Build incident response for data quality governance with defined severity and escalation.
Designing Corporate Data Quality Governance Strategies
A practical programme in data quality governance for professionals who are accountable for results, not just awareness.
📍 Tunis🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Technical debt in data quality governance is borrowed against future delivery capacity, at compound interest. Availability targets for the technology and systems delivery capability are commercial commitments, whatever the engineering team calls them. The content is relevant to those who own this strand of technology and systems delivery and to those who are held accountable for its results. The difficulty is not agreeing that data quality governance matters — it is deciding what to stop doing to make room for it. Mature organisations treat this area of technology and systems delivery as a standing capability rather than a project that finishes. Every module pairs a short input on the practice within technology and systems delivery with structured practice on the participant's own material. The programme takes participants through data quality governance end to end, from framing the problem to closing it out. The programme builds the judgement to know which parts of this area of technology and systems delivery to standardise and which to leave flexible. It ends with a prioritised list of changes to this strand of technology and systems delivery that the participant is prepared to defend internally.
Expected Learning Outcomes
Measure delivery and reliability of data quality governance with indicators teams trust.
Present the case for investment in data quality governance in terms that a finance function will accept.
Build the deployment pipeline for data quality governance so releases are routine rather than events.
Design backup and recovery for data quality governance and prove it by restoring.
Plan the handover of data quality governance so that capability is not lost when key staff move on.
Set acceptance criteria for data quality governance before work begins rather than after.
Who Should Attend
Solution architects designing data quality governance.
Project managers delivering changes to data quality governance.
Infrastructure and platform engineers operating data quality governance.
Quality and test engineers verifying data quality governance.
Business partners who must understand data quality governance well enough to challenge it.
Training and development staff building internal capability in data quality governance.
Course Modules
Data quality governance: backup, recovery and continuity
2 sessions · 8 pointsSession 1Proving the backup of data quality governance by restoring it
- Define incident severity levels for data quality governance and the response each triggers.
- Apply change control to data quality governance including emergency changes.
- Configure alerting on data quality governance that reflects what users experience.
- Confirm every release of data quality governance can be rolled back within a defined time.
Session 2What to measure in data quality governance and what to ignore
- State the availability and recovery objectives for data quality governance as numbers.
- Agree what will be standardised in data quality governance and what will not.
- Set the review interval for data quality governance and who attends.
- Measure current load on data quality governance and project it forward twelve months.
Data quality governance: security, identity and least privilege
2 sessions · 8 pointsSession 1Designing data quality governance around how it will fail
- Identify the single points of failure in data quality governance.
- Write down the assumptions underpinning the approach to data quality governance.
- Confirm data retention and deletion rules applied within data quality governance.
- Set escalation thresholds for data quality governance that work out of hours.
Session 2Documenting data quality governance so someone else can operate it
- Remove steps in data quality governance that add effort without adding assurance.
- Establish what evidence demonstrates data quality governance is under control.
- Document the runbook for data quality governance to the level a new engineer could use.
- Prepare the response for the most likely failure in data quality governance.
Data quality governance: change control and rollback
2 sessions · 8 pointsSession 1Reading the current state of data quality governance honestly
- Set out the decisions in data quality governance that require sign-off and by whom.
- Define the trigger that would require data quality governance to be redesigned.
- Identify single points of dependency in data quality governance and reduce them.
- Set out how exceptions to data quality governance are requested and approved.
Session 2Running an incident on data quality governance calmly
- Review logging on data quality governance for coverage and retention.
- Assess the exit route from any cloud or vendor dependency in data quality governance.
- Inventory third-party dependencies inside data quality governance and their update status.
- Record the technical debt in data quality governance and schedule repayment.
Data quality governance: build, pipeline and release discipline
2 sessions · 8 pointsSession 1Closing out data quality governance and capturing what was learned
- Confirm that those complying with data quality governance understand why it exists.
- Establish who is informed, consulted and accountable in data quality governance.
- Run a load test on data quality governance at expected peak plus a margin.
- Arrange the handover of data quality governance so capability survives staff changes.
Session 2Monitoring data quality governance from the user's point of view
- Restore a backup of data quality governance in a test environment and time it.
- Assign responsibility for keeping documentation of data quality governance current.
- Test the failover for data quality governance rather than assuming it works.
- Establish the boundary of data quality governance and record what sits outside it.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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