MDM Academy lessons and materials are in Portuguese. You can take the program from any country, and an English version is on our roadmap, with no committed date. The certificate is issued in Brazil, in Portuguese, and is valid in Brazil. To earn it you need to follow the lessons in Portuguese.

full program · +100 hours

Program in Data Governance: the most complete course in Brazil.

From concept to implementation: governance, quality and cleansing, master data, LGPD, metadata, data lake, SQL, Python and applied AI. More than 100 hours built by people who implement data in large companies, with university extension certificate recognized by MEC.

284

lessons in 13 tracks

MEC

university extension

+300 graduates
100+ hours of training
data governance data quality data steward MDM LGPD metadata data lake SQL and Python career growth

the context

What is data governance and why it is the career of the moment

Our impact

+124K

users already work in the 4MDG software, the same platform you get access to during the program.

+320

data projects delivered by MDM Academy instructors in industry, retail, energy and services.

Top 100

MDM Academy students work at Brazil's largest companies, from operations to data leadership.

+7 countries

4MDG operates in more than seven countries, with governance and master data projects in global operations.

Diploma

you receive a university diploma: an extension certificate recognized by MEC upon completing the program.

Data governance is the discipline that defines who is accountable for a company's data and the rules by which it is created, measured, corrected and protected. It is not a tool: it is policy, role, standard and process, supported by quality, catalog, metadata, privacy and architecture.

Every medium or large company has already accumulated enough data to have the problem. Almost none has trained people to solve it. That gap, between the amount of data and the number of professionals, opened the fastest career window of the decade. And it closes as the market matures.

+300

graduates of MDM Academy

+100h

of training, from concept to implementation

MEC

university extension certificate

large companies that trust us

Panasonic
iFood
Carrefour
General Motors
SmartFit

+160 large companies have already worked with 4MDG on data projects.

the profile

Who is this program for

01

Master data, materials or data analyst

You already live the problem from the inside. What's missing is the method, the vocabulary and the proof to become the person responsible for the solution.

02

Supply chain, logistics or procurement analyst

You know the real cost of a wrong record. It is the fastest route to a data steward job opening.

03

Financial, tax or controllership analyst

A close that does not balance is almost always a master data problem. Here you learn to tackle the cause.

04

Business or systems analyst

You already translate between business and IT. Data governance is the specialization that values exactly that.

05

Operations manager or coordinator

To structure a governance program, define roles and defend the business case before executive leadership.

06

Those who want to move into data

Without erasing the experience you already have: governance is the way into data that values process knowledge.

remote work

Data governance is one of the careers easiest to do from home

The work is defining rules, analyzing datasets, catalog and committee: everything happens in systems and meetings. That is why governance, quality and master data job openings are among those that most often appear as remote or hybrid, including at companies outside the country that hire in Brazil.

100% remote hybrid international contracts

You don't need to know how to code to get started

The program starts with concept, process and business: the territory of those who already work with master data records, procurement, supply chain or finance. SQL and Python come later, applied to real data problems and at the pace of someone who has never written a line of code. If you already code, you go straight to the technical tracks.

the complete journey

What you will learn

Thirteen tracks and 284 lessons that cover data governance in full, not just master data. Each track combines a recorded lesson, a live webinar, a lab and a project. Click a track to open its modules, and a module to read the content of each lesson.

track content

Data governance fundamentals

1 module · 8 lessons

What data governance is and what role it plays in a master data management program.

How governance solves concrete organizational problems and sustains efficient information management.

How the governance structure is designed and who the actors involved in the process are.

The responsibilities of each actor (data owner, data steward, custodian) and how each role contributes to the outcome.

How to define the policies that govern the governance structure, aligned with the organization's objectives.

Why standardize and how the standard ensures consistency and quality across the entire information chain.

How to document and organize the organization's data assets efficiently and make them searchable.

Information security principles and protection of sensitive data, ensuring integrity and confidentiality.

track content

Data management and master data (MDM)

3 modules · 23 lessons

Introduction by Paulo Cordeiro: a straight talk about data, careers and what to expect from the program.

