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.
data governance for companies
The experience of 330+ data projects now available to your company.
We have trained more than 300 professionals in Brazil's only proficiency certification in data governance, recognized by the MEC. Now we train your entire team, with the methodology we apply in master data projects at the country's largest companies.
The problem may not be technology. It is a lack of training, process, and alignment — governance is not individual, it is organizational.
results for your company
Prepare your team for the data challenges already here.
MDM Academy turns knowledge into real execution capability. Your team learns to connect data, processes, technology and business to make better decisions, reduce risks and lead more complex projects with confidence.
01
Adopt AI safely
Prepare the company to use artificial intelligence with reliable, governed, and protected data, reducing security and privacy risks and incorrect decisions.
AITrustSecurityGoverned data
02
Tackle the new Tax Reform
Train teams to understand how master data quality, tax data, suppliers, and products affect tax calculation, tax credits, and operational compliance.
SuppliersProductsCreditsCompliance
03
Implement complex systems
Prepare the team to take an active part in ERP, MDM, CRM, and other enterprise platform projects, from requirements gathering through implementation and sustainment.
ERPMDMCRMIntegrationImplementation
04
Elevate the conversation between business and IT
Enable business professionals to discuss topics such as data architecture, integrations, data lake, BI, quality, security, and artificial intelligence with greater authority.
BusinessTechnologyData lakeBIQuality
05
Increase the efficiency of record maintenance operations
Organize processes, roles, rules, and indicators to reduce master data maintenance SLAs, eliminate rework, and deliver reliable data faster.
ProcessesRulesSLAQualitySpeed
06
Transform ideas into approved projects
Help professionals present problems, build business cases and defend data initiatives in a language leadership understands and values.
ProblemImpactBusiness caseDecision
RESULT
A trained team does not just understand governance. It turns data into results.
When business and technology share the same language, projects move faster, risks decrease, and the company is better prepared to grow.
A program that prepares your team to execute, decide, and lead with data.
More than a curriculum, MDM Academy develops the skills your company needs to structure governance, improve data quality, implement technologies, and turn knowledge into results.
100+ hours of contentRecognized by the MECMasterclasses live
AREA 01
Structure the governance
Align concepts, roles and responsibilities so all areas work with a shared view of the data.
·Creates a common language across areas
·Defines responsibilities and domain owners
·Lays the foundations of governance
In day-to-day operations, this lets people publish definitions, roles, and policies that all departments follow every day.
capabilities developed
· Creates a common language across areas
· Reduces conflicting interpretations of the same data
· Defines responsibilities and domain owners
· Lays the foundations of governance
· Improves collaboration between business and IT
problems it solves
· Each area with its own definition of customer, product, or supplier
· No owner for the data
· Decisions blocked by conflicting information
areas that benefit
Master dataProcurementSupply chainSalesIT
modules in this training area
Definition of dataThe concept of data and its classifications: qualitative, quantitative, structured, and unstructured. The importance of data in decision-making.
Structured and unstructured dataHow structured data is organized in tables and why text, documents, and media require advanced processing techniques.
Master, reference, and transactional dataThe difference between the three and the main master data domains: people, business partners, materials and products.
Business partner master dataEssential information, onboarding and approval, security and integration, and the role of MDM platforms and workflows.
Material master dataCategorization of raw materials, products, and services; market classification tree, UNSPSC and NCM; differences between master data record maintenance in industry and in retail.
Other master dataPhysical locations, digital assets, employees, ledger accounts, and cost centers, less visible but equally critical to operations.
The importance of metadataData about data: creation date, owner, criticality. How metadata contextualizes, organizes, and enables governance.
Basic concepts of data governanceWhat data governance is and what role it plays in a master data management program.
Objectives and benefitsHow governance solves concrete organizational problems and sustains efficient information management.
Governance structuresHow the governance structure is designed and who the actors involved in the process are.
Roles and responsibilitiesThe responsibilities of each actor (data owner, data steward, custodian) and how each role contributes to the outcome.
Policy developmentHow to define the policies that govern the governance structure, aligned with the organization's objectives.
Data standardsWhy standardize and how the standard ensures consistency and quality across the entire information chain.
