Online professional certificate
Professional Certificate in Data Quality & Data Governance
Make important data understandable, owned, testable and reviewable through practical quality rules, metadata, lineage, issue management and governance routines.
- Format
- Online, self-paced
- Study time
- Up to 1 month
- Curriculum
- 20 applied lessons
- Language
- English
Practical capability
Turn uncertain data into reviewable decisions.
Make ownership, meaning, data movement and quality results clear enough for a business owner to act. Six connected capabilities take you from a bounded dataset to an operating governance rhythm.
Confirm the data use, named owner, steward, custodian and approval route for one bounded asset.
Write critical field meanings and a catalog entry that exposes users, quality context and unknowns.
Trace a field from source to dashboard and assess what a status or reference change could affect.
Profile the intended population, specify a rule and retain reproducible run and exception evidence.
Show coverage and impact, test competing causes, retest a correction and record the owner decision.
Run a practical cadence for master data, exceptions, access handoffs, training and automation review.
Who this course is for
A practical route into data stewardship.
For early-career and transitioning professionals who need to make one important dataset understandable, testable and dependable for an authorized business decision.
The operating cycle
Move from a data question to an owner decision.
Follow one connected fictional service-data problem through definitions, rules, issue evidence, verified change and the routines that keep the result useful.
Curriculum
Four modules. Twenty applied lessons.
Clarify the Data and Confirm Ownership
Alder Bay's service leaders want a reliable answer about completed work orders, but the same status is interpreted differently by Dispatch and Analytics. You will begin with the decision the dashboard supports, the records it covers, and the people who can explain and approve its meaning. This gives the work a clear boundary before anyone writes a rule or changes data.
01 Define the Data Use and Boundary
From a consumer question, identify one authorized data asset, critical elements, population, intended decision, exclusions and boundaries.
Five practical steps
- Ask what the result will change
- Select a bounded asset and known path
- List candidate critical elements
- Define population, period, and exclusions as proposals
- Check permitted evidence and consumers; write the brief and seek the owner’s decision
Primary deliverable: dataset-use boundary brief.
02 Map Data Owners and Stewards
Identify accountable owner, working steward, technical custodian and decision forum for a bounded asset without inventing authority.
Five practical steps
- Bound the asset and decision
- Read approved role evidence
- List the decisions and work
- Map roles to each action
- Resolve or escalate ambiguity; record confirmation and review
Primary deliverable: owner–steward decision map.
03 Plan Safe Access and Evidence
Plan permitted evidence collection and route access/classification questions before touching sensitive records.
Five practical steps
- State the approved or proposed use
- Identify the minimum evidence
- Check classification and handling
- Select the approved environment and route; name each approver separately
- Verify the actual state; maintain the record
Primary deliverable: authorized evidence and access-route record.
04 Document Critical Data Definitions
Document a critical data element and resolve competing business definitions through the correct owner.
Five practical steps
- Name the use and element
- Collect current interpretations
- Write positive, negative and edge examples
- Draft the candidate definition and constraints
- Route the decision; publish and schedule review
Primary deliverable: critical-element definition and conflict note.
05 Maintain a Useful Data Catalog Entry
Maintain a catalog entry that connects business, technical, quality and operational context while exposing unverified fields.
Five practical steps
- Identify the asset and bounded use
- Bring in approved meaning and roles
- Record technical facts with sources
- Add quality and operational context; check classification and access labels
- Test the entry with a new reader; publish through the approved route and set review
Primary deliverable: catalog metadata entry.
Trace and Test the Data
The dispatch application, warehouse table and dashboard do not always tell the same story. A new status value and an incomplete lineage view create a practical question: which data changed, and who might rely on the result? You will follow a critical field from its source to a consumer and mark the gaps that still need evidence.
06 Trace Source-to-Consumer Lineage
Trace a critical element from source through transformation to consumer and mark missing lineage as unknown.
Five practical steps
- Start with the consumer decision
- Identify the critical element at the consumer end
- Move upstream one material hop at a time
- Compare the route with a permitted test
- Mark unsupported segments openly; summarize for the decision maker and plan review
Primary deliverable: source-to-consumer lineage trace.
07 Assess Change Impact Across Consumers
Assess who and what a definition, source, rule or transformation change can affect.