How the program is organized, what you will master by the end and how to make the most of the tracks, webinars and labs.

What master data is, its importance in operations and in business strategies, and how the concept has evolved over time.

The “single source of truth”: why consolidate information, what benefits it brings, and how MDM acts as an orchestrator between systems.

The impact of MDM on the advance of AI, ESG and digital transformation, the growth of the global market and the demand for qualified professionals.

Multi-domain solutions, cloud, low-code, artificial intelligence and blockchain, and how these innovations open room for specialists.

The possible careers: master data analyst, data steward, implementation consultant, and the development path in each one.

The role of the hub in defining, monitoring, and standardizing essential data.

From creation and validation to archiving, with monitoring and data cleansing at every stage.

How to align strategic objectives, define responsibilities, and map processes and SLAs.

Identify gaps and inefficiencies and design the future process with stakeholders.

Turnaround, accuracy, and rework: the indicators that show whether the hub works.

How to ensure the proposed change is technically and financially sustainable.

Committee presentation, strategic approval, team training and supported go-live.

The evolution of customer service to today's tools and the effect of AI on support.

Incident logging and triage, specialized software and operational efficiency gains.

Phone, email, chat and self-service, and how to integrate them with the ticketing tool.

Ticket volume, average handling time, SLAs and channels.

Incident, request, problem, change and inquiry: how to classify and why.

The GUT matrix (severity, urgency, trend) applied to the ticket queue.

Selection criteria, market examples and the value of the knowledge base.

MTTR, AHT, first-contact resolution and volume: how to calculate and interpret them.

How to measure satisfaction, design the survey and use the results to improve operations.

track content

Data quality, cleansing and descriptive standardization of materials (PDM)

2 modules · 16 lessons

Why quality matters, how it affects operations, and which practices and technologies support reliable data.

The dimensions that make it possible to measure quality and how each one reveals a different type of problem.

Software and features for cleansing, standardization, deduplication, and validation.

Error rate, completeness, duplication, and the PDCA cycle applied to continuous quality improvement.

What data cleansing is, what it is for, and how it directly affects the efficiency and reliability of the database.

The benefits of the process and how it improves results and organizational routines.

The stages of data cleansing and the role of each in building a solid foundation.

How standardization and data cleansing ensure consistency, eliminate duplication, and ease integration between systems.

Rulebook, technical characteristics, classification tree, and workflow.

The six stages: cataloging, structuring, normalization, identification, standardization and systematization.

The rules that ensure clarity and consistency in identifying materials in ERPs and internal processes.

How to build the descriptive standard for each domain, in practice.

Applied exercise in creating descriptions that serve both operations and strategy.

The concept, its importance in data management, and the gains in consistency and accessibility.

How the concept evolved and why the right choice is decisive for database consistency.

Market taxonomies: how they help track trends, segment customers, and adjust commercial strategy.

track content

Metadata, structured and unstructured data

1 module · 7 lessons

The concept of data and its classifications: qualitative, quantitative, structured, and unstructured. The importance of data in decision-making.

How structured data is organized in tables and why text, documents, and media require advanced processing techniques.

The difference between the three and the main master data domains: people, business partners, materials and products.

Essential information, onboarding and approval, security and integration, and the role of MDM platforms and workflows.

Categorization of raw materials, products, and services; market classification tree, UNSPSC and NCM; differences between master data record maintenance in industry and in retail.

Physical locations, digital assets, employees, ledger accounts, and cost centers, less visible but equally critical to operations.

Data about data: creation date, owner, criticality. How metadata contextualizes, organizes, and enables governance.

track content

Data lake, architecture and data lineage

3 modules · 12 lessons

How MDM software enables centralized, unified management of the organization's master data.

The modules of an MDM platform and how they work together to keep data consistent.

Techniques and approaches for deploying MDM software in real-world scenarios.

The technical structure needed for a robust and sustainable implementation.

What a protocol is, what documentation is required, and how APIs connect MDM to satellite systems.

