Data & research
Data Governance & Business Intelligence
Build an evidence-led Data Governance & Business Intelligence foundation you can explain in a real workplace or interview.
Build trustworthy decision information through clear ownership, quality controls, privacy and responsible reporting.
About this course
An advanced programme for people working with operational data, dashboards and reporting. It teaches governed decision support: definitions, quality, access, lineage, ethical use and honest communication of limits.
Career relevance
Where this can take you
Designed for learners exploring data careers and building a practical, honest portfolio.
Portfolio project: Create a Data Governance & Business Intelligence capstone pack that documents your purpose, method, evidence, quality check and next action.
Skills you will gain
Practical skills, not just theory
What you will learn
Define decision-ready data products, ownership and quality measures.
Apply privacy, access and retention boundaries to operational data work.
Design dashboard measures that answer real questions without misleading users.
Create a governed BI case with lineage, limitations and action recommendations.
Objective map
A numbered, measurable syllabus
Every lesson, knowledge check and practical activity maps to the objectives below. Domain weights show how much of the programme each area represents.
Domain 1
Data governance purpose, ownership and decision rights to Data quality dimensions and remediation
- 1.1Demonstrate data governance purpose, ownership and decision rights in a realistic data governance & business intelligence context: establish owners, stewards, users and escalation routes for a controlled data product.
- 1.2Demonstrate business questions, metrics and data definitions in a realistic data governance & business intelligence context: convert vague reporting requests into defined measures, populations, periods and decision uses.
- 1.3Demonstrate data quality dimensions and remediation in a realistic data governance & business intelligence context: assess completeness, validity, timeliness, uniqueness and consistency with a repair process.
Domain 2
Data lineage, metadata and reproducibility to Data modelling and integration controls
- 2.1Demonstrate data lineage, metadata and reproducibility in a realistic data governance & business intelligence context: document source-to-report flow, transformations, assumptions and version-controlled definitions.
- 2.2Demonstrate privacy, access and ethical data use in a realistic data governance & business intelligence context: apply purpose limitation, minimum access, retention and escalation when a request exceeds authority.
- 2.3Apply and control data modelling and integration controls in a realistic data governance & business intelligence context: test keys, joins, grain and reconciliation before combining operational sources.
Domain 3
Dashboard design and decision communication to Capstone: governed executive dashboard case
- 3.1Plan and produce dashboard design and decision communication in a realistic data governance & business intelligence context: design accessible measures, comparisons and annotations that support—not manipulate—decisions.
- 3.2Demonstrate insight quality, bias and limitations in a realistic data governance & business intelligence context: check analysis for bias, confounding, missing context and unjustified causal claims.
- 3.3Complete and defend capstone: governed executive dashboard case in a realistic data governance & business intelligence context: create a governed dashboard brief with source map, controls, findings and limitations.
Domain 4
Assurance, monitoring and professional portfolio
- 4.1Complete and defend assurance, monitoring and professional portfolio in a realistic data governance & business intelligence context: set review routines, issue metrics and a portfolio narrative for responsible BI practice.
How the programme works
Learn, practise, explain and prove it
This is a long-form, eight-week guided programme rather than a stack of short articles. Each module contains six substantial lessons that move from concepts and methods into a worked case, a quality clinic, a workplace lab and assessed reflection.
1. Guided learning
Study the purpose, process, risks and professional standard through structured readings and active notes.
2. Worked cases
Compare plausible options, expose missing evidence and defend a decision under realistic constraints.
3. Quality clinics and labs
Diagnose weak work, revise it against a rubric and create portfolio evidence with a full audit trail.
4. Hard assessment
Pass applied checks and the final assessment at 85%, with no unresolved critical-error questions.
Curriculum
A complete programme, module by module
The curriculum below shows the actual learning pathway, objective and evidence for every module. Full lessons remain locked until enrolment.
Build the base
Standards, vocabulary and the core method you will use throughout the programme.
01Data governance purpose, ownership and decision rightsEstablish owners, stewards, users and escalation routes for a controlled data product.+
Measured objectiveDemonstrate data governance purpose, ownership and decision rights in a realistic data governance & business intelligence context: establish owners, stewards, users and escalation routes for a controlled data product.
Evidence you will produceA checked working paper, analysis or report for data governance purpose, ownership and decision rights, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
02Business questions, metrics and data definitionsConvert vague reporting requests into defined measures, populations, periods and decision uses.+
Measured objectiveDemonstrate business questions, metrics and data definitions in a realistic data governance & business intelligence context: convert vague reporting requests into defined measures, populations, periods and decision uses.
Evidence you will produceA checked working paper, analysis or report for business questions, metrics and data definitions, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
03Data quality dimensions and remediationAssess completeness, validity, timeliness, uniqueness and consistency with a repair process.+
Measured objectiveDemonstrate data quality dimensions and remediation in a realistic data governance & business intelligence context: assess completeness, validity, timeliness, uniqueness and consistency with a repair process.
Evidence you will produceA checked working paper, analysis or report for data quality dimensions and remediation, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can explain the fundamentals and complete a first guided task.
Develop core skills
The working skills and tools of the role, practised one at a time.
