Data Management
Data Quality Management Services
Find, fix and prevent bad data with measurable rules, automated pipeline checks and monitoring that catches issues before they reach reports, customers or AI models.
Trustworthy Data Starts with Measurable Quality
A missing postcode delays a shipment. A duplicate supplier receives two payments. A pipeline that silently loads half a day of transactions makes a revenue dashboard wrong for a week. Data quality problems rarely announce themselves; they surface as customer complaints, audit findings and models that drift for no obvious reason. By then the fix is expensive, because the bad data has already spread across warehouses, reports and integrations.
Hanoi Technology treats data quality as an engineering discipline. We define rules against agreed quality dimensions, automate checks inside your pipelines, publish scorecards that business owners understand, and trace recurring defects back to the process or system that creates them, so problems are prevented rather than cleaned up again and again.
The Challenge
Why Data Quality Problems Keep Coming Back
One-off clean-up projects rarely last; these are the underlying causes.
Quality Is Not Measured
Without baseline scores per dataset and dimension, nobody can say whether quality is improving or which issue matters most. Discussions remain anecdotal and budget is hard to justify.
Checks Happen Too Late
Errors are discovered by analysts or customers after data has been loaded and consumed. Fixing them then requires reprocessing, restatements and awkward explanations.
Symptoms Are Fixed, Causes Are Not
Teams correct records in the warehouse while the form, integration or manual process that created them keeps producing new errors.
Silent Pipeline Failures
Schema changes, late files and partial loads often do not fail a job outright. Volume drops and freshness gaps go unnoticed without observability on the data itself.
Business Value
The Business Value of High-Quality Data
Reports People Trust
Certified datasets with visible quality scores give executives confidence that the numbers in front of them are complete and current.
Fewer Operational Errors
Valid addresses, correct tax codes and deduplicated accounts reduce failed deliveries, payment mistakes and customer service escalations.
Earlier Detection, Cheaper Fixes
Automated checks at ingestion and transformation stop bad records before they spread. Engineers fix one source instead of many downstream copies.
More Reliable AI and Analytics
Models trained and grounded on validated data drift less and produce results that are easier to explain and defend.
Clear Accountability
Every rule has an owner and every issue a workflow, so problems are routed to the people able to fix them.
Evidence for Compliance
Quality history and issue logs provide auditors with proof that data used for regulated reporting is controlled.
Quality Is a Product Feature of Your Data
Leading data teams now treat each important dataset like a product, with documented expectations, service levels for freshness and accuracy, and monitoring that tells consumers when something is wrong. We help you adopt that mindset step by step, starting with the data that matters most.
Our Services
Hanoi Technology’s End-to-End Data Quality Services
From a first assessment to continuously monitored data products.
Data Quality Assessment
We profile priority datasets to reveal null rates, invalid formats, outliers, duplicates and referential breaks, and quantify their impact on business processes.
- Profiling report by dataset
- Issue inventory with business impact
- Prioritized remediation plan
Quality Rules and Scorecards
We translate business expectations into rules across completeness, validity, accuracy, uniqueness, consistency and timeliness, and roll them up into scorecards per domain and owner.
- Documented rule library
- Dimension-based scorecards
- Thresholds and quality targets
Automated Pipeline Testing
We embed checks into ingestion and transformation code with tools such as dbt tests, Great Expectations or Soda, so failing data is quarantined or flagged before publication.
- Tests in CI/CD and orchestration
- Quarantine and alert patterns
- Data contracts with producers
Data Observability
We monitor freshness, volume, schema and distribution changes across warehouses and lakes, with anomaly detection and lineage that shows which reports are affected.
- Freshness and volume monitors
- Schema change alerts
- Impact analysis via lineage
Cleansing and Standardization
We correct and enrich historical data with standardization, address verification, deduplication and enrichment from trusted external sources.
- Cleansing and matching jobs
- Address and contact validation
- Before-and-after quality metrics
Root-Cause Remediation
We trace recurring defects to forms, integrations or manual steps and fix them at source with validation, better defaults or process changes.
- Root-cause analysis
- Source-system fixes
- Issue management workflow
Our Approach
Our Data Quality Delivery Approach
A repeatable cycle that moves from measurement to prevention.
- 01
Identify Critical Data
We work with business owners to select the datasets and fields that drive revenue, cost or regulatory exposure, so effort goes where it counts.
Deliverables: Critical data element list · Business impact map
- 02
Profile and Baseline
We profile the data, run initial rules and publish a baseline score per dimension, giving everyone a shared, factual starting point.