Five practical steps
- Define the exact change
- Trace the critical element
- List affected controls
- Find consumers and decisions
- Rank pre-use and post-change checks; route a bounded recommendation and notice
Primary deliverable: change-impact map.
08 Profile and Reconcile a Data Population
Profile and reconcile an authorized population reproducibly before proposing a quality rule or correction.
Five practical steps
- State the business question and authorized population
- Fix grain, period and cut-off
- Profile raw counts
- Reconcile on the same keys
- Challenge alternative explanations; report observation and uncertainty separately
Primary deliverable: profiling and reconciliation evidence sheet.
09 Specify a Testable Quality Rule
Specify a data quality rule with business purpose, exact population, logic, threshold, frequency, owner and response.
Five practical steps
- Name the business purpose
- Obtain the owner-approved meaning
- Define the eligible population
- Write expected logic and boundaries
- Set threshold, run evidence and response; test cases and implementation
Primary deliverable: quality-rule specification.
10 Run and Review Quality Tests
Run or review a rule result, distinguish defect from rule-design or population error, and retain auditable test evidence.
Five practical steps
- Read the approved specification
- Inspect the actual run configuration
- Run planned positive, negative and boundary cases
- Compare source and target on the same keys
- Challenge false positives and timing; record and route a bounded decision
Primary deliverable: rule-run and exception test log.
Resolve Quality Issues with Evidence
A quality monitor reports a failure, but an alert is only the start of the work. Service leaders need to know which orders and decisions are affected, whether the rule covered the intended records, and what should happen next. You will turn the signal into an issue that has an owner, a priority proposal and safe evidence.
11 Build a Quality and Coverage Scorecard
Report quality status with rule coverage, numerator/denominator, trend and blind spots rather than a misleading single score.
Five practical steps
- State the decision and the rules
- Reconcile the eligible and monitored populations
- Calculate the outcome with visible denominators
- Test whether the comparison is fair
- Connect results to open work and decisions; review readability and reproducibility
Primary deliverable: quality and coverage scorecard.
12 Intake and Triage a Data Issue
Register and triage a data quality issue with safe evidence, affected use, owner, proposed severity and next action.
Five practical steps
- Receive and preserve the trigger
- Bound the symptom and population
- Check for duplicates and neighboring problems
- Record the business use and provisional impact
- Route people and proposed priority; set the next evidence and review point
Primary deliverable: issue intake and triage record.
13 Assess Business Impact and Priority
Assess the operational impact of a defect and communicate a bounded recommendation to affected people.
Five practical steps
- Name the decision and deadline
- Build an evidence boundary
- Describe impact without extrapolation
- Propose priority using local criteria
- Offer bounded options; request a decision and set the next review
Primary deliverable: impact and severity decision brief.
14 Investigate Root Cause with Evidence
Separate root-cause hypotheses from verified causes using source, transformation, lineage and retest evidence.
Five practical steps
- Restate the symptom in a testable direction
- Draw the minimum source-to-consumer path
- List competing explanations
- Request authorized discriminating tests
- Look for contrary evidence and control cases; conclude at the level the evidence supports
Primary deliverable: root-cause hypothesis and evidence log.
15 Verify Remediation and Close an Issue
Coordinate an approved correction, retest the defined population and downstream use, and close only with owner decision.
Five practical steps
- Read the approved change and original rule
- Plan positive, negative and control tests
- Observe implementation through local change control
- Repeat the original quality test
- Check the consuming view and other paths; route the closure decision and communicate precisely
Primary deliverable: remediation, retest and closure record.
Operate Governance Day to Day
Trust in data does not end when one issue is closed. New master records, changing definitions, access requests and automated tags create continuing decisions for owners and stewards. You will practise a working governance rhythm that keeps standards, exceptions and responsibilities current without turning every question into a meeting.
16 Assess Master-Data Changes
Control a master or reference data change within the assigned domain without assuming enterprise-wide MDM ownership.
Five practical steps
- Define the requested change
- Confirm the authoritative record
- Trace downstream consumers
- Test old, new and boundary cases
- Route approval and define acceptance; specify recovery and follow-up
Primary deliverable: master-data change assessment.
17 Run the Governance Operating Rhythm
Turn approved data governance policy into a workable owner/steward workflow, forum cadence and evidence trail.
