Data Quality, Master Data and Metadata Management Corporate Training
Give every team one trusted source of customer, product, and reference data.
Your team masters data quality, master data management, and metadata. Employees learn to profile data, build golden records, and catalog metadata so analytics runs on trusted data.
Instructor-led group trainingVirtual / on-site / hybrid10+ languagesDelivered in English, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian, and more100+ countriesWe have delivered in United States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a trusted data team
EDSTELLAR DATA QUALITY CAPABILITY MODEL
Diagnose data quality gaps
Skill checks map your team's profiling, modeling, and governance habits.
Standardize data definitions
One shared playbook for quality dimensions, master domains, and metadata standards.
Practice in guided labs
Labs cover profiling, matching, cataloging, lineage, and quality monitoring.
Measure quality gains
Scorecards track each employee's data stewardship skill before the program ends.
Scale governance across domains
One trusted-data standard rolls out across functions, regions, and business units.
Expert trainersNeed more than one program?Get a group quote
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Program Overview: What is Data Quality, Master Data and Metadata Management?
Data Quality, Master Data & Metadata Management is an instructor-led corporate training program for teams that need trusted data behind every report and dashboard. Over 24 hours, participants learn to profile and cleanse data, design master data models, build golden records with matching and survivorship rules, and manage metadata with catalogs and lineage. It covers the full trust stack, from raw tables to governed domains.
Each module pairs concise instruction with labs on realistic messy data: teams profile quality issues, configure matching rules, merge duplicates into golden records, and document lineage in catalogs. Instructors review every lab, coaching participants through survivorship decisions, metadata standards, and monitoring design before the cohort advances. Practice compounds across the program toward the capstone, where teams build a governed data domain end to end. Exercises use the organization's own data shapes and quality pain points.
Dirty data is a quiet tax on every team: duplicate customers, conflicting product codes, and reports nobody believes. This program teaches teams to fix trust at the source, with master data discipline, quality rules that run continuously, and metadata that explains where everything came from. They return owning data quality instead of apologizing for it.
The capstone asks each team to build a governed data domain from scratch: profiled sources, a master data model with golden records, quality monitoring with remediation workflows, and a metadata catalog with lineage, presented for instructor review. The grading mirrors what stakeholders care about, can the business trust this domain on Monday morning. Graduates take home matching rule templates, quality dashboards, and a governance playbook for their next domain.
Six modules and 24 hours: data quality profiling, master data fundamentals, matching and survivorship, metadata catalogs and lineage, quality engineering and monitoring. Every module pairs concepts with a guided lab, so each technique is practiced. It closes with a capstone governed data domain the team plans, builds, and presents.
Delivery is hybrid across virtual and onsite sessions, scheduled around your calendar
What's Included in this Workshop for Your Enterprise Teams
- 1
Data quality profiling labs
Profile datasets and score quality across six dimensions.
- 2
MDM design practice
Model master domains and assign ownership and stewardship.
- 3
Golden record builds
Write matching rules and apply survivorship to duplicates.
- 4
Metadata cataloging sessions
Build glossaries, catalogs, and lineage views your teams use.
- 5
Quality monitoring setup
Deploy rules, alerts, and remediation workflows in labs.
- Instructor-led delivery across virtual, onsite, and hybrid
- Guided labs in every module with instructor coaching
- Capstone governed data domain, reviewed with your team
- Flexible scheduling around your calendar
- Lab guides and reusable quality rule templates included
- Group size and batches set along with you
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Data Analysis Exercises, Led by Industry Experts
Data Quality, Master Data and Metadata Management Course Topics and Program Outline
A tailored 24-hour, six-module workshop: one learning journey from data profiling to a capstone governed data domain. Sessions pair concepts with labs on real business data. Materials include lab guides and reusable quality rule templates for every participant.
Built by Edstellar with data governance practitioners
Built by practitioners who design master data hubs and quality frameworks in production, shaped around your stack and use cases.
Skill checks map your team's data profiling, modeling, and governance habits.
Live sessions teach quality dimensions, master data concepts, and metadata models.
Guided labs build matching rules, golden records, catalogs, and lineage views.
A capstone governed data domain proves the team can run quality end to end.
From data profiling to a capstone governed data domain, every technique is practiced in guided labs.
Delivered as
Delivered as a hybrid program of live instruction and guided labs, scheduled around your team's calendar in flexible batches.