The role of modeling in creating a logical structure that organizes and defines master data.

How data is processed in large volumes and which tools turn it into visual information.

Emerging trends, including blockchain, and how they are set to transform master data management.

How to trace the complete path of data, from source to destination, and why this underpins reliability, governance, and LGPD.

How the tool maps, tracks, visualizes, and documents the information lifecycle and integrates with systems.

How it differs from the data warehouse, advantages, structures, tools and how to avoid the “data swamp”.

How the layers organize data from raw ingestion to final analysis, and the role of MDM in this chain.

track content

LGPD, GDPR and data compliance

1 module · 14 lessons

The origin of the risk concept and how mitigation techniques evolved into current practices.

Incomplete and duplicate data and leaks, and how these risks compromise decisions and operations.

How to identify and assess risks with a risk matrix and build effective action plans.

Sensitive data protection, GDPR and LGPD compliance, and access control for master data.

The five audit stages based on ISO 9001, 19011 and 27001: planning, execution, risks, report and follow-up.

Formal agreements that define how information is exchanged between systems and organizations, with standards and quality guarantees.

Legal compliance, internal policies, and building an ethical culture in how data is handled.

LGPD, GDPR, CCPA, and Privacy by Design: what each regulation requires in practice.

How environmental, social, and governance data are monitored and reported from a reliable foundation.

Clear policies, training, auditing, and secure communication channels.

The complete flow for onboarding and monitoring suppliers.

How to monitor and evaluate supplier performance throughout the relationship.

How auditing keeps the supplier aligned with the company's standard and uncovers risks.

The method for recognizing suppliers that meet or exceed the established standard.

track content

SQL and Python for data

2 modules · 16 lessons

Automate, clean and transform large master data sets with the formulas every specialist uses.

The operators that power the rules engine of any MDM platform.

AND, OR and NOT: how to create expressions evaluated as true or false to configure rules.

Comparisons that become validation and filter rules, with examples for price, stock, and category.

How SQL is applied in MDM and ETL processes: querying, joining and aggregating.

How to validate, filter, and manipulate master data in real queries.

Introduction to the language: simplicity, versatility, and why it became a must-have tool in data.

How to store numbers, text, and booleans, and manipulate data in master data management scenarios.

if, elif, and else to check values, validate fields, and categorize records.

Lists, tuples, dictionaries, and sets to organize information efficiently.

for and while to review and validate large volumes automatically.

How to organize code, reuse logic, and make your routine more efficient.

Concatenate, modify, and clean text: the foundation of programmatic data cleansing.

Create and transform lists in one line, in a compact and readable way.

Read and write spreadsheets of products and suppliers in bulk.

The libraries that automate processes and ensure database quality.

track content

SAP and ERPs in practice (material master data)

5 modules · 27 lessons

What SAP is, why it dominates master data record maintenance in large companies, and where master data lives inside it.

The difference between standard and custom objects, when to use each and how to identify them.

All versions, the role of the GUI, and how to navigate it efficiently.

How to set up multiple entries in SAP GUI, the way consulting firms do.

How to get around the system and customize the GUI for your day-to-day work.

Display and productivity settings that change the user experience.

What each environment is for and the care each one requires.

What requests are, why they matter, and the difference between Workbench and Customizing.

Field types (date, time, value) and the attention each one requires to maintain data integrity.

Save and reuse predefined data sets to speed up repetitive tasks.

How to access, view, and manage these two essential elements of the system.

How to organize access to the functions you use most.

Day-to-day operations, use of variants, and optimized navigation.

Sapscript and SmartForms, transactions SE71 and SMARTFORMS, layout testing, and field validation.

The difference between the two and how to configure jobs to automate time-consuming tasks.

Output devices, tax document generation, and spool management through SP01.

The “System” and “Help” menus: display, data export, and background execution.

View and change data directly in tables, with the care needed to avoid impacting the system.

Record and replay interactions to automate repetitive processes and bulk loads.

Monitor who is logged in, which transactions are running, and how to end a session safely.

Identify processes that affect performance and act with caution on them.

