Data & AI Training
Issues Management
Designing a structured, transparent process for identifying, prioritising and resolving data issues.
Overview
Most organisations have data issues, but very few have a clear, consistent way of managing them. Issues are reported informally, fixed locally, or ignored entirely. This session introduces a formal issues management process that brings visibility, accountability and prioritisation to data problems.
Participants learn how issues management fits within governance and quality, and how it enables learning and improvement rather than blame.
Who it is for
- Data Owners and Stewards responsible for quality outcomes.
- Operational teams experiencing recurring data problems.
- Governance leads designing issue and escalation processes.
- Programme and delivery teams managing data dependencies.
What you will learn
- What constitutes a data issue and how to classify it.
- How to design an end-to-end issues management process.
- How to prioritise issues based on business impact and risk.
- How issues feed Root Cause Analysis and improvement.
- The role of tooling versus process.
- How to create transparency without overload.
Agenda
- Why data issues persist.
- Defining and categorising data issues.
- Issue lifecycle: log, triage, prioritise, resolve, close.
- Roles and responsibilities in issues management.
- Linking issues to quality metrics and governance forums.
- Practical examples and discussion.
Delivery and format
Delivered as a 1 hour interactive virtual session. Can be extended into a design workshop to define issue categories, workflows and ownership.
- Format: Live virtual or on site.
- Group size: Up to 25 participants recommended.
- Optional: Follow-up policy design workshop.
Next steps
This course is commonly paired with: DG7 · Root Cause Analysis, DQ3 · Data Remediation & Cleansing and DQ4 · Data Quality Improvement