Co-hosted by Artefact and dbt Labs, the webinar “Data Quality by Design: Treating Causes, Not Symptoms” aimed to address a critical strategic alignment that remains underutilized across enterprises: the convergence of Data Governance and Data Engineering.

Florence Bénézit, Partner & Global Manufacturing Lead at Artefact, and Eric Ortiz, Partner Development Manager EMEA at dbt Labs, presented a comprehensive methodological framework for embedding data quality directly into the architecture and design phases of data products—a foundational approach to delivering measurable, long-term ROI.

Why Reactive Quality Management Fails

In most enterprise environments, data quality is handled reactively: errors are identified downstream during consumption—such as in dashboards or executive BI reports—and patched on a case-by-case basis at the end of the pipeline.
This approach suffers from three major drawbacks:

  • Significant Operational Overhead: Data teams spend the majority of their time on corrective maintenance and manual data reconciliation.
  • Erosion of Business Trust: Contradictory or incomplete data hinders analytics adoption and executive decision-making.
  • A Barrier to Scalability: The accumulation of ad-hoc hotfixes complicates pipelines and inflates technical debt.

Data quality cannot be relegated to post-hoc cleanup or treated as a purely documentation-driven exercise within a data catalog. To be effective, quality must be engineered and executed “by design” within the codebase and architecture, spanning from ingestion to consumption.

The 6-Step Roadmap: From Source to Consumer

To resolve root causes rather than symptoms, Artefact structures quality management across six links of the data value chain:

[ A: Source Quality ] ➔ [ B: Monitoring & Remediation ] ➔ [ C: Data Product Design ] ➔ [ D: Code Quality ] ➔ [ E: DataOps & Run ] ➔ [ F: Service & Consumption ]
  1. Source Quality (A)
    The primary imperative is to intervene at the upstream source systems: refining business input processes, adapting enterprise architecture, and enforcing strict input formats within IT applications.
  2. Source Monitoring (B)
    Implementing automated checks on the raw data layer to detect anomalies upon ingestion and trigger immediate remediation workflows.
  3. Data Product Design (C)
    Formalizing strict data contracts between business and engineering teams prior to development. These contracts define schema specs, business semantics, and acceptable data quality thresholds.
  4. Code Quality via dbt (D)
    Industrializing unit and integration tests across transformation pipelines using dbt. Powered by automated CI/CD checks (dbt build), non-compliant code is blocked prior to deployment into production.
  5. Operational Monitoring (E)
    Continuous monitoring of runtime performance: tracking data freshness, execution latencies, and volume spikes to prevent service disruptions.
  6. Downstream Observability (F)
    Surfacing data health metrics directly within BI and reporting tools (e.g., Tableau, Sigma) to alert end-users in real time when issues are detected.

Business and Engineering: Covering the 8 Dimensions of Data Quality

A Data Quality by Design initiative must simultaneously address business requirements and engineering constraints:

Business Dimensions (The 3 Cs + Accuracy):

  • Completeness: Absence of unjustified missing values.
  • Conformity: Adherence to standardized formats and business rules.
  • Consistency: Logical coherence across tables and columns.
  • Accuracy: Strict alignment with real-world ground truth.

Technical Dimensions:

  • Uniqueness: Absence of duplicate primary keys.
  • Timeliness: Data availability aligned with scheduled SLAs.
  • Freshness: Cadence of data refreshes matched to operational needs.
  • Integrity: Lossless data preservation across ingestion and transfer pipelines.

The Value Proposition of the dbt Labs Platform

Leveraging dbt translates these theoretical dimensions into executable code specifications. Checks are declared in human-readable YAML configuration files, version-controlled via Git, and executed automatically with every model update.
By combining dbt’s data lineage, data contracts, and agentic AI-driven assistants (such as dbt Wizard), engineering teams can pinpoint the root cause of an anomaly in minutes while maintaining full end-to-end transformation lineage.

The Critical Impact on Agentic AI

The emergence of autonomous AI agents fundamentally escalates the risk profile associated with poor data quality. Unlike traditional dashboards designed to inform human decision-making, AI agents directly execute operational workflows inside core enterprise systems (e.g., ERPs, CRMs).

In this paradigm, flawed data no longer merely produces flawed reporting, it triggers erroneous automated transactions. A modest 1% error rate across 10,000 daily transactions yields 100 faulty automated operations per day. Autonomous AI therefore demands uncompromising standards for data quality, low latency, and semantic precision.

Action Plan for Data Leaders

To transition from a reactive posture to a Data Quality by Design framework, data leaders should prioritize the following operational imperatives:

  • Prioritize Strategic Use Cases: Establish data contracts for high-impact business data products rather than attempting to overhaul the entire enterprise data asset base at once.
  • Automate Testing in the Development Lifecycle: Embed schema validation and anomaly detection directly into dbt pipelines within the CI/CD workflow.
  • Remediate Anomalies at the Source: Leverage observability insights to fix upstream input logic and enterprise IT flows, driving sustainable, long-term quality improvements.

Watch the webinar on YouTube (French)