Data catalogHow to document and organize the organization's data assets efficiently and make them searchable.
Security, regulation, and privacyInformation security principles and protection of sensitive data, ensuring integrity and confidentiality.
AREA 02
Control quality and risk
Prepare the team to identify causes, create standards, and continuously improve the reliability of the data the company uses.
·Reduces errors and rework in master data maintenance
·Establishes quality rules and indicators
·Supports compliance and risk management
In day-to-day operations, this lets people measure quality, cleanse the database, and maintain their own rulebook and PDM.
capabilities developed
· Reduces errors and rework in master data maintenance
· Improves master data quality for materials and partners
· Establishes quality rules and indicators
· Supports compliance and risk management
· Increases confidence in the information
problems it solves
· Duplicate databases and inconsistent descriptions
· Constant rework and high SLAs for master data creation
· Exposure to compliance and LGPD risks
areas that benefit
Master dataQualityComplianceTaxOperations
modules in this training area
Definition of riskThe origin of the risk concept and how mitigation techniques evolved into current practices.
Types of risks associated with master dataIncomplete and duplicate data and leaks, and how these risks compromise decisions and operations.
Risk analysisHow to identify and assess risks with a risk matrix and build effective action plans.
Data privacySensitive data protection, GDPR and LGPD compliance, and access control for master data.
Data auditThe five audit stages based on ISO 9001, 19011 and 27001: planning, execution, risks, report and follow-up.
Data contractFormal agreements that define how information is exchanged between systems and organizations, with standards and quality guarantees.
Compliance: definition and purposeLegal compliance, internal policies, and building an ethical culture in how data is handled.
Local and global regulationsLGPD, GDPR, CCPA, and Privacy by Design: what each regulation requires in practice.
ESG and its application in MDMHow environmental, social, and governance data are monitored and reported from a reliable foundation.
Compliance and ethics awareness in MDMClear policies, training, auditing, and secure communication channels.
Partner approval: pre-registration, registration, and approvalThe complete flow for onboarding and monitoring suppliers.
Partner approval: performance evaluationHow to monitor and evaluate supplier performance throughout the relationship.
Partner approval: second-party auditHow auditing keeps the supplier aligned with the company's standard and uncovers risks.
Partner approval: performance certificationThe method for recognizing suppliers that meet or exceed the established standard.
Definition of data qualityWhy quality matters, how it affects operations, and which practices and technologies support reliable data.
Data quality dimensionsThe dimensions that make it possible to measure quality and how each one reveals a different type of problem.
Data quality toolsSoftware and features for cleansing, standardization, deduplication, and validation.
Performance metrics and indicatorsError rate, completeness, duplication, and the PDCA cycle applied to continuous quality improvement.
Definition of data cleansingWhat data cleansing is, what it is for, and how it directly affects the efficiency and reliability of the database.
Data cleansing processesThe benefits of the process and how it improves results and organizational routines.
Descriptive standardization of materialsThe stages of data cleansing and the role of each in building a solid foundation.
Introduction to PDMHow standardization and data cleansing ensure consistency, eliminate duplication, and ease integration between systems.
PDM structureRulebook, technical characteristics, classification tree, and workflow.
PDM developmentThe six stages: cataloging, structuring, normalization, identification, standardization and systematization.
Short and long descriptionsThe rules that ensure clarity and consistency in identifying materials in ERPs and internal processes.
PDM processes: products, customers, and suppliersHow to build the descriptive standard for each domain, in practice.
Short and long descriptions in practiceApplied exercise in creating descriptions that serve both operations and strategy.
TaxonomyThe concept, its importance in data management, and the gains in consistency and accessibility.
Units of measureHow the concept evolved and why the right choice is decisive for database consistency.
Market classificationMarket taxonomies: how they help track trends, segment customers, and adjust commercial strategy.
AREA 03
Implement and execute
Train professionals to take part in projects, structure processes, and use technologies that turn governance into operations.
