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The Head of IT Data Governance reports to the Group Head of IT and Security and is accountable for the quality, consistency and usability of the data the IT function relies on to run and improve itself. The role involves leading the IT Data Programme and establishing and sustaining an IT data governance capability that keeps the data model accurate.
This is a governance role, not a data ownership role. The IT leader accountable for a service remains accountable for the data supporting that service (e.g. the Head of IT Infrastructure and Networks owns infrastructure and identity data, the Head of IT Platforms owns platform and application data, etc.). This role is responsible for implementing and operationalising data governance across IT, in accordance with the Enterprise Data Governance framework, policies and standards. It works closely with the Enterprise Data lead to ensure alignment with enterprise governance, data quality, metadata and information management requirements.
The role develops IT-specific controls within the enterprise framework, measures compliance, reports performance and escalates non-compliance through agreed arrangements.
Lead the IT Data Programme
Own delivery of the IT Data Programme end to end, including the traceability model, the roadmap and the implementation backlog that follows.
Make sure the programme answers the business questions IT needs answered and doesn't become a documentation exercise. Every data requirement should be traced to a question and every gap to a prioritised action.
Turn assessment findings into a sequenced backlog with named owners and see it through to completion.
Implement and operate the data governance framework
Implement and operationalise the enterprise Data Governance Framework within IT. Support the nomination of named Data Owners, Stewards and Custodians across in-scope IT data domains, consistent with enterprise roles, responsibilities, standards and decision rights.
Apply enterprise data governance decision rights, escalation pathways and dispute resolution processes to IT data domains, escalating cross-functional matters through the established enterprise governance arrangements.
Set up and chair an IT Data Governance forum that makes decisions instead of reviewing status, aligned with the Enterprise Data Governance Framework and the governance arrangements led by the Enterprise Data team. Oversee IT data quality, stewardship, compliance and remediation through the forum.
Support the identification of a network of data stewards in each IT function, and support their training, standards and ways of working.
Hold Data Owners to account for the quality of their domains and report non-compliance through appropriate escalations.
Maintain IT data standards, definitions and authoritative sources
Develop and maintain the IT domain-specific data definitions, metadata, reference data and lifecycle requirements in accordance with enterprise data standards established by the Enterprise Data team, working closely with Enterprise Architecture and relevant Data Owners.
Maintain the catalogue of IT systems of record and their authoritative data sources, ensuring alignment with the enterprise data catalogue and metadata standards. Identify and resolve IT-specific source conflicts, escalating cross-domain ownership or authoritative source disputes through enterprise data governance.
Ensure that IT data assets, ownership, classifications, definitions and lineage are maintained in the enterprise data catalogue, working with the Enterprise Data team to establish consistent metadata and assurance requirements.
Govern alignment to the ServiceNow Common Service Data Model, ensuring the CMDB conforms to the enterprise IT data model rather than diverging from it.
Apply the existing data standards that system integrations, platform changes and new tooling must meet before approval.
Data quality, measurement and remediation
Implement the enterprise data quality framework across IT data domains, developing domain-specific rules, thresholds, controls and KPIs in collaboration with IT Data Owners and Stewards, consistent with enterprise data quality standards and reporting requirements.
Establish ongoing data quality measurement and remediation across IT data domains, using enterprise-approved methodologies and integrating results into enterprise data quality reporting and assurance processes.
Drive remediation of quality failures through the accountable Data Owner, escalating where remediation does not happen.
Operate the IT data certification process, so users of the data know which data they can trust.
Operate the information lifecycle for IT data, including retention, archiving and disposal, in line with Information Security policy.
Build data literacy across IT leadership so Data Owners understand and can discharge their accountability.
Publish and track an IT data maturity assessment, with a target state and a measurable trajectory.