Profile a sample customer dataset and flag quality issues.
learnData quality dimensions defined
Profiling data with statistical summaries
Detecting duplicates and anomalies
Reading a data quality scorecard
Map master data domains and assign ownership for a case company.
learnMaster data domains: customer, product, location
MDM styles: registry, coexistence, centralized
Data ownership and stewardship roles
Governance policy essentials
Build matching rules and produce golden customer records.
practiceDeterministic and probabilistic matching
Designing merge rules and thresholds
Survivorship: choosing the best value
Handling hierarchies and relationships
Build a catalog entry and trace lineage for a reporting pipeline.
practiceBusiness glossaries and data dictionaries
Building a searchable data catalog
Tracing column-level data lineage
Metadata-driven access and policy control
Deploy quality rules with monitoring and an alert workflow.
applyWriting testable data quality rules
Automating validation in pipelines
Quality dashboards, alerts, and SLAs
Root cause analysis and remediation
Deliver a governed data domain with quality evidence, reviewed live.
applyDefining the domain scope and owners
Designing the MDM and quality blueprint
Building catalog, rules, and golden records
Measuring quality lift with scorecards
Presenting the governed domain package
Customize This Program Tailored to Your Team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Seven job-ready outcomes your team will demonstrate by the end of the program. Each competency is practiced in guided labs and reviewed in the capstone build.
Profile data quality across dimensions
Run profiling checks and score datasets for accuracy, completeness, and consistency.
Define data quality rules and thresholds
Write testable rules that catch bad data before it reaches reports.
Model master data domains
Design customer, product, and location domains with clear ownership.
Implement matching, merging, and survivorship
Build golden records from duplicates with transparent merge logic.
Build a metadata catalog with lineage
Publish glossaries and trace data from source to dashboard.
Monitor quality with scorecards and alerts
Automate validation checks and respond to quality SLAs.
Govern data through policy and stewardship
Apply policies, roles, and remediation workflows that keep data trusted.
Teams that Benefit from this Data Quality, Master Data and Metadata Management Group Training
This group training is built for teams that own customer, product, and reference data and want one trusted source of truth. Content adapts to each cohort's roles, tools, and experience, so every batch practices the workflows it owns, from profiling to governance.
Reliable pipelines for data engineering teams
Data engineers learn to embed quality checks and master data services into the pipelines they build and maintain.
Workflow friction
- Duplicate records polluting downstream tables
- Untested quality rules failing silently
- Unclear stewardship when issues surface
How the program helps
- Fewer broken pipelines from bad source data
- Reusable matching and validation components
- Clear ownership for every master domain
Practice use cases
- pipeline validation
- master data services
- deduplication jobs
Trusted inputs for analytics and BI teams
Analysts learn to work from governed golden records and cataloged metadata, so every dashboard rests on trusted data.
Workflow friction
- Conflicting customer counts across reports
- Undocumented metric definitions
- No lineage for audit questions
How the program helps
- Dashboards that agree across teams
- Faster insight discovery with clean inputs
- Lineage that explains every metric
Practice use cases
- governed datasets
- metric definitions
- audit-ready lineage
Working policies for governance teams
Governance teams learn to turn policies into executable rules, catalogs, and stewardship workflows that people follow.
Workflow friction
- Policies that live only in documents
- Stewardship with no tooling support
- Catalogs that go stale after launch
How the program helps
- Policies enforced through automation
- Stewardship roles with clear accountability
- Evidence packs for audits and reviews
Practice use cases
- policy automation
- stewardship workflows
- compliance evidence
Clean master data for operations teams
Operations teams learn to keep the customer and product records they touch clean, current, and consistent.
Workflow friction
- Messy records entered at the front line
- Product data out of sync across systems
- No feedback loop for data issues
How the program helps
- Fewer customer record errors at the source
- Consistent product data across channels
- Faster onboarding to data workflows
Practice use cases
- record hygiene
- product data sync
- issue reporting
Cohorts train as a cross-functional workforce, aligned on shared data definitions, quality standards, and stewardship roles.
Prerequisites: This program benefits data engineers, analysts, and governance teams who work with customer, product, and reference data. Basic SQL or spreadsheet comfort helps but is not required, and no prior MDM experience is needed. Content adapts to each cohort's data sources, tools, and experience level.
Data Analyst to Data Steward
Tracks how data professionals progress from basic profiling to advanced data stewardship across the program's learn, practice, and apply phases.
A program that grows with data careers, from first data profile to confident data stewardship.
Different Teams, Locations, Time Zones, or Languages? We've Got You Covered.
Whether your teams are spread across countries, time zones, languages, or experience levels, we deliver tailored training programs that ensure a consistent learning experience across your global workforce.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Onsite, Virtual and Hybrid
Live instructor-led delivery with guided labs in every module, scheduled around your team's calendar in flexible batches.
Onsite Training
The trainer comes to you: teams practice on real data in the room with direct instructor coaching.
