Data Engineering

Data Quality Management - Catch Issues in the Pipeline, Not in the Board Meeting

Numlytics implements expert data quality management for enterprises across the US, UK, Australia & UAE. We build automated data quality frameworks using dbt tests, Great Expectations, and Soda - validating completeness, accuracy, consistency, and timeliness at every layer of your data pipeline so quality issues are caught before they reach dashboards, models, or decision-makers.

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Automated validation at every pipeline layer, not manual spot checks
dbt tests, Great Expectations & Soda - tool-agnostic delivery
Quality dashboards & Slack alerts included as standard
Up to 50% lower cost vs US/UK data engineering firms
Typical Outcomes
90%
Reduction in data quality
incidents post-implementation
100%
Pipeline coverage with
automated quality checks
3wk
First quality framework
live in 3 weeks
50%
Lower cost vs US/UK
engineering firms
We implement
dbt Tests
Great Expectations
Soda
Monte Carlo
Microsoft Purview
Databricks DQ
Snowflake
Microsoft Fabric
What We Implement

Data Quality Is Caught in the Pipeline or Felt in the Meeting

Every organisation with a data quality problem knows the symptom: an executive questions a number in a report. Nobody can explain it. Someone spends three days tracing it back to a source system transformation that broke six weeks ago. By then, the decision has already been made on bad data.

Effective data quality management moves the detection point from the boardroom to the pipeline. Automated validation rules run at every layer - completeness, accuracy, consistency, timeliness, and uniqueness checks executed as part of your normal pipeline run, with failures that alert your team before bad data reaches a single downstream consumer.

Our data quality framework implementation covers profiling, rule design, automated testing, monitoring dashboards, and the ownership model that keeps standards maintained over time, not just at launch.

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Why Clients Come to Us
"A dashboard showed the wrong number and we didn't know for 3 weeks"
No automated checks on pipeline output. A source system change silently broke a transformation. By the time the error was noticed, three weeks of decisions had been made on incorrect data.
"Our analysts spend 40% of their time fixing and explaining data issues"
Data quality problems discovered in production - by business users - require analyst investigation time to diagnose, fix, and communicate. A reactive data quality process turns analysts into support staff.
"We don't know what our data quality actually is"
No profiling, no quality metrics, no baseline. Quality issues are anecdotal, reported when someone notices something wrong, not measured systematically. No way to improve what isn't measured.
"Our AI models are producing unreliable outputs and we think it's the data"
ML model accuracy degrading over time due to training data quality drift. Features computed from inconsistent or stale data. Garbage in, garbage out - the model isn't the problem.
What We Deliver

Six Components of a Complete Data Quality Framework

A full data quality management programme covering profiling, rules, automation, monitoring, and the governance model that sustains quality over time.

Data Profiling & Quality Baseline

Before writing a single test, we profile your data - null rates, cardinality, distribution, referential integrity, and pattern analysis across every critical dataset. The profiling baseline defines where your quality stands today and which issues to prioritise first.

Automated profiling across all critical datasets
Quality baseline report per dimension
Priority issue list ranked by business impact
Quality Rule Design & Documentation

We co-design data quality rules with data owners and business stakeholders - defining what "good" looks like for each dimension (completeness, accuracy, consistency, timeliness, uniqueness) per dataset, agreed and documented before automation begins.

Quality rules per dimension & dataset
Business rule glossary & definitions
Threshold & severity classification
Automated Testing Implementation

Automated data quality tests implemented at every pipeline layer - source validation, transformation checks, and output assertions - using dbt tests, Great Expectations, or Soda, integrated into your existing pipeline run and CI/CD workflow.

dbt tests across all models
Great Expectations / Soda suite setup
CI/CD pipeline quality gates
Quality Monitoring & Dashboards

A real-time data quality dashboard - test pass/fail rates, quality score trends, anomaly detection alerts, and freshness monitoring, so your team has visibility into data health across every pipeline and dataset, updated on every run.

Quality score dashboard per dataset
Anomaly detection & trend alerts
Slack / Teams failure notifications
Lineage & Impact Analysis

Data lineage implemented via dbt documentation or Microsoft Purview so when a quality failure fires, your team can immediately see which upstream source caused it, which downstream models and dashboards are affected, and what the business impact is.

dbt lineage graph & impact analysis
Microsoft Purview lineage integration
Downstream impact classification
Quality Ownership & Governance Model

We design the operating model that keeps quality standards maintained, data quality owners per domain, escalation processes for failures, SLA definitions per dataset criticality, and a quarterly review cadence so the framework evolves with your data.

Data quality ownership model
Failure escalation & SLA design
Governance review cadence
How We Deliver It

From Data Profiling to Full Quality Framework in 4 Phases

First automated tests live in 3 weeks. We profile first, rule-design second so every test we write is grounded in measured reality, not assumptions.

Profile & Baseline

Automated profiling across all critical datasets - null rates, cardinality, distribution anomalies, duplicate detection, and referential integrity analysis. Output: a quality baseline report with a prioritised list of issues ranked by business impact.

⏱ Weeks 1–2
Define & Document Rules

Quality rule design workshops with data owners and business stakeholders. Every quality rule is agreed, documented, and classified by severity before automation begins, so tests reflect what the business actually needs, not what's technically easiest to check.