Analyze execution step by step, find errors and value deviations with breakpoints and watchpoints.

Take reports out of SAP for analysis, charts, and sharing.

Filter information, validate records, and investigate configuration or master data problems.

Mapping, standardization, and validation; the role of ETL and the importance of a contingency plan.

LSMW, S/4HANA Migration Cockpit, and Data Services: features, advantages, and limits.

From the project in Fiori to the XML template and staging tables for loading into S/4HANA.

track content

AI applied to data

1 module · 3 lessons

Which types of AI exist and where each one actually optimizes the master data management process.

Emerging trends in data platforms and why governance is a prerequisite for any trustworthy AI.

The AI track gets new lessons every month, following what actually goes into production at companies.

track content

Methodology and data project management

4 modules · 43 lessons

Specific objective, temporality, uniqueness, and the importance of detailed planning.

What a project management methodology is and how to choose the right one for each context.

PMBOK, PRINCE2, Agile (Scrum, Kanban), Waterfall, and Six Sigma: strengths and limits of each approach.

How to combine traditional and agile practices to balance structure and flexibility.

Plans, schedules, and records that organize, control, and communicate progress.

Project charter, WBS, schedule, communication plan, RACI matrix, and risk management.

Project and communication plan: scope, objective, timeline, and responsibility.

Field mapping, integration development, test plan and report.

PMO, status reports, and the handling of risks, deviations, and changes.

Customer success, assisted operation after go-live, handover, and closure document.

Organization, cost and schedule control, delivery quality, and continuous improvement.

How to define scope, mitigate risk, and control cost in a master data project.

Welcome to the client, team introduction, checklist, and kickoff meeting.

Information gathering, technical proposal, assumptions, and schedule approval with the client.

Scenario mapping, integration, testing, cutover, and data migration.

Progress tracking, data quality, risk, change, and communication with stakeholders.

Validation of deliverables, team release, lessons learned, and satisfaction assessment.

How to justify the investment, calculate ROI, and present the financial and strategic benefit.

How to communicate the idea, align stakeholders, and sustain the decision. Includes a business case template to adapt.

Error, duplication, and inconsistent description: the effect on operational and financial efficiency.

Pareto, data profiling, and Ishikawa diagram applied to the ACME S/A case.

SMART methodology, standardized processes, validation and deduplication through MDM, and team training.

How to present tangible and intangible results and prepare alternative scenarios.

Characteristics, service models, and deployment options, with the financial and operational impact of each.

Direct comparison between cloud and on-premise: control, upfront cost, and flexibility.

Proprietary, open source, freeware, shareware, and SaaS.

Accessibility, automatic updates, cost reduction and market examples.

Integration, data quality, and scalability: the criteria that set a good solution apart.

Technical knowledge, analytical skills, and communication: the profile the market is hiring for.

Company, administrator, and user portals; fields, templates, stages, businesses, and business groups.

Single sign-on, manual registration, self-registration, internationalization, and support channel.

Navigate the platform, interpret the dashboard, and create custom reports.

Create, edit, and manage instances; deactivate, annotate, and review the audit trail of each step.

Communication between users, change history, pending-approval alerts, and language switching.

Detailed search, export for analysis, and charts configured by the administrator.

Create and change many items at once, with accuracy and traceability.

Load status, error review, and direct correction in the spreadsheet for reprocessing.

Create administrators, define responsibilities, limit records by department, and block access.

Visual theme of the tenant, session expiration, and tracking of all portal changes.

Organize flows into categories, define key fields, and configure validation and external interaction for each stage.

Text, integer, decimal, date, list, and upload; character limits and regular expressions.

Conditions and actions that make a field mandatory, validate information, or restrict access.

Access by user and department, informative header and field organization in the list.

track content

Personal development and career

4 modules · 14 lessons

How to identify your behavioral profile, understand the DISC test, and use the result to improve communication, relationships, and professional performance.

How to strengthen your image and convey trust, credibility, and authority in day-to-day interactions.

Techniques for communicating clearly, reducing misunderstandings, and building healthy, productive professional relationships.