·Prepares the team for ERP, MDM, CRM, and integrations
·Improves business involvement in projects
·Enables automation and scale
In day-to-day operations, this lets people configure workflows, validate bulk loads, and automate routines with SQL and Python.
capabilities developed
· Increases the team's execution capacity
· Improves business involvement in projects
· Prepares the team for ERP, MDM, CRM, and integrations
· Brings processes and technology closer
· Enables automation and scale
problems it solves
· System projects driven only by IT
· Poorly gathered requirements and rework during implementation
· Manual routines that do not scale
areas that benefit
ITProjectsMaster dataSupply chainData engineering
modules in this training area
Definition of MDM softwareHow MDM software enables centralized, unified management of the organization's master data.
Main components of an MDM systemThe modules of an MDM platform and how they work together to keep data consistent.
Implementation modelsTechniques and approaches for deploying MDM software in real-world scenarios.
Architectural componentsThe technical structure needed for a robust and sustainable implementation.
Data integrationWhat a protocol is, what documentation is required, and how APIs connect MDM to satellite systems.
Data modelingThe role of modeling in creating a logical structure that organizes and defines master data.
Big Data and data visualizationHow data is processed in large volumes and which tools turn it into visual information.
The future of MDM softwareEmerging trends, including blockchain, and how they are set to transform master data management.
Advanced Excel formulasAutomate, clean and transform large master data sets with the formulas every specialist uses.
Mathematical logic for specialistsThe operators that power the rules engine of any MDM platform.
Logical operatorsAND, OR and NOT: how to create expressions evaluated as true or false to configure rules.
Relational operatorsComparisons that become validation and filter rules, with examples for price, stock, and category.
SQLHow SQL is applied in MDM and ETL processes: querying, joining and aggregating.
Logical and relational operators with SQLHow to validate, filter, and manipulate master data in real queries.
PythonIntroduction to the language: simplicity, versatility, and why it became a must-have tool in data.
Variables and data typesHow to store numbers, text, and booleans, and manipulate data in master data management scenarios.
Control structuresif, elif, and else to check values, validate fields, and categorize records.
Data structuresLists, tuples, dictionaries, and sets to organize information efficiently.
Loopsfor and while to review and validate large volumes automatically.
FunctionsHow to organize code, reuse logic, and make your routine more efficient.
String manipulationConcatenate, modify, and clean text: the foundation of programmatic data cleansing.
List comprehensionsCreate and transform lists in one line, in a compact and readable way.
Working with filesRead and write spreadsheets of products and suppliers in bulk.
Useful libraries for dataThe libraries that automate processes and ensure database quality.
Cloud or on-premiseCharacteristics, service models, and deployment options, with the financial and operational impact of each.
Definition of cloud computingDirect comparison between cloud and on-premise: control, upfront cost, and flexibility.
Types of software licensingProprietary, open source, freeware, shareware, and SaaS.
Definition of SaaSAccessibility, automatic updates, cost reduction and market examples.
Choosing MDM softwareIntegration, data quality, and scalability: the criteria that set a good solution apart.
The role of the implementation consultantTechnical knowledge, analytical skills, and communication: the profile the market is hiring for.
Technical and functional componentsCompany, administrator, and user portals; fields, templates, stages, businesses, and business groups.
Login screenSingle sign-on, manual registration, self-registration, internationalization, and support channel.
Sidebar menu, dashboard, and reportsNavigate the platform, interpret the dashboard, and create custom reports.
Viewing a workflowCreate, edit, and manage instances; deactivate, annotate, and review the audit trail of each step.
Header: notifications, chat, alerts, and chatbotCommunication between users, change history, pending-approval alerts, and language switching.
Advanced searches and statisticsDetailed search, export for analysis, and charts configured by the administrator.
Bulk creation and changesCreate and change many items at once, with accuracy and traceability.
Monitoring bulk processingLoad status, error review, and direct correction in the spreadsheet for reprocessing.
Administrator portal: users and departmentsCreate administrators, define responsibilities, limit records by department, and block access.
Settings and auditVisual theme of the tenant, session expiration, and tracking of all portal changes.
Business groups, businesses, and stagesOrganize flows into categories, define key fields, and configure validation and external interaction for each stage.
Field types and validationsText, integer, decimal, date, list, and upload; character limits and regular expressions.
Advanced multiple business rulesConditions and actions that make a field mandatory, validate information, or restrict access.
