Data Governance & Quality Services
Establishing the standards, ownership, and controls that make data trustworthy and usable across the organization.
Our Data Governance Approach
A data governance framework ensures the right people have access to the right data, that data is accurate and consistent, and that the organization understands what data it has and how it is being used.
Our approach to data governance consulting is practical, not theoretical. We focus on the policies, processes, and tooling that address real business problems. We help teams establish ownership, improve data quality management, and build operational practices that sustain governance over time.
What We Focus On:
- Establishing clear data ownership and accountability
- Improving data quality management so analytics and AI can be trusted
- Building a data catalog so teams know what data exists and where
- Defining policies and standards that teams will actually follow
- Making data governance framework implementation sustainable, not a one-time project
Build Trust in Your Data
Data Governance Framework Design
We help organizations design a data governance framework that establishes clear ownership, accountability, and decision-making authority for data across the enterprise.
What’s Included:Â
Data ownership and stewardship model design
Data governance framework roles and responsibilities
Policy and standards development
Operating model for data governance consulting delivery
Data Catalog & Metadata Management
We implement and configure data catalog solutions with full metadata management that give teams a unified view of what data exists, where it lives, and who owns it.
What’s Included:Â
Data catalog platform selection and implementation
Metadata management and documentation
Data lineage mapping and visualization
Business glossary development
Data Quality Management
We assess current data quality and implement the rules, monitoring, and processes that are core to strong data quality management at enterprise scale.
What’s Included:Â
Data profiling and quality assessment
Data quality management rule definition and implementation
Automated quality monitoring and alerting
Root cause analysis and remediation planning
Master Data Management (MDM)
We help organizations establish master data management to create a single, trusted source of truth for critical business entities such as customers, products, and vendors.
What’s Included:Â
Master data management strategy and scoping
Master data model design
Master data management platform implementation
Data matching, deduplication, and consolidation
Data Access & Privacy Controls
We implement the controls and policies at the heart of any mature data governance framework to ensure data is accessed appropriately and sensitive data is protected.
What’s Included:Â
Data classification and sensitivity tagging
Role-based access control design
PII and sensitive data identification
Regulatory alignment: GDPR, CCPA, HIPAA
Audit logging and access monitoring
Trusted by Leading Organizations
Case Studies
See Our Delivery Framework
See how we deliver real results with a proven model that brings clarity, speed, and confidence to every project.
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FAQs
Data engineering services involve designing and building the systems that collect, move, transform, and store data so it can be used by analytics and AI — including pipelines, platforms, data models, and the processes that keep data reliable.
AI models and analytics tools are only as good as the data they consume. Without reliable data pipeline development and clean, structured data, analytics are inaccurate and AI projects fail. Data engineering services create the foundation everything else depends on.
A data warehouse stores structured data optimized for reporting. A data lakehouse architecture combines the scalability of a data lake with the structure of a data warehouse, supporting both analytics and AI workloads from a single cloud data platform.
We build cloud data platform environments on Microsoft Azure, Amazon Web Services, and Google Cloud Platform, selecting the right platform based on existing infrastructure and long-term requirements.
Yes. We approach data platform modernization incrementally, assessing your current environment and identifying the changes that deliver the most value with the least disruption. Data warehouse migration rarely requires a full rebuild.
DataOps applies software engineering practices such as version control, automated testing, and CI/CD to data pipeline development. It allows data teams to build, test, and deploy pipelines faster and with fewer errors.
We implement monitoring, alerting, and testing as part of every data pipeline development project. We also establish DataOps standards that allow pipelines to be maintained without introducing new risk.
Yes. We design and build both batch and real-time data pipeline development solutions. Real-time pipelines are appropriate for operational dashboards, alerting, or real-time AI scoring.
Data quality is built into our data engineering services design, not added afterward. We implement validation, testing, and monitoring at key points so issues are caught before they reach analytics or AI systems.
A semantic layer sits between your data and analytics tools, defining consistent business metrics. It is a critical component of strong data engineering services ensuring every metric means the same thing everywhere.
We’re Ready When You Are
Whether you have a clear goal, a rough idea, or a problem that needs fixing, we’re here for it.
Fill out the form and we’ll follow up to schedule a short, focused conversation. We’ll walk through what you’re trying to achieve, share how we work, and outline the next steps to move forward with confidence.
Address
802 N. Pinyon Ct,
Hartland, WI 53029
Billing Inquiries
(866) 568-8615
Information and Sales
info@xorbix.com