Module 1
Data Sources, Profiling and Fitness for Use
- Analytical data requirements
- Source systems and data extraction considerations
- Profiling distributions, completeness and uniqueness
- Fitness-for-use criteria and risk
Data Quality Flagship
Data Preparation, Quality and Governance
Profile, clean, transform and govern data so analytical outputs are built on evidence that is trustworthy and fit for use.
Course overview
Make data quality an analytical discipline—not a clean-up step.
Data Preparation, Quality and Governance develops the practical capability to turn raw operational data into dependable analytical assets.
Participants work through profiling, cleaning, transformation, matching, quality rules, metadata, lineage, ownership, privacy and governance decisions while keeping the intended analytical use in view.
The course connects technical preparation with accountability so teams can explain where data came from, what changed, what quality risks remain and whether the data is suitable for the decision being supported.
Learning outcomes
What participants will be able to do.
- Profile datasets and identify completeness, validity, consistency and uniqueness issues.
- Design practical cleaning and transformation rules.
- Manage missing values, duplicates, outliers and inconsistent coding.
- Define data-quality measures, thresholds and exception handling.
- Document metadata and end-to-end data lineage.
- Clarify roles for data ownership, stewardship and issue resolution.
- Identify privacy, access and retention considerations for analytical data.
- Prepare a governed analytical dataset and quality report.
Who should attend
Designed for professionals who use data, analysis or evidence to improve decisions.
- Business and data analysts.
- Data stewards and data-quality professionals.
- BI, reporting and performance teams.
- Business analysts working on data-intensive initiatives.
- Managers responsible for analytical data quality and governance.
Prerequisites
Professionally relevant and application-focused.
Participants should be comfortable working with tabular data. Experience with spreadsheets, SQL, BI or analytics tools is helpful but not mandatory.
Course structure
A five-Module journey from analytical understanding to workplace application.
The sequence may be delivered across five days or adapted to another approved format while preserving the learning outcomes and the five connected Modules.
Module 2
Cleaning, Transformation and Integration
- Missing values and invalid records
- Standardisation, recoding and derivation
- Duplicates, matching and reconciliation
- Transformation controls and repeatability
Module 3
Data Quality Rules, Measurement and Remediation
- Quality dimensions and business rules
- Thresholds, scorecards and exception management
- Root causes and remediation priorities
- Quality monitoring and escalation
Module 4
Metadata, Lineage, Ownership and Privacy
- Business and technical metadata
- Lineage from source to analytical output
- Data ownership and stewardship
- Privacy, access, retention and responsible use
Module 5
Governed Analytical Data Products
- Reusable analytical datasets
- Documentation and quality evidence
- Change control and version discipline
- Integrated data-quality case and improvement roadmap
Learning experience
Move from understanding to application, production and workplace value.
Understand
Connect concepts with business decisions
Clarify methods, assumptions, evidence requirements and the decision context before applying tools.
Apply
Work through realistic analytical cases
Use datasets, scenarios and decision questions to practise analytical judgement in context.
Produce
Create practical analytical outputs
Develop artefacts that can be adapted to reporting, modelling, governance or decision-support work.
Review
Challenge evidence and analytical choices
Use peer review, validation criteria and facilitated critique to improve analytical reasoning.
Transfer
Apply the learning at work
Identify how to adapt the methods to organisational data, decisions, systems and governance requirements.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate actively in case discussions, data exercises and analytical workshops.
- Complete the principal analytical or decision-support outputs assigned during the programme.
- Contribute to the integrated case, model, dashboard or application workshop.
- Complete knowledge checks and a workplace application or study plan.
Participant outputs
Leave with practical analytical artefacts.
- Data-profile report.
- Cleaning and transformation rule set.
- Data-quality scorecard.
- Metadata and lineage record.
- Data ownership and issue-escalation matrix.
- Governed analytical dataset specification.
Delivery options
Select the learning format that fits your people and analytical environment.
Instructor-led
Live Classroom
Facilitated face-to-face learning with analytical cases, datasets, modelling tasks, discussion and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, data exercises, breakout analysis and guided model development.
Flexible
Blended Learning
A structured combination of preparation, live facilitation, applied assignments, analytical work and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational datasets, measures, tools, governance, decisions and analytics maturity where appropriate.
Frequently asked questions
Course information and participation.
Is this course only for data engineers?
No. It is designed for analysts, stewards, BI teams and business professionals who must understand and manage the quality of data used in analysis.
Does it include hands-on data preparation?
Yes. Exercises use realistic data-quality problems and preparation decisions. Tool choice can be adapted to the delivery environment.
Does the course cover data governance?
Yes. Governance is connected directly to analytical use through ownership, metadata, lineage, privacy, quality controls and accountability.
Can our own data-quality rules be incorporated?
Yes. Corporate delivery can use approved organisational examples, standards and data-governance practices.
Does completion provide professional certification?
The course develops practical data-preparation, quality and governance capability and may provide an INDENTRA course-completion record where applicable. It is not a third-party certification.
Take the next step
Build analytics on data you can explain and trust.
Discuss a tailored programme using your organisation’s data-quality priorities, governance model and analytical environment.