Who should use the Validate 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
Practical execution plan for validate data quality with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A certified dataset with documented quality status, ready for use in analytics, ML, or reporting.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A certified dataset with documented quality status, ready for use in analytics, ML, or reporting.
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 Soda AI to a clear, documented set of quality rules and thresholds that will be used to evaluate the data. Then, you pass the output to dbt Cloud (AI-Powered) to a profiling report that highlights data shape, missingness, and potential red flags. Then, you pass the output to Soda AI to a validation report showing which criteria passed, which failed, and the exact records that violated rules. Then, you pass the output to SQLAI.ai (AI Pro Query SQL) to a clear understanding of why each quality failure occurred, enabling targeted fixes. Then, you pass the output to dbt Cloud (AI-Powered) to a cleaned dataset where all identified quality issues are resolved or documented as acceptable exceptions. Finally, DQLabs is used to a certified dataset with documented quality status, ready for use in analytics, ml, or reporting.
Define quality criteria and thresholds
A clear, documented set of quality rules and thresholds that will be used to evaluate the data.
Profile the dataset
A profiling report that highlights data shape, missingness, and potential red flags.
Validate against defined criteria
A validation report showing which criteria passed, which failed, and the exact records that violated rules.
Investigate root causes of failures
A clear understanding of why each quality failure occurred, enabling targeted fixes.
Remediate and correct data issues
A cleaned dataset where all identified quality issues are resolved or documented as acceptable exceptions.
Re-validate and certify data quality
A certified dataset with documented quality status, ready for use in analytics, ML, or reporting.
Start by identifying the key dimensions of data quality relevant to your use case (e.g., completeness, accuracy, consistency, timeliness, uniqueness). For each dimension, set measurable thresholds or rules (e.g., 'no more than 2% missing values in column X', 'all email addresses must match regex pattern'). Document these criteria in a shared specification to align stakeholders.
Why Soda AI: Soda AI is a dedicated data quality framework that allows defining and enforcing data quality rules, thresholds, and contracts, directly matching the need for a quality criteria definition tool.
Run automated profiling on the raw dataset to generate summary statistics, distributions, missing value counts, and data type inferences. Use profiling tools to quickly surface anomalies like unexpected nulls, outliers, or format inconsistencies. This step provides a baseline understanding of the data's current state before deeper validation.
Why dbt Cloud (AI-Powered): dbt Cloud (AI-Powered) can profile data through automated SQL generation and documentation, and is commonly used for data profiling in modern data stacks.
Apply the quality criteria from step 1 to the profiled data using automated checks. For each rule, run a validation query or assertion (e.g., 'count rows where column X is null', 'check that date column values are within expected range'). Collect pass/fail results and log violations with row-level details for traceability.
Why Soda AI: Soda AI is a dedicated data validation framework that enforces data quality rules and detects anomalies, directly matching the validation step.
For each failed check, drill into the violating records to understand the source of the issue. Common causes include upstream system bugs, manual entry errors, schema changes, or data integration mismatches. Document findings in a root cause analysis (RCA) log to inform remediation and prevent recurrence.
Why SQLAI.ai (AI Pro Query SQL): SQLAI.ai (AI Pro Query SQL) generates SQL queries from natural language and explains them, enabling root cause analysis of data failures through querying.
Based on the root cause analysis, apply corrections to the dataset. This may involve removing duplicate rows, imputing missing values with defaults or calculated values, standardizing formats (e.g., dates, phone numbers), or filtering out irrecoverable records. For systemic issues, coordinate with data producers to fix the pipeline upstream.
Why dbt Cloud (AI-Powered): dbt Cloud (AI-Powered) is a leading data transformation tool that automates SQL generation for cleaning and correcting data issues.
Run the validation suite again on the corrected dataset to confirm all previously failing checks now pass. If any checks still fail, iterate on remediation. Once all critical and major checks pass, generate a final quality certification report summarizing the overall quality score, remaining minor issues, and a sign-off for downstream use.
Why DQLabs: DQLabs monitors data pipeline health, enforces quality rules, and provides automated discovery, combining re-validation with reporting capabilities.
§ 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.
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