Deliverables: Profiling results · Baseline scorecard
- 03
Define Rules and Targets
We agree rules, thresholds and owners with stakeholders and document them in a catalog or rule repository.
Deliverables: Approved rule library · Ownership assignments
- 04
Automate and Monitor
We implement rules in pipelines and monitoring tools, wire alerts to the right channels and build dashboards for stewards and leadership.
Deliverables: Automated checks in production · Alerting and dashboards
- 05
Remediate and Prevent
We clean historical data, fix root causes at source and review scores monthly to retire, tighten or add rules.
Deliverables: Remediation results · Monthly quality review
Use Cases
Data Quality Use Cases
Where improved data quality delivers visible results.
Customer Contact Data
An e-commerce company validates emails, phone numbers and addresses at checkout and in its CRM, reducing bounced campaigns and failed deliveries.
Financial Close Controls
A finance team runs reconciliation and completeness checks on ledger feeds before close, catching missing entries days earlier.
Product Information Quality
A distributor checks mandatory attributes, units and images before syndicating products to marketplaces, reducing listing rejections.
Healthcare and Claims Data
A health services provider validates codes, dates and identifiers in claims data to avoid rejections and support accurate reporting.
Machine Learning Feature Data
A logistics company monitors distributions of model features and alerts data scientists when inputs drift outside expected ranges.
IoT and Sensor Streams
A manufacturer detects frozen sensors, impossible values and gaps in time-series data before they distort maintenance predictions.
Technology
Data Quality Tools We Use
We select tools that fit your platform and team skills.
- Great Expectations
- Soda
- dbt Tests
- Monte Carlo
- Informatica Cloud Data Quality
- Ataccama ONE
- Microsoft Purview Data Quality
- Databricks Lakehouse Monitoring
- Delta Live Tables Expectations
- Snowflake Data Metric Functions
- Apache Airflow
- Dagster
- OpenMetadata
- Python and Pandas
- SQL
Platforms
Deliver Data Quality on Your Preferred Platform
- InformaticaEnterprise data integration, quality and governance.Explore
- AtaccamaData quality, governance and master data in one platform.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Real estate
Transparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
Read the Full StoryMore Success Stories
Logistics
−25%operational costsRoute optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Find Out How Healthy Your Data Really Is
Share the datasets that cause the most trouble today. We will reply within 24 hours and arrange a free consultation to scope a focused data quality assessment.
Data Quality FAQ
Which data quality dimensions do you measure?
We typically measure completeness, validity, accuracy, uniqueness, consistency and timeliness, and add integrity or conformity where relevant. The exact set depends on how the data is used. A marketing list cares most about validity and uniqueness of contacts, while a financial feed cares about completeness and timeliness. We define each rule against a dimension so scores can be aggregated into scorecards that business owners can read without technical knowledge.
What is the difference between data quality and data observability?
Data quality rules check business expectations, such as every order having a valid customer ID. Data observability monitors the health of data flows: whether tables arrived on time, whether row counts or distributions changed unexpectedly and whether schemas shifted. Both are needed. Observability catches pipeline failures you did not anticipate, while rules enforce what the business has explicitly agreed. We implement them together and link alerts to lineage.
How long does a data quality assessment take?
For a single domain with a handful of source systems, profiling, baseline scoring and a prioritized remediation plan usually take a few weeks, depending on access to data and stakeholder availability. Larger programs covering several domains are phased. The assessment is designed to produce a factual baseline and a ranked list of issues with estimated business impact, so you can decide where to invest before committing to a larger implementation.
Do we need a commercial data quality tool?
Not always. Teams on modern cloud platforms can go far with dbt tests, Great Expectations or Soda combined with built-in features in Databricks, Snowflake or Microsoft Purview. Commercial suites such as Informatica or Ataccama add value when you need business-friendly rule authoring, address verification, matching at scale or integration with MDM. We compare options against your requirements and existing licenses before recommending a purchase.
How do you prevent bad data from coming back?
Cleaning is only the first step. We trace recurring issues to the forms, integrations or manual processes that create them and add validation at source, data contracts with producing teams and automated checks in pipelines. Each rule has a named owner and an issue workflow, and monthly reviews retire or tighten rules. That combination of prevention, monitoring and accountability is what keeps quality from sliding back.
Can you work with sensitive or regulated data?
Yes. We profile data inside your environment wherever possible, use least-privilege access, mask or tokenize personal fields in non-production environments and follow your security policies. Quality rules can run without exporting data. We document access and processing so your security and compliance teams can review the approach, and we sign the confidentiality and data processing agreements your organization requires before work begins.