fraud with full visibility into your data

The Data Complexity Challenge in Insurance

Insurance companies manage vast amounts of sensitive data across multiple systems, formats, and regulatory frameworks.

Without complete visibility, critical business processes suffer.

Regulatory Compliance Risks

Meeting NAIC, state insurance commission requirements & federal regulations demands flawless data accuracy.
Manual validation processes are time-consuming and error-prone, leaving organizations vulnerable to compliance violations and penalties.

Actuarial Model Uncertainty

Risk pricing and reserve calculations depend on clean, reliable data.
Data quality issues in historical claims data can skew actuarial models, leading to mis-pricing and increased financial risk.

PII Data Security Concerns

Managing sensitive personal information across multiple systems increases privacy risks.
Without proper data governance visibility, it's challenging to ensure PII compliance and prevent data breaches.

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Data Observability Transforms Insurance Data Operations

Sifflet empowers insurance leaders to detect issues proactively, ensure data reliability, and unlock operational excellence, across every policy, claim, and customer interaction.

USE CASE #1

Automated Regulatory Compliance Monitoring

‍The challenge: Insurance companies spend weeks manually validating data for regulatory reports. With hundreds of data points across multiple systems, errors slip through, risking compliance violations and costly penalties from state insurance commissions.

‍The Sifflet edge: Sifflet automatically monitors all regulatory data pipelines in real-time, catching anomalies before they reach compliance reports. Pre-built validation rules for NAIC requirements ensure your data meets regulatory standards every time.

Automated NAIC data validation with pre-configured rules
Real-time alerts for compliance-critical data issues
Complete audit trails ready for regulatory examinations

USE CASE #2

Accelerated Claims Processing

‍The challenge: Claims adjusters waste hours investigating data discrepancies between policy systems and claims platforms. Missing or inconsistent data delays settlements, frustrates customers, and increases operational costs.

‍The Sifflet edge: Complete visibility into your claims data pipeline from FNOL through settlement. Sifflet identifies data quality issues upstream, preventing delays and ensuring adjusters have reliable information for faster decisions.

End-to-end claims data lineage with instant issue tracking
Cross-system consistency validation (policy ↔ claims ↔ payment)
Predictive alerts prevent processing delays before they occur

USE CASE #3

Enhanced Fraud Detection Accuracy

‍The challenge: Fraud detection models rely on data from multiple sources that often contain inconsistencies. Poor data quality creates blind spots, allowing fraudulent claims to slip through while flagging legitimate claims incorrectly.

‍The Sifflet edge: Sifflet ensures fraud detection models receive clean, consistent data by monitoring all input sources in real-time. Advanced data profiling catches subtle inconsistencies that could compromise fraud scoring accuracy.

Real-time monitoring of fraud model input data quality
Cross-reference validation across claims, policy, and external data
ML model drift detection when data patterns change

USE CASE #4

Reliable Actuarial and Risk Modeling

‍The challenge: Actuaries spend months cleaning historical data for pricing models, only to discover quality issues after models are built. Unreliable data leads to mis-pricing, inadequate reserves, and increased financial risk.

‍The Sifflet edge: Comprehensive data quality monitoring across all actuarial data sources. Sifflet validates historical claims patterns, policy data consistency, and external risk factor reliability, giving actuaries confidence in their models.

Historical data validation with trend analysis for outlier detection
External data source reliability scoring and monitoring
Automated data quality reports for actuarial model documentation

Enterprise Security

SOC 2 Type II certified with advanced encryption and access controls. Purpose-built to handle sensitive PII data with the security standards insurance companies require.

Seamless Integration

Connect to your existing policy systems, claims platforms, and data warehouses without disruption. Pre-built connectors for major insurance software providers.

Scalable Architecture

Handle millions of policies and claims records with enterprise-grade performance. Scale monitoring across all lines of business from personal to commercial insurance.

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Gartner® Hype Cycle™ for Data and Analytics Governance.

Get access to the 2025 Gartner Hype Cycle for Data & Analytics Governance and discover why Data Observability and AI governance are reshaping how modern organizations manage data trust, compliance, and agility.

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The No-Nonsense Checklist for Data Observability Buyers

Cut through the buzz and download this (really) free checklist that helps you pick a data observability platform that pays off in speed, trust, and measurable impact.

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Data Observability, Five Years In: Why the Old Playbook Doesn’t Work Anymore

Five years ago, data observability was a novel concept, mostly reactive and tailored for high-scale tech companies. That worked for a while, but not anymore.

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Every inaccurate record increases your exposure

Sifflet helps you protect what matters: pricing, reserves, and compliance.
Prevent bad data from becoming your next risk event.

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

Frequently asked questions

How often is the data refreshed in Sifflet's Data Sharing pipeline?

The data shared through Sifflet's optimized pipeline is refreshed every four hours. This ensures you always have timely and accurate insights for data quality monitoring, anomaly detection, and root cause analysis within your own platform.

How does the updated lineage graph help with root cause analysis?

By merging dbt model nodes with dataset nodes, our streamlined lineage graph removes clutter and highlights what really matters. This cleaner view enhances root cause analysis by letting you quickly trace issues back to their source with fewer distractions and more context.

How does data observability complement a data catalog?

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.

How does Sifflet help with data drift detection in machine learning models?

Great question! Sifflet's distribution deviation monitoring uses advanced statistical models to detect shifts in data at the field level. This helps machine learning engineers stay ahead of data drift, maintain model accuracy, and ensure reliable predictive analytics monitoring over time.

Why is table-level lineage important for data observability?

Table-level lineage helps teams perform impact analysis, debug broken pipelines, and meet compliance standards by clearly showing how data flows between systems. It's foundational for data quality monitoring and root cause analysis in modern observability platforms.

What are some engineering challenges around the 'right to be forgotten' under GDPR?

The 'right to be forgotten' introduces several technical hurdles. For example, deleting user data across multiple systems, backups, and caches can be tricky. That's where data lineage tracking and pipeline orchestration visibility come in handy. They help you understand dependencies and ensure deletions are complete and safe without breaking downstream processes.

What’s the best way to prevent bad data from impacting our business decisions?

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

What role does anomaly detection play in modern data contracts?

Anomaly detection helps identify unexpected changes in data that might signal contract violations or semantic drift. By integrating predictive analytics monitoring and dynamic thresholding into your observability platform, you can catch issues before they break dashboards or compromise AI models. It’s a core feature of a resilient, intelligent metadata layer.

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