Strategies for handling pressure, controlling reactions, and deciding with more balance and emotional intelligence.

How to communicate clearly and objectively in the corporate environment and manage speaking anxiety to convey ideas with confidence and impact.

How persuasion works in the corporate environment and how to structure your communication to build understanding and buy-in.

How to negotiate strategically, seeking agreements that deliver results without creating unnecessary conflict.

How to organize a presentation clearly and strategically to drive understanding and action.

How to identify conflicts in the corporate environment and understand their origins, when departments have different goals, priorities, and ways of working.

Active listening, conversation structure, and solution focus to lead difficult conversations, promote alignment between departments, and reduce friction.

How to use conflict to identify failures, improve processes, strengthen communication, and drive growth in teams.

How to structure a profile that communicates value in a few seconds, following the case of a professional transitioning into data governance.

How the platform works, how to tune your profile, and how to use keywords and positioning to increase your visibility in the market.

Which content works, how to structure strategic posts, and how to turn learning into professional opportunity.

track content

Data Mesh

1 module · 10 lessons

The core idea of Data Mesh, where it came from and which data architecture problem it seeks to solve.

The bottlenecks of a central data team and the signs that the centralized model has stopped scaling with the company's growth.

How to transfer responsibility for data to the business areas that know it best, and what changes in the roles and boundaries of each domain.

What it means to treat data as a product, with an owner, quality, documentation, and consumers who depend on it.

The platform that lets each domain's teams publish and consume data autonomously, without depending on a central team for every request.

How to balance domain autonomy with common rules for quality, security, and interoperability, defined and enforced in a federated way.

The components of a data product: the data, the metadata, the contract, the access interfaces, and the quality indicators.

The differences and relationships between these approaches, and where MDM comes in to ensure master data consistency.

Where to start, how to choose the first domain, structure the teams, and grow adoption gradually.

Applying the concepts in a complete case. Comes with a practical exercise, which is not a lesson.

track content

Generative AI for Data Governance Analysts

12 modules · 91 lessons · about 6 hours of video

Historical context, the field's two winters and the shift to data.

What artificial intelligence is

Dartmouth and the birth of the field

The expert systems of the 1980s

The two AI winters

The turning point of data and computing power

From the lab to everyday business

Explicit rules, predictive AI and generative AI, with a criterion for when to use each.

Automation with explicit rules

Predictive AI: forecasting from history

Generative AI: creating new content

When to use each one

Weak AI, strong AI and what is just marketing

Where each type shows up in data governance

Training and testing, types of learning, overfitting and why bad data breaks any model.

What it means to train a model

Training, validation and test data

Supervised learning

Unsupervised learning

Reinforcement learning

Overfitting and underfitting

Metrics: accuracy is not everything

Why bad data breaks any model

Neuron, layers, weights, the Transformer architecture and the attention mechanism, without the math.

The artificial neuron

Layers, weights and biases

How the network adjusts its weights

Deep learning: what changes with depth

The Transformer architecture

The attention mechanism

Why the Transformer changed everything

How they generate text, the context window, why they vary and why they hallucinate.

What a language model is

Pretraining, fine-tuning and alignment

How the model chooses the next word

Temperature and why the answer varies

Context window

What hallucination is and why it happens

Open models and closed models

Multimodality: text, image, audio and tables

How meaning becomes numbers and how to find duplicates that the ERP search never finds.

What a token is

How text is split into tokens

From token to number: vectorization

What an embedding is

Similarity: the distance between meanings

Finding duplicates the ERP search cannot find

Embeddings applied to material descriptions

The limits of embeddings

Vector databases, chunking, RAG and the direct dependence on trustworthy master data.

Why the model needs your data

What a vector database is

Chunking: chunk size matters

Indexing and retrieval

What RAG is

RAG in practice: from question to answer

The direct dependence on trustworthy master data

Why AI projects fail in the master records, not in the model

Glossary, catalog, metadata, knowledge graphs and descriptive standardization.