Reporting and insight
Lead the development of trusted IT management reporting and performance measures, ensuring alignment with enterprise data architecture, common definitions, semantic standards and approved reporting platforms, in collaboration with Enterprise Data and Enterprise Architecture. This should be the single agreed source of the metrics used in leadership reporting, scorecards and business impact reporting.
Ensure that every metric reported to leadership has a defined calculation, a named owner and a known level of confidence.
Reduce reporting duplication across the function, retiring competing versions of the same measure.
Provide the data foundation that lets IT quantify its impact on business productivity in hours and cost.
Enable AI and automation
Lead IT data readiness activities supporting enterprise AI and automation initiatives, working with the Enterprise Data team to assess data availability, quality, classification, accessibility and fitness for purpose against enterprise AI readiness and governance requirements.
Maintain the data classification and sensitivity position for IT data sources feeding AI and automation tooling, to standards set by Information Security.
Identify and prioritise the data gaps that currently prevent automation, agentic use cases and self-healing capability.
Collaborate with the Enterprise Data team to prioritise IT data improvements that enable agreed AI analytics and automation use cases, ensuring alignment with the enterprise data strategy and roadmap.
IT Data Programme milestones delivered to committed dates.
Percentage of in-scope IT data domains with a named Data Owner and Data Steward.
Percentage of core IT data entities with a defined authoritative source and no unresolved source conflict.
Data quality scores by domain, trending against published thresholds.
Number of data quality defects remediated after their committed date, trending down.
CMDB completeness and accuracy against the CSDM-aligned target model.
Percentage of leadership metrics with a defined calculation, named owner and stated confidence level.
Reduction in duplicate or competing versions of the same reported measure.
IT data maturity assessment score, trending against target.
Number of business questions the function can answer reliably, against the agreed question set.
Substantial experience leading data governance in a complex, global or federated organisation, with results to show rather than frameworks produced.
Proven experience implementing a Data Owner, Data Steward and Data Custodian model and making it work, including holding senior stakeholders to account for the domains they own.
Experience working in Enterprise IT teams and an understanding of how they are structured, the services they provide and the frameworks that underpin them (ITIL, COBIT, NIST, Cyber Essentials, etc.)
Strong technology domain knowledge, specifically of IT data: configuration items, service and application dependencies, identity and entitlement, asset lifecycle, vulnerability and risk, and event and telemetry data.
Practical experience of ServiceNow CMDB and the Common Service Data Model, sufficient to govern conformance and challenge platform decisions credibly.
Experience implementing and operating data quality frameworks including rules, measurement, KPIs, certification and remediation.
Programme leadership experience, including accountability for external advisory or delivery partners, deliverable acceptance and commercial performance.
A track record of turning an assessment or strategy into a delivered backlog, not stopping at recommendations.
The credibility and confidence to challenge senior peers constructively, including those at or above their own grade, and to report uncomfortable findings plainly.
The ability to explain data governance in terms of business consequences (risk, cost, productivity and decision quality) to audiences with no interest in data management as a discipline.
Experience in professional services, engineering, consultancy, construction, regulated or global matrix organisations.
Experience of data governance in support of enterprise AI or Copilot adoption, including readiness assessment and classification.
Experience of identity data governance, including non-human and service identities.
Familiarity with LeanIX, enterprise architecture repositories or IT asset management tooling.
Formal data management credentials alongside practical delivery experience.
Experience building a data steward network across a distributed organisation.
Experience preparing governance papers or investment cases for executive committees.
Comfortable holding authority without owning the assets, and working through influence, evidence and governance rather than direct control.
Pragmatic. Delivers a working standard quickly rather than a perfect one slowly, and sequences improvement instead of attempting everything at once.
Persistent. Data governance works through repetition and follow-through, not through the launch.
Direct. Willing to publish an uncomfortable quality score and name the owner, while keeping the relationship intact.
Commercially aware, able to justify data investment in terms of risk reduction, cost and productivity.
A clear communicator who can make data governance matter to people who don't care about it.
Builds capability in others rather than becoming the single point of knowledge.
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