⏱ Weeks 2–3
Automate & Integrate

Tests implemented in dbt, Great Expectations, or Soda - integrated into your pipeline runs and CI/CD workflow. Quality monitoring dashboard deployed. Slack or Teams alerting configured for every failure severity level.

⏱ Weeks 3–6
Handover & Embed Ownership

Quality ownership model activated data owners trained on their responsibilities, escalation processes documented, SLAs agreed per dataset criticality. Full documentation so your team adds, modifies, and maintains quality rules without our involvement.

⏱ Weeks 5–7
Why Numlytics

Why Choose Numlytics for Data Quality Management

We've implemented data quality frameworks for enterprises across financial services, SaaS, manufacturing, and retail in the US, UK, and Australia.

Profile Before We Test
We profile your data before writing a single rule. Quality rules designed without a baseline are guesses and tests that fail from day one because thresholds were set without evidence cause alert fatigue, not quality improvement.
Rules Co-Designed With Business Owners
We run quality rule workshops with the data owners and business stakeholders who use the data, not just the engineers who build the pipelines. Rules that reflect what the business actually needs catch the issues that matter.
Automated - Not Manual Spot Checks
Every rule we design is automated and runs as part of your normal pipeline execution. No manual processes, no scheduled review spreadsheets, no relying on someone to notice. Quality failures alert your team automatically, every run.
Visibility Through a Quality Dashboard
Every quality framework we implement includes a monitoring dashboard showing test pass rates, quality score trends, and anomaly alerts across every dataset. Quality becomes measurable and what's measured, improves.
Connected to Your Existing Stack
We implement quality checks inside your existing dbt project, pipeline orchestration, and data warehouse, not as a separate tool with its own maintenance burden. Quality validation runs where the data lives.
Up to 50% Lower Cost
Certified offshore data engineers from India, same data quality framework depth as US or UK engineering firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout.
★★★★★

"Our analysts were spending roughly two days every week investigating data quality issues reported by business users. A transformation had broken, a source system had changed schema, a join was producing duplicates - and we only found out when someone noticed a number looked wrong. Numlytics profiled our entire data estate, co-designed quality rules with our business owners, and implemented over 340 automated dbt tests and a Great Expectations suite across our pipelines. In the three months after go-live, we had two quality incidents, both detected and alerted within minutes, before any downstream consumer was affected. Our analysts now spend that two days a week building analytics instead of fixing data."

LN
Lauren N.
Head of Data · SaaS Platform, United Kingdom

Data quality is built into every layer. These services are where quality rules live.

ETL Pipeline Development
Quality gates built into every pipeline we deliver
→
Data Warehouse Consulting
Quality validation inside and around the warehouse layer
→
Data Lakehouse Architecture
Quality checks at every medallion layer boundary
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Data Governance Consulting
The governance model that sustains quality standards
→
All Data Engineering Services ↗
Full data engineering service hub
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FAQ

Data Quality Management FAQs

Common questions before starting a data quality management engagement with Numlytics.

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Data quality management is the practice of measuring, monitoring, and maintaining the accuracy, completeness, consistency, timeliness, and uniqueness of data across your organisation. It involves profiling data to establish a baseline, defining quality rules with business stakeholders, automating validation in your pipelines, and monitoring quality metrics over time, so issues are caught before they reach dashboards or decision-makers.
The five core dimensions are: Completeness (are all required values present?), Accuracy(does the data correctly reflect reality?), Consistency(same data represented the same way across systems?), Timeliness (is the data fresh enough for the decisions it supports?), and Uniqueness (no duplicate records that shouldn't exist?). We design automated tests for all five dimensions across every critical dataset.
Our primary tooling includes dbt tests and dbt-expectations for transformation-layer validation, Great Expectations and Soda for comprehensive expectation suites, and Monte Carlo for data observability and anomaly detection. We also use Microsoft Purview for lineage and cataloguing, and Power BI for quality monitoring dashboards. Tool selection is based on your existing stack.
Numlytics has automated quality tests live in 3 weeks - covering profiling, rule design, and initial test implementation. A full data quality framework including monitoring dashboards, Slack alerting, lineage integration, and ownership model typically runs across a 5–7 week engagement. We profile first so every test we write reflects measured reality, not assumptions.
Yes, retrofitting quality checks to existing pipelines is one of our most common engagements. We profile your existing data, identify the highest-risk datasets, and add dbt tests, Great Expectations suites, or Soda checks to your existing pipeline runs without rebuilding anything. Quality gates are added incrementally, starting with your most business-critical data flows. See our ETL pipeline development service for combined delivery.
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Catch Data Issues in the Pipeline - Not in the Board Meeting

Get a complete data quality management framework - profiling, automated tests, monitoring dashboards, and an ownership model that sustains quality over time. 3 weeks to first tests live. Proposal in 24 hours. US, UK, Australia & UAE.

Get Free Consultation ← All Data Engineering Services
Other Data Engineering Services
ETL Pipeline Development Data Warehouse Consulting Data Lakehouse Architecture Real-Time Data Streaming Data Governance Consulting