Syntax, semantics and meaning

Business glossary

Taxonomy and merchandise hierarchy

Ontology: the relationship between concepts

The metadata AI uses

Data catalog

Knowledge graphs

Descriptive standardization of master records

Bias, explainability, LGPD, confidentiality, intellectual property and regulation.

Bias in data and in the model

Explainability and the black-box problem

LGPD and personal data inside the prompt

Confidentiality and leaks through use

Intellectual property of what AI generates

Who is accountable for AI errors

Internal AI use policy

Regulation: the AI Act and the Brazilian landscape

AI governance checklist

Context, instruction structure, working with documents and the limits of basic use.

Choosing the free tool

The role of context

The structure of a good instruction

Asking for a format: table, list, text

Working with documents

Reviewing and iterating on the answer

Checking what the AI answered

The limits of basic use

Projects, knowledge base, reusable skills, connectors and automation.

Organizing work into projects

Building a knowledge base

Reusable instructions

Skills and agents

Connectors and system access

Automating record maintenance routines

Working with spreadsheets and volume

When to stop and call a specialist

Full diagnosis of a materials master with AI support, from exploration to action plan.

ACME's material master

Exploring the database with AI support

Finding duplicates with embeddings

Diagnosing quality and completeness

Standardizing descriptions with AI support

Building the action plan

Presenting the results to leadership

Extra content every month: data migration, SAP deep dives, data lake, data lineage and masterclasses with guest specialists.

I want to learn more

the differentiator

Certification in university extension recognized by MEC

Upon completing the program you receive a university extension certificate issued by an institution recognized by the MEC: the same type of credential accepted in internal promotion processes, in public exams and for complementary hours. This is what places this program at a rare point: specialization depth, without the years and cost of a graduate degree.

Valid nationwide

Issued by a recognized higher education institution.

More than 100 hours

Course hours recorded on the certificate, useful for supplementary hours.

Portfolio, not just a diploma

You finish with data cleansing projects and a business case already done.

illustrative sample

Certificate of 100 hours, with registration and QR code validation.

career and market

Data governance: how much a professional earns

Data governance roles (governance analyst, data steward, master data specialist, data quality coordinator) reach levels well above the average of the administrative roles these professionals come from, because they combine business understanding with technical skill and are hard to hire.

I want to live my dream→

data governance compensation

up to R$ 30K

per month, in governance management positions

Data/master data analyst
Governance analyst
Data steward
Senior specialist / coordination
view salary range by level→

Illustrative figures, compiled from public job postings and market salary surveys, only to indicate the trend in the field. They do not constitute a promise of hiring or compensation.

salary range by level

Junior

R$ 5K to R$ 7K

per month

First governance position: master data entry support, running quality rules and cleansing datasets.

Mid-level

R$ 9K to R$ 12K

per month

Runs quality, catalog and metadata routines, and is accountable for master data domains.

Specialist / Senior

R$ 12K to R$ 18K

per month

Designs policies, roles and indicators. At multinationals and large banks the range can exceed R$ 20K.

Governance Manager

R$ 18K to R$ 30K

per month

Owns the entire program: committee, business case, stewardship teams and the relationship with executive leadership.

Illustrative figures, compiled from public job postings and market salary surveys. They do not constitute a promise of hiring or compensation.

who teaches

Instructors and specialists

Whoever teaches here implements data governance in large companies: the content comes from delivered projects, not from textbooks.

Paulo Cordeiro

Master data specialist · founder of 4MDG

Author of the book Master Data Management: fundamentos e aplicações. Leads governance and MDM programs in large industrial and retail companies. Responsible for the master data, data cleansing, PDM and software implementation tracks.

César Coelho

Technology and digital transformation specialist

Specialist in technology, Python, SQL and digital transformation. Works on governance policies, guidelines and structures, and is responsible for the fundamentals, metadata, catalog, privacy and compliance tracks.

Flávio Sousa

Process and innovation specialist

Works with metrics, process redesign and leading data teams. Responsible for the methodology, project management and business case tracks.