04Data lineage, metadata and reproducibilityDocument source-to-report flow, transformations, assumptions and version-controlled definitions.+
Measured objectiveDemonstrate data lineage, metadata and reproducibility in a realistic data governance & business intelligence context: document source-to-report flow, transformations, assumptions and version-controlled definitions.
Evidence you will produceA checked working paper, analysis or report for data lineage, metadata and reproducibility, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
05Privacy, access and ethical data useApply purpose limitation, minimum access, retention and escalation when a request exceeds authority.+
Measured objectiveDemonstrate privacy, access and ethical data use in a realistic data governance & business intelligence context: apply purpose limitation, minimum access, retention and escalation when a request exceeds authority.
Evidence you will produceA checked working paper, analysis or report for privacy, access and ethical data use, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
06Data modelling and integration controlsTest keys, joins, grain and reconciliation before combining operational sources.+
Measured objectiveApply and control data modelling and integration controls in a realistic data governance & business intelligence context: test keys, joins, grain and reconciliation before combining operational sources.
Evidence you will produceA checked working paper, analysis or report for data modelling and integration controls, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can carry out routine work independently and check it properly.
Apply it in realistic scenarios
Case studies and simulations with competing priorities and incomplete information.
07Dashboard design and decision communicationDesign accessible measures, comparisons and annotations that support—not manipulate—decisions.+
Measured objectivePlan and produce dashboard design and decision communication in a realistic data governance & business intelligence context: design accessible measures, comparisons and annotations that support—not manipulate—decisions.
Evidence you will produceA checked working paper, analysis or report for dashboard design and decision communication, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
08Insight quality, bias and limitationsCheck analysis for bias, confounding, missing context and unjustified causal claims.+
Measured objectiveDemonstrate insight quality, bias and limitations in a realistic data governance & business intelligence context: check analysis for bias, confounding, missing context and unjustified causal claims.
Evidence you will produceA checked working paper, analysis or report for insight quality, bias and limitations, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
09Capstone: governed executive dashboard caseCreate a governed dashboard brief with source map, controls, findings and limitations.+
Measured objectiveComplete and defend capstone: governed executive dashboard case in a realistic data governance & business intelligence context: create a governed dashboard brief with source map, controls, findings and limitations.
Evidence you will produceA checked working paper, analysis or report for capstone: governed executive dashboard case, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can make and justify decisions in realistic situations.
Prove it and plan your next step
Portfolio evidence, final review and a development plan you can show an employer.
10Assurance, monitoring and professional portfolioSet review routines, issue metrics and a portfolio narrative for responsible BI practice.+
Measured objectiveComplete and defend assurance, monitoring and professional portfolio in a realistic data governance & business intelligence context: set review routines, issue metrics and a portfolio narrative for responsible BI practice.
Evidence you will produceA checked working paper, analysis or report for assurance, monitoring and professional portfolio, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You have portfolio-ready evidence and a clear next step.
Suggested pace
Eight-week study plan
Plan for roughly 5–9 hours each week across readings, worked cases, practice, portfolio production and assessment remediation. Repeat a week when the knowledge checks show a gap.
Week 1
Data governance purpose, ownership and decision rights to Business questions, metrics and data definitions
- •Complete the six guided lessons for Data governance purpose, ownership and decision rights and Business questions, metrics and data definitions and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data governance purpose, ownership and decision rights and business questions, metrics and data definitions portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 2
Data quality dimensions and remediation
- •Complete the six guided lessons for Data quality dimensions and remediation and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data quality dimensions and remediation portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 3
Data lineage, metadata and reproducibility
- •Complete the six guided lessons for Data lineage, metadata and reproducibility and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data lineage, metadata and reproducibility portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 4
Privacy, access and ethical data use
- •Complete the six guided lessons for Privacy, access and ethical data use and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the privacy, access and ethical data use portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 5
Data modelling and integration controls to Dashboard design and decision communication
- •Complete the six guided lessons for Data modelling and integration controls and Dashboard design and decision communication and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data modelling and integration controls and dashboard design and decision communication portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 6
Insight quality, bias and limitations
- •Complete the six guided lessons for Insight quality, bias and limitations and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the insight quality, bias and limitations portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 7
Capstone: governed executive dashboard case
- •Complete the six guided lessons for Capstone: governed executive dashboard case and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the capstone: governed executive dashboard case portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 8
Assurance, monitoring and professional portfolio
- •Complete the six guided lessons for Assurance, monitoring and professional portfolio and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the assurance, monitoring and professional portfolio portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Submit the capstone evidence pack, defend the key decision, remediate every weak objective and pass the final assessment at 85% with no critical misses.
Data & research
Continue your learning path
Business Intelligence Dashboards
Design controlled dashboards that answer operational questions.
60–72 hours
Business Statistics
Use statistical thinking to interpret evidence for workplace decisions.
60–72 hours
Data Engineering Foundations
Understand how reliable data pipelines support analysis and products.
55–66 hours
Assessment and certificate
A long timed data-governance case covering metric definitions, privacy, lineage, quality failures and responsible executive reporting.
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Who this is for
- ✓Professionals progressing in data careers.
- ✓Learners building credible data governance & business intelligence evidence.
- ✓Career changers prepared for sustained, scenario-led study.