Data, AI, Digital, IT & Cybersecurity
Professional Mini-Master in Enterprise Data Strategy and Governance
Develop professional judgment for a measurable enterprise data strategy and governance that operates in practice.
Free
Program facts
- Study fee
- Free
- subjects
- 5
- lessons
- 20
- Estimated time
- 45 hours
- Content last reviewed
- Access period
- 60 days
- Study language
- Arabic and English
About the program
Organizations produce many figures, but a number is not fit for a decision merely because it appears on a dashboard. Begin by asking who defines an element and who may accept its risk. Build a glossary and lineage record that expose the meaning and sources of fields. Measure completeness, validity and consistency with explicit denominators and windows. Investigate defects across source, transformation and display, and plan repair with change and rollback evidence. Finish by running a stewardship meeting, a decision dashboard, an honest quality statement and a measurable thirty-day improvement cycle. Cities, organizations and applied numbers in the lessons are teaching fiction, not descriptions of real services. This is a foundational completion diploma, not a professional accreditation, license or legal authority. Real decisions remain subject to the policies and permissions of the learner's organization.
Practical relevance
Use the outputs with a fictional appointment, order or loan table: a metric card with numerator and denominator, a layered evidence issue record, a meeting decision with owner and deadline, and a consumer notice saying what can be used now. The course requires neither a commercial tool nor access to real production data.
Why this program
Two teams may agree on a field name yet disagree about its meaning or which population enters a report. The program connects charter, definition and test to issue and decision so version mismatches can be exposed before they mislead action. Learners practice saying when impact is unverified instead of announcing unsupported success.
Career relevance
Operations analysts, beginning data stewards, service managers and report consumers can use these methods to document definitions, question measures and route quality issues to the right decision owner. Such skills are relevant across teams but carry no promise of employment, promotion or satisfaction of a professional credential requirement.
What you will learn
- Set decision rights and draft a limited governance charter.
- Define data elements and versions and trace their movement across layers.
- Build quality rules and calculate rates with explicit populations and denominators.
- Separate observed defects, possible causes and unmeasured service impact.
- Plan source repair and historical reprocessing with tests and rollback.
- Run a steward meeting, dashboard, quality statement and improvement cycle.
Learning outcomes (20)
- Separate accountability for a business definition from stewardship, technical custody, and use; produce a decision-rights matrix and escalation path.
- Define a data-domain boundary and prioritize elements by a named decision and consequence instead of labeling every field critical.
- Write a bounded charter with decision purpose, authority, stewardship, cadence, exception route and evidence of completion.
- Compare competing data-use requirements, expose uncertainty, choose a proportionate publication decision and record approval plus follow-up.
- Draft a business-glossary entry with purpose, population, inclusion, exclusion, time boundary, formula, owner and counterexamples.
- Specify data type, permitted values, null semantics, transition rule, source, decision owner and acceptance examples for a critical element.
- Draw field-level lineage, classify transformations by whether they preserve or change meaning, and isolate the step that changes a metric.
- Compare candidate datasets through scope, provenance, refresh, limitations and contact path; write a catalog card and fitness-for-use warning.
- Choose a fit-for-purpose quality dimension and translate it into a testable rule without mistaking validity for accuracy or completeness for coverage.
- Calculate field and record completeness over an eligible population, handle conditional requiredness and exclusions, and explain a rate change without assuming quality improved.
- Specify executable checks for value format, entity-level uniqueness and cross-system consistency, with false-positive review and exception handling.
- Define population, rule, measurement frequency, target, alert band and sampling limits for a fit-for-use quality metric.
- Log an issue with affected asset, failed rule, observed population, business consequence, evidence, uncertainty, owner and next test.
- Rank remediation using affected decision, severity, exposure, time sensitivity, repair feasibility and uncertainty; preserve urgent exceptions outside average score.
- Trace a defect through intake, transformation, join and publication; compare competing hypotheses with disconfirming tests and a bounded conclusion.
- Design a bounded correction with source fix, quarantine, historical backfill, before-after checks, rollback trigger and accountable approval.
- Build a decision reading from numerator, denominator, period, scope and impact while detecting population shifts
- Run a governance meeting that converts issue evidence into owned, dated decisions and escalation
- Write a quality notice matched to audience, evidence and the affected decision
- Integrate charter, catalog, quality rules, issue log and dashboard in an auditable execution cycle
Who it is for
- Operations analysts and report consumers who need to judge whether a figure is usable before a service decision.
- Beginning data stewards who will write definitions, check rules and issue logs and need to know when an authority or ownership question should be escalated.
- Service and team managers who receive data-quality alerts and need to distinguish temporary containment from source repair and choose testable follow-up.
- Learners seeking a practical general foundation without prior programming, willing to read small tables and work through straightforward percentage calculations.
Prerequisites
- Be able to read a small table of rows and columns, distinguish an identifier from a description and date, and calculate a percentage with a calculator. Advanced data engineering, programming and database administration experience are not required; lessons introduce terms and examples from a shared starting point.
- It helps to describe one service decision that incomplete data could affect and to review a short written exercise. All exercises can use the supplied fictional data; no real work records or personal information need to be collected or shared.
What it does not offer
- This is neither EDM Council, W3C nor UK government accreditation, nor preparation for a specified professional examination.
- It does not provide legal advice or authority to edit production systems, or advanced study of data pipelines and enterprise architecture.
Curriculum
- Subject 1 Open for free
Governance purpose and decisions
This subject grounds governance in a specific service decision.