And more +10 instructors specializing in data governance and MDM support the tracks and labs.

plan

Reach your goals

Three paid formats and a free entry point, the same complete data governance content. A specialist presents the current terms, installment options and team discounts.

offer

up to 12x

Plus Program

12-month access to all thirteen tracks, masterclasses and extra lessons.

  • ✓Thirteen complete data governance tracks
  • ✓Masterclasses and extra lessons
  • ✓Live webinars and labs
  • ✓University extension certificate (MEC)
  • ✓Email support and student group
I want to join the program

Secure purchase · Instant access · 7-day guarantee

Lifetime Program

most popular

Permanent access, with all new content included forever.

  • ✓Everything in the Plus plan, with no access deadline
  • ✓Updates and new content every week
  • ✓Future new tracks already included
  • ✓Access to the 4MDG MDM platform in the labs
  • ✓University extension certificate (MEC)

Exclusive lifetime student kit

  • ★Master Data Management Book
  • ★MDM Academy Backpack
  • ★Custom bottle
I want to start a data career

Secure purchase · Instant access · 7-day guarantee

Corporate

teams

A program designed for your company's data problem.

  • ✓Tracks prioritized based on the company assessment
  • ✓Strategic masterclasses for leaders
  • ✓Closed cohort and progress tracking
  • ✓Invoice for training budget
  • ✓Terms by number of participants
Request a proposal for the team

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for free

Data Club

A free, ongoing program: daily content, access to videos and ebooks, and invitations to webinars and masterclasses. Join through WhatsApp and start studying data governance today, at no cost.

Join the Data Club

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who've done it

Testimonials of students

video testimonial

Students share how MDM Academy changed their day-to-day work in MDM and data governance

Hear from people who studied and now apply it at work. Below, written accounts from other students.

More videos on the 4MDG channel
“

The MDM Academy platform offers a rich, hands-on learning experience, with well-structured and up-to-date content. The online format is accessible and flexible, ideal for anyone looking to sharpen their skills and move forward in their career independently.

Maida Soares

MDM Academy student

“

I am very satisfied with the MDM course I am taking. The content is comprehensive and well structured, covering everything from the fundamentals of Master Data Management (MDM) to more advanced topics, such as the importance of master data and the impact of MDM on the advance of artificial intelligence. I especially enjoyed the module on metadata, which brought a new perspective on how to manage and interpret this data.

Adão Aparecido dos Santos

MDM Academy student

“

MDM Academy is giving me a deeper understanding of master data management in a light way, with rich content organized by topic, which makes it easier to absorb the knowledge. Even though I already had some MDM knowledge, being able to learn and connect theory and practice has been very good for my professional growth.

José Paulo

MDM Academy student

gallery

Watch before you decide

Three short conversations about salary, prerequisites and profile, straight from the MDM Academy channel.

up next

In data governance, you don't need advanced math

questions

Frequently asked questions about the data governance course

Did we miss something? A specialist answers on WhatsApp during business hours.

I have questions Learn more before deciding

Join the Data Club for free and receive daily content before choosing a plan.

I'm still thinking it over and want more information

No problem: not everyone decides on the first visit. The Data Club is our free entry point, on WhatsApp, with daily content, videos, ebooks and invitations to webinars and masterclasses. You follow our method from the inside, ask questions to people already working in the field, and decide on the program when it makes sense for you.

Join the Data Club
What is free university certification and who issues the diploma?

The diploma is issued by Universidade Anhanguera, as a free university extension certificate. Free university extension covers teaching activities open to the community, focused on exchanging knowledge and with no prior undergraduate enrollment required. It is aimed at the general public, with no complex selection process, and the difference from an ordinary free course lies precisely in the certification: the certificate is official, issued by a higher education institution recognized by MEC.

I have no data experience. Can I keep up?

Yes, and that is the most common profile in the cohorts. The program starts with concept, process and business, which is the ground of anyone already working with master data, procurement, supply chain, finance or systems. The technical parts come later, starting from zero and always applied to a real data problem.

Do I need to code?

