Support Master Data Management

• Help business stakeholders understand the value of Master Data Management and secure their participation in defining and governing key data domains.
• Act as the bridge between business stakeholders and the teams designing and implementing Master Data Management capabilities.

Improve data quality at source

• Promote the principle of getting data right at source, helping colleagues understand the downstream business, operational, reporting and technology consequences of poor-quality data.
• Work with business teams to identify recurring data-quality problems and address their root causes rather than continually correcting symptoms downstream.
• Help establish clear accountability for data quality and encourage the organisation to move from reactive data cleansing towards prevention.

Build understanding of end-to-end data dependencies

• Help move the organisation away from viewing applications and their data in isolation.
• Build awareness that once systems are integrated, a change to a data field, structure, definition or business rule in one application can have significant consequences elsewhere.
• Promote an end-to-end data mindset, encouraging system owners and business teams to understand downstream dependencies before making changes.
• Work with technology, architecture and integration teams to establish appropriate governance and impact assessment for changes affecting shared or integrated data.

Drive adoption and behavioural change

• Identify stakeholder groups and assess their readiness for new data responsibilities and ways of working.
• Develop targeted engagement, communications, workshops and training for different audiences, from senior leaders and Data Owners through to operational users.
• Identify resistance or misunderstandings early and work constructively with individuals and teams to overcome them.
• Develop practical examples and stories that demonstrate the consequences of poor data management and the benefits of improved data ownership and quality.
• Measure adoption and maturity over time and identify where further intervention, training or leadership support is required.

Skills & Experience:

The successful candidate will combine data knowledge, communication ability and organisational change skills. They will be:

• Passionate about the value of data and able to make others care about it.
• Credible with senior business stakeholders, operational teams and technical specialists.
• An excellent communicator who can make complex data concepts simple, relevant and engaging.
• Confident delivering presentations, workshops, training and one-to-one coaching.
• Comfortable challenging existing behaviours and ways of working constructively.
• Able to influence people without necessarily having direct authority over them.
• Pragmatic and commercially/business focused rather than overly theoretical about data governance.
• Naturally outgoing and comfortable building relationships across organisational boundaries.
• Able to connect individual decisions about data with their wider end-to-end consequences.

Knowledge and Experience

Ideally, the individual will have experience in several of the following areas:

• Data governance and data management.
• Data ownership and data stewardship operating models.
• Data quality and data-quality improvement.
• Master Data Management.
• Business or digital transformation.
• Organisational change and adoption.
• Stakeholder engagement and communications.
• Training, facilitation and workshop delivery.
• Enterprise systems and system integration.
• Data architecture, data models and data flows at a level sufficient to engage confidently with technical teams.

Deep engineering expertise is not required. However, the individual must have enough technical understanding and curiosity to work credibly with data architects, integration specialists, developers and technology delivery teams.