- Instructor at your location
- Labs on your own datasets
- Team whiteboard working sessions
- Scheduled around your calendar
Live Virtual Training
Fully live virtual delivery with screen-shared labs, breakout practice groups, and instructor reviews.
- Live sessions, no recordings only
- Guided virtual lab environments
- Breakout groups for practice
- Same capstone, delivered online
Hybrid Training
Mix virtual sessions with onsite lab days so distributed teams learn together and practice hands on.
- Virtual sessions for concepts and demos
- Onsite lab days for deep practice
- One schedule across locations
- Consistent materials everywhere
Need a Customized Training Format That Works for Your Team?
Our training delivery is customized to your team's schedule, full days, half days, sessions spread across weeks, or modular formats. Share your preferred timeline, and we'll plan the training around it.
Frequently Asked Questions
Share your team size and goals through the request form; we respond with a tailored proposal and group quote.
Yes. A short call aligns the program with your data sources, tools, and governance goals before scheduling.
Yes. Live virtual delivery covers all six modules and labs, with the same capstone reviewed online.
Yes. Onsite batches bring the trainer to you, with labs run on your own datasets and systems.
The program runs 24 hours across six modules, scheduled in flexible batches around your team's calendar.
Question Not Answered Here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Data quality work sits right after governance basics: once owners and policies exist, your team can measure and fix the data itself. This course covers that step, then the path moves quality checks into engineered pipelines and finishes with preparing data that is fit for AI.
SET GOVERNANCE BASICS
Data Governance Fundamentals Training
Set ownership, policies and stewardship first, so quality issues have a clear owner and a rule to be judged against.
MANAGE DATA QUALITY
Data Quality, Master Data & Metadata Management Training
Measure and improve data quality, align master data across systems and manage the metadata that explains what each field means.
ENGINEER QUALITY INTO PIPELINES
Data Contracts & Data Quality Engineering Training
Build the same quality rules into pipelines with data contracts, so problems are caught before they reach consumers.
GO FURTHER: AI-READY DATA
Data Readiness for AI: Quality, Lineage & Consent Training
Apply quality, lineage and consent checks to the data your team plans to use for training and running AI.
Why Choose Edstellar as Your Data Quality, Master Data and Metadata Management Corporate Training Provider
When comparing providers, Edstellar is the training partner teams choose for data expertise and 5+ years of corporate training experience. Our methodology blends concise instruction with guided labs, and we share each trainer profile before booking.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content maps to your data sources, master domains, and governance goals, so every lab mirrors your team's real data work.
Proven Methodology
Learn, practice, apply: concepts are taught, drilled in guided labs, then proven in a capstone governed data domain.
Practice-Based Learning
Every module includes a guided lab on profiling, matching, cataloging, or quality monitoring, reviewed with instructors.
Expert Trainers
Sessions are led by data practitioners who build MDM hubs and quality frameworks in production, matched to your stack.
Global Scale
One data quality standard rolls out across functions, regions, and time zones with consistent labs and materials.
Measurable Outcomes
Quality scorecards and capstone reviews track each employee's data stewardship skills before the program closes.
Build Trusted Data Domains With Matching Rules and Traceable Metadata
Edstellar connects data profiling with master-data ownership, golden records, and metadata management. Participants test matching and survivorship decisions, trace reporting lineage, and develop quality monitoring. The final governed domain is reviewed for consistent rules, clear accountability, and evidence that records and metadata support the intended reporting and operational use.
- Step 01
Consult
We explore customer and product data responsibilities to identify gaps in profiling, domain ownership, record matching, survivorship decisions, and metadata traceability.
- Step 02
Plan
We establish quality dimensions before master-data domains and golden records, then catalog lineage and monitored rules within the complete governed domain.
- Step 03
Align
We align master-data instruction with engineers, analysts, and governance teams, matching SQL support and tool examples to their record-management and stewardship responsibilities.
- Step 04
Customize
We tailor sample customer duplicates, product records, and reporting pipelines to relevant domains while making match criteria, ownership, and survivorship assumptions explicit.
- Step 05
Execute
Participants profile records, test merge rules, and document lineage, receiving feedback on false matches, survivorship logic, quality thresholds, and alert ownership.
- Step 06
Evaluate
We assess the governed domain for justified matching rules, consistent golden records, traceable metadata, and observable quality controls with clear remediation responsibilities.
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→Managed training services
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→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Data Quality, Master Data & Metadata Management Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request a Quote for Data Quality, Master Data and Metadata Management Training for Your Team
Share your team size, data sources, master data domains, and timelines to receive a tailored training plan and quote. Book a discovery call to discuss your goals; we confirm availability and respond with a proposal.