Data Quality Management Services
Data quality management for failures that should not reach the dashboard.
A successful pipeline run can still deliver stale, partial, duplicated, or semantically wrong data. We add tests, freshness checks, anomaly monitoring, lineage, and named response paths around the tables that carry important decisions.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does the team learn about stale or incomplete data from a dashboard user?
Can anyone trace a disputed metric from report to source without reading weeks of logs?
Plain answer
Data quality management makes unreliable data visible before it reaches a report or automated decision. RaftLabs adds dbt tests, freshness checks, volume and value monitoring, schema-change alerts, lineage, and response ownership to existing pipelines and warehouses. Focused implementations start at $8,000 and usually take six to ten weeks.
The job was green. The table was still wrong.
An upstream field changed from empty to zero. Nothing crashed. When the load completed, the dashboard refreshed and showed a real decline where no decline had happened, so nobody received an alert.
Data quality work begins with failures that look plausible.
Relevant migration proof
- Energia remediation scripts
- 11
- Run before production migration
- customer records in scope
- 300K+
- RaftLabs project record
- recurring customer feeds
- 4
- With missing-file visibility
The Energia Rewards case study documents these controls inside a platform migration. It does not represent a standalone data-quality engagement or prove that every implementation needs the same checks.
Add data-quality controls where a plausible wrong number can change a decision.
Monitoring every table creates noise. Start with the data products whose failure has an owner and a consequence.
Critical reports or automations depend on scheduled pipeline data.
Known tests pass while freshness, volume, or meaning can still drift.
A team can own alerts and follow a recovery runbook.
The immediate problem is moving inaccessible source data.
No one can define which tables or metrics are decision-critical.
Alerts will be created without an owner or response window.
Scope
Controls that make trust inspectable
- 01
Structural and business-rule tests
Check required values, uniqueness, relationships, accepted ranges, and domain rules during each transformation run. - 02
Freshness and volume monitoring
Alert when a source is late, a load is partial, or the delivered row count departs from its expected pattern. - 03
Schema and distribution change detection
Surface breaking fields and unusual value shifts before downstream models silently reinterpret them. - 04
Lineage and impact analysis
Trace a dashboard measure back to its sources and identify which consumers a failed table can affect. - 05
Alert routing and runbooks
Assign severity, owner, response time, quarantine path, and recovery steps so monitoring changes what happens after a failure.
How it works
From silent failure to owned response
- Phase 101
Rank the data risks
Identify the tables, metrics, decisions, and failure modes where bad or late data creates material harm.
- Phase 202
Define trustworthy data
Write structural, business-rule, freshness, volume, and distribution checks with agreed tolerances and owners.
- Phase 303
Test failures on purpose
Inject late, partial, duplicated, and changed data so alerts, quarantine paths, and recovery procedures are exercised.
- Phase 404
Put response into operation
Release dashboards and alerts with runbooks, routing, lineage, and a review cadence for new sources and rules.
Risk
What makes quality monitoring fail
- Every anomaly pages the team
- Set severity and tolerance by consequence so normal variation does not train people to ignore alerts.
- A test blocks good data
- Define quarantine, override, and review paths for rules that can produce a false positive.
- A metric has no stable meaning
- Agree its population and calculation before treating a value shift as a pipeline incident.
- Lineage stops at the warehouse
- Connect important models to the reports and automated decisions that consume them.
Scope and price
A focused data-quality implementation starts at $8,000.
Start with the critical tables, known failure modes, freshness targets, alert owners, and recovery paths.
If the assessment shows that inaccessible data or weak architecture is the root problem, we will route the work to pipeline or warehouse development before adding broad monitoring.
Starting investment
Starts at $8,000
Focused work usually takes six to ten weeks. Custom anomaly models, deep lineage, many sources, or regulated evidence can extend the plan.
Defined alert ownership
Client-owned controls
Work with us
Show us the data failure people find too late.
Bring the affected tables, reports, and last incident. We will identify the smallest useful set of checks and response paths.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Data quality management defines what trustworthy data means, tests records and pipeline behavior against those rules, alerts the right owner when a rule fails, and documents how to recover. It covers correctness, completeness, uniqueness, freshness, consistency, and traceability across the data path.
Tests check known rules, such as a required identifier or a valid relationship. Monitoring detects unexpected shifts in freshness, volume, or value distributions. A useful system combines both: tests catch defined failures, while monitoring surfaces changes nobody knew to encode in advance.
Not usually. Focused controls can be added to an existing dbt project, warehouse, or pipeline. If the data cannot be accessed reliably, has no stable identifiers, or bypasses a shared platform entirely, pipeline or warehouse work may need to precede broader monitoring.
A focused implementation starts at $8,000 and usually takes six to ten weeks. Scope grows with source count, table count, custom business rules, lineage depth, alert integrations, and the recovery paths required. We price the first set of critical data products before development begins.
The Energia Rewards case documents 11 remediation scripts, more than 300,000 customer records in migration scope, four recurring customer feeds, and missing-file alerts. It is evidence of data controls inside a platform migration, not a standalone data-quality implementation or a guarantee of the same scale.