- Who decides what customer data means? 1.5 hours
- Bound a domain and identify critical elements 1.5 hours
- A small, usable governance charter 1.5 hours
- Resolve speed-versus-confidence conflicts 3 hours
- Subject 2
Definitions and traceability
This subject moves from a field label to a meaning that can be tested and recalculated.
- Define the term before calculating it 1.5 hours
- A testable data-element specification 1.5 hours
- Trace transformations from source to dashboard 1.5 hours
- A catalog card that leads to a fit source 3 hours
- Subject 3
Measuring data quality
This subject turns quality from a general label into tests linked to use.
- Which quality dimension tests this defect? 1.5 hours
- Completeness begins with the denominator 1.5 hours
- Rules for validity, uniqueness and consistency 1.5 hours
- A metric card and threshold fit to the decision 3 hours
- Subject 4
Resolving defects at source
Defect resolution begins by stating what was observed, where and when, then investigating cause rather than putting a guess in the ticket title.
- From quality alert to investigable issue 1.5 hours
- Prioritize defects with different impacts 1.5 hours
- Test the root cause instead of guessing 1.5 hours
- Repair source, backfill and verify 3 hours
- Subject 5
Operating governance and improvement
The final subject puts the diploma’s tools into an operating cadence.
- A quality dashboard that explains rate, volume and impact 1.5 hours
- Stewardship meeting, decision log and escalation 1.5 hours
- A quality statement for users with clear limits 1.5 hours
- A thirty-day improvement cycle linking definitions, rules, issues and decisions 3 hours
Subjects unlock in sequence as you progress.
Strengths
- Five subjects connect decision authority, meaning, measurement, repair and operations rather than teaching isolated tools.
- Each lesson contains fictional worked cases and exercises with checkable outcomes, keeping observed, estimated and unverified claims distinct.
- Each subject has a summative assessment and the completion record includes subject results.
- Primary sources are described within inspected access limits; an organization's guidance is never presented as a universal law.
Evidence basis
The course draws on the UK Government's data-quality and issues guidance, EDM Council's public DCAM overview, and W3C's DQV and DCAT 3 documents within their publicly inspected scope. DQV is a Working Group Note, and the accessible DCAM page is an overview rather than its detailed framework. Cases and exercises are original teaching fiction; current review is self-review, not accreditation or learner psychometric testing.
Reference organizations
- UK Government
- EDM Council
- W3C
Methodology
Fictional case and exercise instruction with author self-review; no accreditation or independent learner trial.
How the route works
Short Diplomas are relatively comprehensive learning pathways that cover a major subject through five connected learning subjects and structured assessments, with a diploma transcript available with the credential.
Self-paced sequential study. Content unlocks progressively and each subject combines reading, examples, exercises, knowledge checks, and assessment.
- Build integrated knowledge in a major subject.
- Study through five clear, connected learning subjects.
- Complete a defined assessment for each subject.
- Receive a diploma transcript showing subjects and results when the credential is issued.
Assessment
Each Short Diploma contains five subjects, each with an approved assessment. A sixth final exam is not required when the five assessments adequately cover the learning outcomes.
Assessment attempts: 2 per assessment, 7 days apart
The pass mark is announced before the program is published.
Certificate
Successful completion makes the learner eligible for the Short Diploma certificate and Diploma Transcript under the credential policy.
Every certificate has a public verification code. Verify a certificate
The real design with fictional details; opens in a new window.
Verification example: a real issued certificate
Disclaimer: Educational completion certificate with subject results; not professional accreditation or a licence.
Program questions
When choose this program over data quality alone?
Choose it when you need to connect measurement with definition ownership, escalation, decision and communication rather than calculating defect rates alone.
Do I need to write SQL?
No. Rules, numerators, denominators and lineage are explained with small tables; implementing them on a real system requires organizational skills and permissions.
Do the cases describe real services in the named cities?
No. Locations are fictional teaching settings; figures, organizations and decisions are invented to explain the skill.
How does a quality rule differ from an action threshold?
The rule determines which records fail within a defined population. A threshold decides when a result calls for action, according to a use owner’s purpose.
How can I distinguish a source defect from transformation loss?
Compare identifiers and values at source, after transformation and in the display; record where the gap first appears before assigning cause.
When may a provisional figure be shown?
Show it with the incomplete arrival window, known denominator and use limit; do not present it as a final trend or silently omit exceptions.
What if an old value cannot be recovered with evidence?
Keep it unknown or quarantined according to the use owner’s decision; do not invent a default event date to improve an indicator.
What does the thirty-day improvement cycle produce?
A baseline, rule, conditional repair decision, impact and rollback tests, a complete comparable observation window, and a choice to retain, adjust or reverse.
Does a green indicator prove no impact?
No. Check population, count, rule and the link to consumer decisions; a chart can improve while valid records disappear.
Is Short Diploma in Data Governance and Quality free, and do I get a certificate?
Yes. Short Diploma in Data Governance and Quality is free on ARPS: every lesson, exercise and assessment is free, the first subject is open to read without an account, and the program is available in Arabic and English. Issuing a certificate is optional; it is a training completion certificate showing you passed the program's assessments, not a university degree, a professional licence or an endorsement by an external body.
What other names is Short Diploma in Data Governance and Quality known by?
The subject of this program is also known as: data governance, data quality, data management. Learners usually find it by searching for: data governance course, data quality management, data governance framework, data management course online, free data governance course.
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