Data Management
Data Management Services
Turn scattered, inconsistent data into a governed, trusted asset that your analytics, operations and AI initiatives can rely on every day.
Make Data a Dependable Business Asset
Most organizations do not suffer from a lack of data. They suffer from customer records duplicated across CRM and billing, product codes that differ between ERP and e-commerce, reports that disagree in the board meeting, and nobody who can say with confidence which number is correct. Every analytics dashboard, automation and AI model inherits these problems, and the cost shows up as rework, manual reconciliation and decisions made on instinct instead of evidence.
Hanoi Technology helps you fix the foundations. We combine data quality engineering, practical governance, master and reference data management and AI data readiness into one program, so ownership is clear, rules are enforced in pipelines rather than in spreadsheets, and the data your teams consume is documented, monitored and fit for purpose.
The Challenge
Why Data Management Programs Stall
Data management is rarely a technology problem alone; these are the obstacles we see most often.
No Clear Ownership
When a field is wrong, nobody knows who is accountable for fixing it at the source. Issues bounce between IT and the business, and the same errors return every month.
Conflicting Versions of the Truth
Customers, products, suppliers and locations are defined differently in each application. Integration teams build one-off mappings that break as soon as a source system changes.
Governance That Lives Only on Paper
Policies are written, approved and filed, but nothing in the pipelines, catalogs or access controls actually enforces them. Compliance becomes an annual scramble rather than a routine.
Data That Is Not Ready for AI
Models and copilots need well-described, lineage-traced, permission-aware data. Without that, AI pilots stall in legal review or produce answers nobody trusts.
Business Value
What Well-Managed Data Delivers
Decisions Based on One Trusted Number
Shared definitions and certified datasets end the debate over whose report is right. Leaders spend meetings on actions instead of reconciliation.
Lower Operational Cost
Fewer duplicate records, failed deliveries, billing corrections and manual clean-up tasks. Data stewards fix problems once at the source instead of repeatedly downstream.
Faster Analytics and AI Delivery
Engineers and data scientists find documented, quality-checked data in a catalog instead of rediscovering it for every project. New use cases move from idea to production sooner.
Audit-Ready Compliance
Lineage, classification and access policies make it straightforward to answer regulators and auditors under GDPR, CCPA, HIPAA or industry rules. Evidence is generated by the platform, not assembled by hand.
Better Customer Experience
A single, accurate customer view means correct addresses, consistent pricing and personalization that recognizes the same person across channels.
A Foundation That Scales
Reusable rules, reference datasets and master data services support every new system you add, so growth does not multiply data chaos.
Data Management Is the Prerequisite for AI
Every generative AI assistant, forecasting model and automated workflow is only as good as the data behind it. Organizations that invest in quality, governance and master data first find that AI projects become cheaper, faster and far easier to approve.
Our Services
Hanoi Technology’s End-to-End Data Management Services
Five connected disciplines, delivered as a single program or as focused engagements.
Data Quality
We profile your critical data, define measurable rules for completeness, validity, uniqueness, consistency and timeliness, and embed automated checks and monitoring into your pipelines.
- Data profiling and quality assessment
- Rule library and quality scorecards
- Monitoring and alerting in pipelines
Data Governance
We design a lightweight operating model with owners, stewards and decision rights, and back it with a catalog, glossary, lineage and access policies that people actually use.
- Governance operating model and RACI
- Business glossary and data catalog
- Classification and access policies
Master Data Management
We create golden records for customers, products, suppliers and other core entities using match, merge and survivorship rules, then synchronize them to the systems that need them.
- MDM style and architecture selection
- Match, merge and survivorship rules
- Stewardship workflows and integration
Reference Data Management
We centralize code lists such as countries, currencies, units, product categories and internal status codes, with versioning, approvals and crosswalks between systems.
- Reference data inventory
- Crosswalks and code mappings
- Versioned distribution via APIs
AI-Ready Data
We assess and prepare structured and unstructured data for machine learning and generative AI, covering metadata, lineage, labeling, privacy and retrieval pipelines.
- AI data readiness assessment
- Document ingestion and enrichment
- Feature and embedding pipelines
Data Management Teams
Add experienced data engineers, stewards and architects to your team, with weekly written progress updates and sprint demos.
- Dedicated or blended teams
- Managed data operations
- Knowledge transfer to your staff
Explore
Explore Our Data Management Services
- Data QualityMeasure, monitor and improve the accuracy and completeness of critical data.Learn more
- Data GovernanceClear ownership, policies, catalog and lineage that people actually use.Learn more
- Master Data ManagementGolden records for customers, products, suppliers and more.Learn more
- Reference Data ManagementGovern code lists, hierarchies and crosswalks across systems.Learn more
- AI-Ready DataPrepare structured and unstructured data for ML and generative AI.Learn more
Our Approach
How We Build Your Data Management Program
We start with the data that matters most to the business and expand from proven results.
- 01
Assess Maturity and Pain Points
We interview data producers and consumers, profile priority datasets and benchmark current practices against a recognized maturity model such as DAMA-DMBOK knowledge areas.
