Who should use the Monitor Data Quality workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
A streamlined workflow to validate, prepare, monitor, and manage data quality using specialized tasks and tools for reliable insights.
Deliverable outcome
A robust automated pipeline is in place, capable of scheduling, error handling, and integration with downstream systems.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A robust automated pipeline is in place, capable of scheduling, error handling, and integration with downstream systems.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Kili Technology to a clear baseline of data quality is established, with actionable issues flagged for resolution. Then, you pass the output to Make to data is in a consistent format and structure, reducing ambiguity for subsequent monitoring steps. Then, you pass the output to Soda AI to a live dashboard of data quality metrics is established, with automated notifications for violations. Finally, dbt Cloud (AI-Powered) is used to a robust automated pipeline is in place, capable of scheduling, error handling, and integration with downstream systems.
Validate Data Quality
A clear baseline of data quality is established, with actionable issues flagged for resolution.
Transform Data
Data is in a consistent format and structure, reducing ambiguity for subsequent monitoring steps.
Monitor Data Quality
A live dashboard of data quality metrics is established, with automated notifications for violations.
Manage Data Pipelines
A robust automated pipeline is in place, capable of scheduling, error handling, and integration with downstream systems.
Assess the initial quality of your data sources using Kili Technology to identify issues like missing values, duplicates, or inconsistencies before further processing.
Early validation prevents wasted effort downstream and ensures only high-quality data enters the monitoring pipeline.
A clear baseline of data quality is established, with actionable issues flagged for resolution.
Use Make to clean, normalize, and restructure your data, such as converting formats or aggregating fields, to create a uniform dataset ready for monitoring.
Transformation standardizes data, making it easier to apply consistent quality rules and compare across sources.
Data is in a consistent format and structure, reducing ambiguity for subsequent monitoring steps.
Run continuous quality checks with Soda AI to track metrics like completeness, accuracy, and freshness, generating alerts for any deviations.
This is the core step where actual monitoring occurs, providing real-time visibility into data health.
A live dashboard of data quality metrics is established, with automated notifications for violations.
Configure and maintain data pipelines using dbt Cloud to ensure reliable execution of quality checks, schedule regular runs, and handle failures or anomalies.
Effective pipeline management ensures monitoring runs smoothly and results are actionable, preventing data drift from going unnoticed.
A robust automated pipeline is in place, capable of scheduling, error handling, and integration with downstream systems.
Timeline Map
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
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