Responsible Data Leadership in an AI-Driven World
Gemma Dias, Head of Data Governance, Tyro Payments
Gemma Dias, Head of Data Governance, Tyro Payments
I have held leadership roles across multiple domains within the data space, ranging from lead consultant positions in Data Warehousing to in-house leadership roles in Data Strategy, Data Architecture, Marketing Campaign Management, Data Management and most recently, Data Governance.
Throughout these roles, I have led cross-functional teams comprising data engineers, domain and solution architects, data architects, solution designers, and data stewards. Together, we have architected, designed, and delivered a range of enterprise-wide data capabilities; including data warehouse platforms, data lakes, campaign management systems, and customer and address master data management, reference data management, metadata management, and data quality solutions.
These initiatives have enabled organizations to effectively leverage their data assets, creating business value, reducing data-related risks, enhancing customer satisfaction, and strengthening their competitive advantage.
In my current role as Head of Data Governance at Tyro, my practical experience in delivering end-toend data management solutions has allowed me to bridge the gap between policy and practice. I have developed and implemented a comprehensive data governance framework, aligned with the DAMA framework, along with a supporting strategy, policies, and playbooks. These materials are designed to be accessible and actionable for both business and technical teams, ensuring data standards are embedded in daily operations and easily adopted across the organization.
With the rise of AI and gen AI, my focus is on ensuring that data governance evolves to support the responsible and scalable use of these technologies
Having led data teams across various sectors, including finance, telecommunications, and design technology, I’ve developed a broad and adaptable perspective on data governance. Each industry brings its own regulatory environment, data sensitivity, and pace of innovation, which has forced me to think critically about both compliance and agility.
While every organisation has its unique data maturity, challenges, business objectives, and priorities, I have observed several common data-related issues that persist across industries. These often include inconsistent data quality, lack of standardised definitions, fragmented ownership, and challenges in aligning data strategy with business goals.
That said, the degree of regulatory oversight and industry-specific requirements plays a critical role in shaping data governance practices.
Highly regulated sectors such as finance and telecommunications necessitate more stringent controls and auditability. As a result, governance frameworks must be tailored to reflect both the external regulatory environment and the internal organisational context.
Although the core principles of data governance, such as accountability, data quality, and stewardship, remain consistent, their implementation must be adapted to the organisation’s data maturity and specific needs.
This cross-industry exposure has shaped my approach to be principle-driven rather than rule-bound. I focus on building governance frameworks that strike a balance between control and enablement, ensuring compliance and trust in data while empowering teams to move quickly and innovate.
Given your background in leadership and people development, how do you nurture future data leaders within your team while maintaining delivery at scale?Nurturing future data leaders while maintaining delivery at scale requires a deliberate balance between developing individual potential and ensuring team-wide execution discipline. My approach involves mentorship, skill development opportunities and empowerment through ownership, with a delivery-focused culture.
I actively promote a culture of collaboration and peer learning within my team. One key initiative I've implemented is the use of roundtable sessions, where team members regularly present their work to the rest of the group. These sessions not only provide visibility into each other’s contributions but also foster a supportive environment for sharing best practices and receiving constructive feedback.
This approach encourages knowledge exchange, breaks down silos, and helps team members develop both technical and communication skills. It also empowers individuals to take ownership of their work while contributing to the collective growth and cohesion of the team.
I also promote a culture of psychological safety and shared learning, where experimentation and reflection are encouraged. This not only builds confidence but also equips future leaders with the critical thinking and problemsolving skills they’ll need at scale.
Ultimately, by aligning personal development with organisational objectives, I’m able to grow capable, autonomous data leaders who contribute meaningfully while sustaining high delivery standards across the team.
What emerging trends in data governance and management are you most focused on, with the increased use of Artificial Intelligence (AI) and Generative Artificial Intelligence (Gen AI)?With the rise of AI and Gen AI, my focus is on ensuring that data governance evolves to support the responsible and scalable use of these technologies. A critical area of emphasis is providing context to data. AI doesn’t just need high-quality data; it requires context to interpret that data accurately and minimise issues like hallucinations.
Data governance plays a pivotal role in this by ensuring that business glossaries, data classification, data lineage, and data provenance are well-defined, creating a solid knowledge base for AI models. Additionally, data quality measures such as completeness, accuracy, and timeliness are essential to ensuring reliable and trustworthy outputs.
My key priorities include strengthening data quality and lineage, embedding privacy-by-design principles into data solutions, and ensuring that governance frameworks are both adaptable and robust. Ultimately, the goal is to ensure AI is built on trusted data and used responsibly across the organisation.
What advice would you offer to young professionals in your field looking to build successful careers? If you're just starting in the data field, here are a few things I'd suggest:• Get the basics right. Spend time learning the fundamentals, SQL, data modelling, and data engineering. These are the building blocks, and having a solid hands-on experience with them will give you confidence no matter where your career takes you.
• Be curious. The data world changes fast. New tools, new techniques, and now AI are constantly reshaping the landscape. Be open to learning, experimenting, and stepping outside your comfort zone.
• Learn the "why" behind the data. Don’t just focus on crunching numbers, but understand the business problems you are trying to solve. That is where you can add real value.
• Communicate simply. Being able to explain complex data or insights in a simple, clear, non-technical way is a superpower. Work on your storytelling skills; it will help you connect with different teams and make a bigger impact.
• Find good people to learn from, such as mentors, peers, and online communities. Surround yourself with others who are curious and driven. Ask questions, share your knowledge, and grow together.
• And finally, don’t rush. Building a solid career in data takes time, so focus on learning, stay engaged, and enjoy the journey.