Deliverables: Maturity assessment · Profiling results for priority domains
- 02
Prioritize Critical Data Elements
Together we identify the customer, product, financial or operational fields that drive revenue, cost or compliance, and agree measurable targets for each.
Deliverables: Critical data element list · Quality and governance targets
- 03
Design the Operating Model and Architecture
We define roles, decision rights and workflows, then select the catalog, quality, MDM and integration components that fit your stack and budget.
Deliverables: Operating model and RACI · Target architecture and tool selection
- 04
Implement in Iterations
We deliver one data domain at a time, with rules, golden records, lineage and dashboards live in production before moving to the next.
Deliverables: Working rules and pipelines per domain · Stewardship dashboards
- 05
Operate and Improve
We track quality scores, issue resolution times and adoption metrics, and tune rules and processes as the business changes.
Deliverables: Monthly data health report · Continuous improvement backlog
Use Cases
Data Management in Practice
Typical problems our data management engagements address.
Single Customer View for Retail
A retailer with separate store, e-commerce and loyalty databases merges duplicate customers into one profile. Marketing stops sending three emails to the same person and personalization finally works across channels.
Product Data for Marketplaces
A distributor standardizes product attributes, units and categories before syndicating to online marketplaces. Listing rejections fall and new products go live faster.
Regulatory Reporting in Financial Services
A lender traces every figure in a regulatory report back to source systems with documented lineage. Audit questions are answered in hours instead of weeks.
Post-Merger Data Consolidation
After an acquisition, two companies harmonize customer, supplier and chart-of-accounts data so finance and sales can operate on one set of records.
Knowledge Base Preparation for AI
A SaaS company cleans, deduplicates and tags thousands of support articles and tickets with ownership and freshness metadata before launching a support copilot.
Supplier Master for Procurement
A manufacturer consolidates supplier records across plants, revealing duplicate vendors and enabling volume-based negotiation and accurate spend analytics.
Technology
Technologies We Work With
We are tool-agnostic and build on the platforms you already own wherever possible.
- Informatica IDMC
- Ataccama ONE
- Microsoft Purview
- Collibra
- Alation
- Databricks Unity Catalog
- Snowflake Horizon
- Great Expectations
- Soda
- dbt
- Apache Airflow
- Reltio
- Profisee
- OpenMetadata
- Azure Data Factory
- AWS Glue
Platforms
Deliver Data Management 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
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.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
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.
Fintech
+162%monthly active usersAll-in-one digital banking app for a traditional bank
A traditional financial institution wanted to compete with neobanks for mobile-first customers. We replaced a set of separate banking, investing and payment apps with one product that people use every day.
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.
Start Building a Trusted Data Foundation
Tell us which data problems cost you the most today. We will reply within 24 hours and set up a free consultation to outline a practical first step.
Data Management FAQ
Where should a data management program start?
Start with a small number of critical data elements tied to a clear business outcome, such as reducing failed deliveries or speeding up month-end close. We profile that data, agree targets with business owners and deliver measurable improvements within a few months. Starting enterprise-wide with policies and committees usually produces documents rather than results, so we prefer to expand domain by domain once value is proven and the operating model has been tested in practice.
How is data management different from data governance?
Data governance defines who decides, which policies apply and how compliance is verified. Data management is the broader set of disciplines that execute those decisions: data quality, master and reference data, metadata, integration, security and lifecycle management. Governance without management stays on paper, and management without governance drifts into inconsistent local fixes. Our programs deliver both together, with governance decisions enforced directly in pipelines, catalogs and access controls.
Do we need to buy new software?
Not necessarily. Many organizations already own capable tools in Microsoft Purview, Databricks Unity Catalog, Snowflake or their ETL platform but use only a fraction of them. We first assess what you have, then recommend additions only where there is a clear gap, such as a dedicated MDM hub or quality monitoring tool. Where open-source options like Great Expectations, Soda or OpenMetadata fit, we include them in the comparison.
What drives the cost of a data management engagement?
The main cost drivers are the number of data domains and source systems in scope, the condition of the data, licensing for any new platforms, the degree of integration with operational systems, and how much stewardship work your team can absorb. A focused assessment and first domain is a contained project; an enterprise-wide MDM rollout is a multi-phase program. We provide a phased estimate after discovery so you can fund the work incrementally.
How long until we see results?
A maturity assessment and prioritized roadmap typically takes a few weeks. Quality rules and dashboards for a first domain can be in production within the following one to three months, depending on access to source systems and business availability. MDM and enterprise governance take longer, but we structure them so each release delivers usable improvements, such as a deduplicated customer list, rather than waiting for a big-bang launch.
How do you work with our internal teams?
We work as an extension of your data, IT and business teams, either as a project, a dedicated team or a managed service. You receive a written progress update every week and a demo at the end of every sprint. We document rules, models and processes as we go and run knowledge-transfer sessions so your stewards and engineers can operate and extend the solution confidently after handover.