Permissions, header, and listingAccess by user and department, informative header and field organization in the list.
Introduction and what SAP isWhat SAP is, why it dominates master data record maintenance in large companies, and where master data lives inside it.
What is Standard, Z, or YThe difference between standard and custom objects, when to use each and how to identify them.
How to download and install SAP GUIAll versions, the role of the GUI, and how to navigate it efficiently.
Creating an entry to access customersHow to set up multiple entries in SAP GUI, the way consulting firms do.
Navigating the menus (SPRO, transactions, menus)How to get around the system and customize the GUI for your day-to-day work.
Configuring your SAP GUIDisplay and productivity settings that change the user experience.
Environments and clients (DEV/QAS/PRD)What each environment is for and the care each one requires.
Requests in SAPWhat requests are, why they matter, and the difference between Workbench and Customizing.
Filling in the information in the fieldsField types (date, time, value) and the attention each one requires to maintain data integrity.
Variants for programsSave and reuse predefined data sets to speed up repetitive tasks.
Tables and programsHow to access, view, and manage these two essential elements of the system.
Adding transactions to the main menuHow to organize access to the functions you use most.
Running programs, functions, and transactionsDay-to-day operations, use of variants, and optimized navigation.
Finding forms, invoices, and DANFEsSapscript and SmartForms, transactions SE71 and SMARTFORMS, layout testing, and field validation.
Background and foreground executionThe difference between the two and how to configure jobs to automate time-consuming tasks.
Printing informationOutput devices, tax document generation, and spool management through SP01.
Menus for detailed programsThe “System” and “Help” menus: display, data export, and background execution.
SM30: table updatesView and change data directly in tables, with the care needed to avoid impacting the system.
SHDB: process mappingRecord and replay interactions to automate repetitive processes and bulk loads.
SM04: list of logons on the serverMonitor who is logged in, which transactions are running, and how to end a session safely.
SM50: active process monitoringIdentify processes that affect performance and act with caution on them.
Debug ABAPAnalyze execution step by step, find errors and value deviations with breakpoints and watchpoints.
Exporting data to Excel, TXT, and other formatsTake reports out of SAP for analysis, charts, and sharing.
SE11, SE16, and SE16N: table dataFilter information, validate records, and investigate configuration or master data problems.
Data migrationMapping, standardization, and validation; the role of ETL and the importance of a contingency plan.
Migration tools within SAPLSMW, S/4HANA Migration Cockpit, and Data Services: features, advantages, and limits.
Creating a migration project in SAPFrom the project in Fiori to the XML template and staging tables for loading into S/4HANA.
Different types of artificial intelligenceWhich types of AI exist and where each one actually optimizes the master data management process.
AI, blockchain, and the future of data managementEmerging trends in data platforms and why governance is a prerequisite for any trustworthy AI.
Masterclasses and new monthly contentThe AI track gets new lessons every month, following what actually goes into production at companies.
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
AREA 04
Transform data into strategy
Develop professionals who can structure projects, build business cases, and connect governance to company objectives.
·Improves the defense of new projects
·Strengthens the conversation with leadership
·Turns governance into business value
In day-to-day operations, this lets people build the business case, set up the master data hub, and take indicators to the committee.
capabilities developed
· Improves the defense of new projects
· Connects data to business objectives
· Strengthens the conversation with leadership
· Helps prioritize initiatives
· Turns governance into business value
problems it solves
· Data initiatives without sponsorship
· Difficulty proving return
· Lack of a method to run the program
areas that benefit
LeadershipPMOControllershipMaster data hub
modules in this training area
Definition of projectSpecific objective, temporality, uniqueness, and the importance of detailed planning.
Definition of methodologyWhat a project management methodology is and how to choose the right one for each context.
Types of project methodologiesPMBOK, PRINCE2, Agile (Scrum, Kanban), Waterfall, and Six Sigma: strengths and limits of each approach.
Hybrid methodologyHow to combine traditional and agile practices to balance structure and flexibility.
Definition of methodological artifactsPlans, schedules, and records that organize, control, and communicate progress.
Initial phase artifactsProject charter, WBS, schedule, communication plan, RACI matrix, and risk management.
