Defining data quality is far from being a one-size-fits-all task. Data quality should be approached with the unique dynamics and complexities of each organization, adapting to the specificities of the type of data, related technologies, and its usage. This definition encompasses first a technical aspect, universally applicable to all organizations, in which the emphasis is on the smooth functioning of data pipelines. Across industries, consensus prevails on ensuring data freshness, preventing data loss, and maintaining vigilance over schema changes.
However, there exists a critical dimension of data quality that is tied to the specific use cases of the data from the business perspective. This entails, for example, the understanding that certain metrics must never fall below specified thresholds to remain meaningful and that the data is consistent with business logic. In essence, data quality definitions must blend technical robustness and adaptability to the unique landscape of every company. Achieving this balance involves collaboration with all data stakeholders.
Why this matters
As companies constantly update their data stacks and embrace new approaches to data management, isolated data quality definitions and monitoring results in silent and unseen anomalies. This can lead to data catastrophes (such as customer attrition, legal fines, and uninformed and wrong decision-making), and also productivity issues and challenges when aligning responsibilities and processes across teams.
At Sifflet, we've developed our data observability solution to serve all data teams and stakeholders. Our approach to building our data platform is one so that data remains organized, accessible, and reliable:
Below is how these concepts are transformed into tangible product functionalities.
Sifflet provides a wide range of ML-based monitors developed to support any use case, such as table-level health monitors, field profiling, format validations, metrics health, and business logic without relying on complex and custom SQL queries.
All templates come pre-computed, enabling quick and straightforward setup, and they offer multiple parameterization options for customization, allowing users to fine-tune the solution to their specific needs.
Sifflet’s intuitive UI provides a seamless and efficient user experience, allowing users to quickly create monitors create monitors and gain valuable insights about their data quality vulnerabilities. Users can easily track the monitoring results over time to identify patterns and trends and identify actions for improvement.
The Sifflet AI Assistant simplifies data context enrichment and data quality monitoring setup, and maintenance.
For business users, the Sifflet AI Assistant allows the creation of complex data quality monitors using natural language, such as setting a monitor for specific data formatting without getting into the complexities of using regular expressions. Furthermore, all Sifflet users can utilize the AI Assistant to supervise active monitors and tweak the parameters of ML-based monitors to optimize accuracy and reduce false alerts.
Powered by field-level automated lineage, Sifflet goes beyond the detection of the anomaly to enable troubleshooting with minimal time-to-resolution. This is delivered with the automated business impact assessment, the root cause analysis, and the seamless cross-team collaboration on the incident report.
With Sifflet, organizations can effectively design and manage their own meaningful version of data quality. Data engineers can monitor their data pipelines while having visibility of the business requirements and dependencies. Data users can collaborate with their colleagues on the platforms which extends the monitoring coverage of their team's needs based on their data use cases. Finally, leaders can have the peace of mind of being ahead of any quality catastrophes while ensuring team productivity.