Not to get started. SQL and Python come in midway through the program, explained from scratch and applied to real datasets. If you already code, you can skip straight to the technical tracks.

How much time per week do I need to commit?

Most students study between four and six hours per week and finish in a few months. Lessons are recorded and split into short blocks, so you can move forward late at night, on the weekend or during a work break, at your own pace.

Are the lessons live or recorded?

The main lessons are recorded and released from day one. In addition, there are live webinars with the specialists, guided labs and hands-on projects. If you cannot attend live, you watch the recording afterwards.

How long do I have access to the content?

It depends on the plan: Plus gives twelve months of access and Lifetime gives permanent access, including all new content published after you join. For Corporate, the term is defined together with the company.

Will I be able to change fields or jobs after the course?

The program delivers what is missing in almost every transition: method, technical vocabulary, completed projects and a certificate recognized by the MEC to support the change in the hiring process. The outcome also depends on your own moves in the market, so we do not promise a job or a salary: we show you the path our students have taken.

Can you work remotely in this field?

A good part of governance work happens in systems and in meetings: rule definition, database analysis, catalog, committee. That is why governance, quality and master data openings often appear as remote or hybrid, including at companies outside the country that hire in Brazil.

Do I get support for questions during the course?

Yes. You get email support, a student group and the live webinars, where the group's questions are answered by the same specialists who teach the lessons.

Can I try a lesson before deciding?

Yes. Ask a specialist for a free class on WhatsApp and watch a real excerpt of the program before you enroll. In addition, every plan has a seven-day guarantee: if it doesn't work out for you, you ask for a refund.

Can my company pay for my program?

Yes, and it happens quite a lot. We issue an invoice for reimbursement or the training budget, and in the Corporate format we build a custom cohort based on the number of participants and the company's data problem.

What is the difference between data governance and data management?

Governance decides and oversees: it defines policies, roles, standards and indicators. Data management executes: it creates records, integrates, models, corrects and maintains. A company can have data management without governance, and that is exactly where duplicate master data, reports that do not reconcile and unreliable BI projects show up.

How much does a data governance professional earn?

Public job openings in Brazil reach around R$ 15K for data governance specialists and coordinators, varying by region, company size and seniority. It is an illustrative market value, meant to indicate a trend, not a promise of hiring or pay.

How do I make the career move into data?

The shortest path is to start from what you already know. If you know the master data creation, procurement or supply chain process, you already understand the data from the inside: what is missing is the vocabulary, the method and the proof. The program delivers all three: governance concepts and method, hands-on data cleansing, SQL and Python, and a university extension certificate recognized by MEC to support the change in the hiring process.

I already know data governance and want to go further. What is there for me?

The CDGM, our proficiency certification: boost your career with Brazil's first proficiency certification in Data Governance. It is made for those who already work in the field and need to prove their technical level: the assessment covers definitions and operating model, quality and risk, master data and standardization, execution and project, and shows your level in each domain.

Learn about CDGM
What is the length and format of the lessons?

More than 100 hours, across thirteen tracks. The format combines recorded lessons (watched at your own pace), live webinars with the specialists, labs and simulations, and project-based learning (PBL). There is email support and a student group, plus extra content published monthly.

How much does it cost and how does payment work?

There are three formats: 12-month access, lifetime access with updates and a corporate package for teams. Current terms, installments and discounts for companies are presented by a specialist, because they vary by cohort and number of participants. Talk to a specialist and get a proposal for the format that makes sense for you.

Is there a discount for companies?

Yes. The corporate package is custom-built based on the number of participants, with tracks prioritized according to the company's data problem, strategic masterclasses and an invoice for reimbursement or the training budget. The design and price are defined together with a specialist.

Is the certificate recognized? Does it count as a postgraduate degree?

The certificate is a university extension certificate, issued by a higher education institution recognized by MEC, with the course hours recorded, accepted in internal promotion processes, in public-sector exams and as complementary hours. It is not a lato sensu postgraduate degree, and that is precisely the positioning: specialization-level depth without the years and cost of a postgraduate program.

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