Planning phase artifactsProject and communication plan: scope, objective, timeline, and responsibility.
Execution phase artifactsField mapping, integration development, test plan and report.
Monitoring phase artifactsPMO, status reports, and the handling of risks, deviations, and changes.
Closing phase artifactsCustomer success, assisted operation after go-live, handover, and closure document.
Importance of project management methodologyOrganization, cost and schedule control, delivery quality, and continuous improvement.
The project management methodology in MDMHow to define scope, mitigate risk, and control cost in a master data project.
Starting an MDM projectWelcome to the client, team introduction, checklist, and kickoff meeting.
Planning an MDM projectInformation gathering, technical proposal, assumptions, and schedule approval with the client.
Executing an MDM projectScenario mapping, integration, testing, cutover, and data migration.
Monitoring an MDM projectProgress tracking, data quality, risk, change, and communication with stakeholders.
Closing an MDM projectValidation of deliverables, team release, lessons learned, and satisfaction assessment.
Definition of business caseHow to justify the investment, calculate ROI, and present the financial and strategic benefit.
The strategic importance of the business caseHow to communicate the idea, align stakeholders, and sustain the decision. Includes a business case template to adapt.
Identifying pain points with master dataError, duplication, and inconsistent description: the effect on operational and financial efficiency.
Collecting metrics and defining the offendersPareto, data profiling, and Ishikawa diagram applied to the ACME S/A case.
Defining corrective actionsSMART methodology, standardized processes, validation and deduplication through MDM, and team training.
Presenting resultsHow to present tangible and intangible results and prepare alternative scenarios.
Introduction and concept of the master data hubThe role of the hub in defining, monitoring, and standardizing essential data.
Master data lifecycleFrom creation and validation to archiving, with monitoring and data cleansing at every stage.
Executive and operational meetingsHow to align strategic objectives, define responsibilities, and map processes and SLAs.
Macroflow mappingIdentify gaps and inefficiencies and design the future process with stakeholders.
KPIs and metricsTurnaround, accuracy, and rework: the indicators that show whether the hub works.
Validations and feasibility analysisHow to ensure the proposed change is technically and financially sustainable.
Approval and implementationCommittee presentation, strategic approval, team training and supported go-live.
AREA 05
Communicate and lead change
Professionals prepared to communicate, negotiate, and present ideas clearly to different audiences across the company.
·Improves communication across departments
·Reduces internal conflicts
·Prepares the team to lead change
In day-to-day operations, this lets people run meetings, negotiate priorities, and defend projects with more confidence.
capabilities developed
· Improves communication across departments
· Reduces internal conflicts
· Increases professionals' autonomy
· Strengthens presentations to leadership
· Prepares the team to lead change
problems it solves
· Initiatives that die from lack of communication
· Recurring conflicts between areas
· Technical professionals left out of decisions
areas that benefit
HRCorporate universityLeadershipAll business departments
modules in this training area
Self-awareness applied to career (DISC)How to identify your behavioral profile, understand the DISC test, and use the result to improve communication, relationships, and professional performance.
Professional image and credibility in the corporate environmentHow to strengthen your image and convey trust, credibility, and authority in day-to-day interactions.
Professional behavior, posture, and ethicsTechniques for communicating clearly, reducing misunderstandings, and building healthy, productive professional relationships.
Emotional management in high-pressure contextsStrategies for handling pressure, controlling reactions, and deciding with more balance and emotional intelligence.
Clear and objective communication (managing speaking anxiety)How to communicate clearly and objectively in the corporate environment and manage speaking anxiety to convey ideas with confidence and impact.
Fundamentals of persuasion in the corporate environmentHow persuasion works in the corporate environment and how to structure your communication to build understanding and buy-in.
Negotiation and influence techniques in the corporate environmentHow to negotiate strategically, seeking agreements that deliver results without creating unnecessary conflict.
Structuring professional presentationsHow to organize a presentation clearly and strategically to drive understanding and action.
Identifying conflicts and their originsHow to identify conflicts in the corporate environment and understand their origins, when departments have different goals, priorities, and ways of working.
Mediation and negotiation techniquesActive listening, conversation structure, and solution focus to lead difficult conversations, promote alignment between departments, and reduce friction.