Five practical steps
- Name the domain and decision
- Map roles and reserved decisions
- Define intake and triage
- Choose rhythm and triggers
- Specify records and follow-up; test the rhythm with two unseen requests
Primary deliverable: governance operating charter and cadence schedule.
18 Document Exceptions and Access Decisions
Document an exception, risk owner, permitted period and access-decision state without mistaking request approval for working permission.
Five practical steps
- State the two decisions separately
- Attach observed evidence safely
- Prepare report-use options
- Route access through the specialist
- Set owner, period and review; communicate exact states
Primary deliverable: exception and access-decision record.
19 Train and Handoff to Data Users
Handoff an adopted standard to data creators and consumers and check that they can act on it.
Five practical steps
- Define the audience and their first action
- Confirm the approved source
- Write the minimum useful explanation
- Show a normal and an exception case
- Collect a teach-back and feedback; publish through the approved route and set review
Primary deliverable: steward training and handoff brief.
20 Verify Automation Before Trusting It
Evaluate automation or AI-assisted governance output against asset coverage, product maturity and human decision rights.
Five practical steps
- Define the exact output and decision
- Check feature maturity and deployment
- Define eligible and excluded catalog assets
- Run a controlled asset-level dry test; investigate errors and suggestions
- Confirm permission, owner and reversal; choose a bounded disposition
Primary deliverable: automation coverage and human-review checklist.
Applied capstone
Recommend a sound service-data decision.
Choose the course methods relevant to the evidence and prepare one coherent recommendation for the business owner.
The situation
Alder Bay Equipment Services is preparing a month-end service reliability report after a service-region mapping update. A bounded synthetic extract shows region and completion-time gaps, while managers need to decide whether the current dashboard can support customer follow-up and planning.
Your task
Prepare a decision-ready recommendation for the Service Operations Manager: use the dashboard, use it with stated conditions, or hold it. Show the tested population, quality results, missing business-completion evidence, affected use, owner decisions and a practical correction and retest route.
The people behind MTF
Meet MTF faculty and the learner community.
Explore the professional backgrounds of MTF faculty and learn more about the international community studying with the Institute.
Enrollment
Enroll in Professional Certificate in Data Quality & Data Governance
One-time course price: €10, including applicable taxes. Payment is processed securely by Stripe. No card details are stored on the MTF Institute website.
You will receive an email with access to the course. If you have any difficulties, please write to welcome@gtf.pt.
Questions and details
Frequently asked questions
Open the sections that matter to you, including delivery format, AI-supported practice and the evidence used to design the curriculum.
Who is this data quality and governance course for?
The course is designed for early-career data analysts, business data stewards, data operations practitioners and adjacent professionals who need a practical route into bounded ownership, quality and governance work. It starts with everyday business decisions and builds toward rules, issue resolution and operating routines.
How does the course work?
The course is online and self-paced, with four modules, 20 applied lessons and one capstone. Each lesson connects explanation, a practical method, a fictional workplace case, a blank template and a completed example. Learners can work through the material over up to one month at a pace that suits their practice.
How is AI used in the practical work?
Every lesson includes a prompt to draft its exact work product and a separate prompt to challenge that draft. Learners compare suggestions with supplied facts, record uncertainty and use their organization’s approved tools and data-handling rules. Named owners and specialists retain their decision and approval roles.
What evidence supports the curriculum?
The curriculum was derived from a purposive set of 100 directly verified U.S. employer postings and a separate review of current changes in metadata, lineage and quality operations. The vacancy counts describe that selected sample, not national prevalence. The research report has an open Zenodo DOI record.
What practical work will I complete?
You will practise a dataset-use boundary brief, owner–steward map, definitions and catalog entry, lineage and change records, profiling and quality-rule evidence, a scorecard, issue and remediation records, and a governance operating rhythm. The capstone brings relevant methods together in one Data Trust Brief for an owner decision.
What do data ownership and governance mean in this course?
Ownership identifies who can approve a business meaning, permitted use, priority or exception for a bounded data asset. Stewardship makes the evidence, rules, issues and handoffs usable in the ordinary work cycle. The course practises how to document and route those decisions without treating a job title or a tool output as proof of authority.
What certificate and access will I receive?
After successful enrollment, you receive access to the MTF learning platform. Completing the required learning activities provides the MTF Institute course-completion certificate for Professional Certificate in Data Quality & Data Governance.