FAQ support one mags/DQ

To note - travel to Petty France is no more than once per month, considerations are given to utilising Microsoft teams where travel is not achievable. The DQ team is flexible in the travelling approach. Staff are required to work from their nearest local court/hub and help will be offered to arrange this with the successful candidate.

Coordinating delivery

Coordinate data quality activity across teams to ensure alignment with agreed priorities, standards, and organisational objectives, supporting effective planning, scheduling, and clarity around roles and dependencies. Work closely with the One Mags project manager to track progress against improvement plans, milestones, and actions, providing clear and timely updates to stakeholders on performance, risks, issues, and emerging dependencies to inform decision-making. Contribute to the continuous improvement of data quality processes, tools, and ways of working by capturing lessons learned, sharing best practice, and identifying opportunities to increase efficiency and embed a culture of ongoing improvement.

Delivering data quality improvements 

Support the planning and delivery of data quality audits across HMCTS crime service metrics, assessing data against agreed standards and requirements by gathering evidence, reviewing datasets, and documenting findings. Identify data quality issues and trends through analysis and feedback, carrying out root cause analysis to understand underlying drivers and supporting the development of practical remediation actions. Work with stakeholders to implement proportionate data quality processes, standards, and controls, helping embed improvements into day-to-day operations to strengthen data reliability and confidence in reporting. Collaborate with subject matter experts to promote consistent data definitions and standards, supporting alignment between business and technical teams to reduce ambiguity and improve shared understanding.

Applying data quality across the data lifecycle

Work collaboratively with operational, analytical, and technical teams to identify data quality issues early and implement practical improvements, providing tailored guidance, tools, and best practice on standards, validation, documentation, and remediation. Proactively assess and manage data quality risks, evaluating their impact and likelihood, escalating where appropriate, and supporting proportionate, risk-based mitigation that balances improvement with operational priorities.

Support the delivery of data quality audits across HMCTS services

Identify data quality issues and patterns through audit findings, analysis, and stakeholder feedback, carrying out root cause analysis to understand underlying process, system, or behavioural drivers and supporting the delivery of practical remediation actions to prevent recurrence. Work with stakeholders to implement proportionate data quality processes, standards, and controls, embedding improvements into day-to-day operations to strengthen data reliability, consistency, and confidence in reporting. Collaborate with subject matter experts to promote consistent data definitions and reference data, aligning business and technical teams to reduce ambiguity, improve interoperability, and build a shared understanding of data across the organisation.

Communicating insight and supporting decisions 

Present data quality findings clearly and in a structured, accessible way for both technical and non-technical audiences, translating complex issues into understandable insights through effective summaries, visuals, and narrative. Provide practical, evidence-based recommendations that outline options, impacts, and next steps, supporting stakeholders to prioritise actions and make informed decisions. Adapt communication style and level of detail to suit different audiences, ensuring messages are relevant, focused, and enable effective engagement and decision-making.

Stakeholder engagement

Develop and maintain strong, collaborative relationships across HMCTS teams, building trust and credibility through constructive engagement and a clear understanding of different service contexts. Work with both technical and non-technical stakeholders to identify and resolve data quality issues, acting as a bridge between business and technical teams to translate requirements and enable practical, sustainable solutions. Facilitate discussions across teams with differing priorities, helping to balance competing demands, surface risks and dependencies, and support agreement on proportionate actions that improve data quality while supporting operational delivery.

Enabling teams and building capability 

Support teams across HMCTS to strengthen understanding of what good quality information looks like and why it matters, building awareness of its impact on operational delivery, performance reporting, decision-making, and public trust, while encouraging shared ownership of outcomes. Contribute to improving data literacy by helping users understand limitations, assumptions, and appropriate use, enabling confident and responsible interpretation while highlighting risks such as bias or misuse. Develop, maintain, and promote clear, practical guidance and documentation to support consistent approaches, ensuring materials are accessible, up to date, and help teams apply agreed standards, definitions, controls, and quality checks in day-to-day delivery.