Turning conflicts into opportunitiesHow to use conflict to identify failures, improve processes, strengthen communication, and drive growth in teams.
A winning LinkedIn profile for data analystsHow to structure a profile that communicates value in a few seconds, following the case of a professional transitioning into data governance.
How to find job openings and get foundHow the platform works, how to tune your profile, and how to use keywords and positioning to increase your visibility in the market.
Strategic content creation on LinkedInWhich content works, how to structure strategic posts, and how to turn learning into professional opportunity.
AREA 06
Talk on equal terms with IT
Architecture, data lineage, data lake, data mesh, layers and pipelines so business areas can take part in technical discussions knowledgeably.
·Brings business and technology closer
·Improves requirements gathering
·Reduces noise in projects
In day-to-day operations, this lets people discuss integrations, traceability, and data layers with the technical team.
capabilities developed
· Brings business and technology closer
· Improves requirements gathering
· Reduces noise in projects
· Broadens understanding of architecture
· Strengthens data decisions
problems it solves
· Requirements mistranslated between departments
· Data projects without business involvement
· Technical discussions inaccessible to operations
areas that benefit
BusinessITProjectsData and analytics
modules in this training area
Introduction to data lineageHow to trace the complete path of data, from source to destination, and why this underpins reliability, governance, and LGPD.
Technical characteristics of lineage softwareHow the tool maps, tracks, visualizes, and documents the information lifecycle and integrates with systems.
Basic concepts of a data lakeHow it differs from the data warehouse, advantages, structures, tools and how to avoid the “data swamp”.
Bronze, Silver, and Gold layersHow the layers organize data from raw ingestion to final analysis, and the role of MDM in this chain.
What is Data MeshThe core idea of Data Mesh, where it came from and which data architecture problem it seeks to solve.
Why centralized architecture starts to failThe bottlenecks of a central data team and the signs that the centralized model has stopped scaling with the company's growth.
Principle 1: data ownership by domainHow to transfer responsibility for data to the business areas that know it best, and what changes in the roles and boundaries of each domain.
Principle 2: data as a productWhat it means to treat data as a product, with an owner, quality, documentation, and consumers who depend on it.
Principle 3: self-service data platformThe platform that lets each domain's teams publish and consume data autonomously, without depending on a central team for every request.
Principle 4: federated governanceHow to balance domain autonomy with common rules for quality, security, and interoperability, defined and enforced in a federated way.
Anatomy of a data productThe components of a data product: the data, the metadata, the contract, the access interfaces, and the quality indicators.
Data Mesh, Data Fabric, Lakehouse, and MDMThe differences and relationships between these approaches, and where MDM comes in to ensure master data consistency.
How to implement Data Mesh in a companyWhere to start, how to choose the first domain, structure the teams, and grow adoption gradually.
Data Mesh in practice: the ACME caseApplying the concepts in a complete case. Comes with a practical exercise, which is not a lesson.
the platform
Lessons recorded in a studio, tracks, and assessments by module
Each employee tracks progress, downloads supporting materials, and takes the proficiency assessment within their own environment.
recorded lesson
Lessons produced in a studio
Studio-recorded content organized within the learning track, with supporting material for each lesson.
Module 04 · Technology
applied lessons
Case studies and models applied to business
Each topic is demonstrated on real-world scenarios of master data maintenance, integration, and corporate data analysis.
Module 04 · Applied lessons
tracks and assessments
Progress and assessment by module
Each employee tracks their progress, downloads the materials, and takes the proficiency assessment in their own environment.
Module 07 · Data cleansing and PDM
01 / 03
format for companies
Scalable for teams of any size
✓Full access for the whole team
✓Learning at each employee's own pace
✓Centralized, standardized content
✓Closed cohort, progress tracking, and invoice for training budget
investment
The more people, the lower the cost per employee.
We build the proposal based on the number of participants and the departments involved. In a 20-minute conversation we learn about the company's data landscape and send you the cohort design.
MDM Engineering: turning data into strategic assets
For companies that need to go beyond training, 4MDG leads the master data engineering program: Maturity Map, value structuring, assisted delivery, and smart scaling, working across the people, process, and technology triad.
