Director, Data Generation & Privacy

Leading the selection, procurement, and enterprise-wide adoption of synthetic data platforms. Defining the strategic roadmap for platform capabilities and integration across business units. Managing vendor relationships and multi-million-pound licensing agreements.

Designing the overall cloud architecture for synthetic data generation and storage. Setting standards for cost optimisation, security, and scalability across the entire data estate. Making high-level decisions on cloud service adoption and vendor strategy.

Defining the enterprise orchestration strategy for all data generation pipelines. Setting SLAs, monitoring standards, and ensuring robust, scalable infrastructure for automated data synthesis. You'll oversee, not build, the complex DAGs.

Establishing the enterprise-wide MLOps strategy for generative models. Mandating tools and processes for model versioning, experiment tracking, and data quality validation. Defining the key utility and privacy metrics the organisation will track at a strategic level.

• C-Suite (CEO, CDO, CTO, CPO, CLO)
• Board of Directors (especially Audit & Risk Committees)
• Legal & Compliance Teams
• Product & Engineering Leadership
• Heads of Business Units (e.g., Marketing, Finance, Operations)
• Internal Audit

• Data Protection Regulators (e.g., ICO in the UK)
• Industry Bodies & Standards Organisations
• Key Technology Vendors & Partners
• External Auditors
• Privacy Advocacy Groups

As data silos persist and privacy regulations tighten, the ability to train generative models on decentralised datasets without centralising raw data becomes critical. Federated learning and similar decentralised approaches offer a path to synthetic data generation where data never leaves its source, significantly enhancing privacy and compliance. This will be key for cross-organisational data collaboration.

Federated Averaging (FedAvg) and its variants · Secure aggregation protocols · Differential privacy in federated learning · Decentralised data marketplaces for synthetic data · Homomorphic encryption for model updates

• This quarter: Task a senior engineer or architect with researching and presenting a strategic overview of federated learning for synthetic data.
• Next 6 months: Identify a pilot project where federated learning could enable data synthesis across two internal, siloed datasets.
• Next 12 months: Oversee the development of a proof-of-concept for decentralised synthetic data generation.
• Ongoing: Evaluate commercial platforms offering federated learning capabilities and assess their fit for our enterprise strategy.

Quick win: Sponsor a hackathon within your team focused on exploring federated learning frameworks (e.g., PySyft, TensorFlow Federated) for a simple synthetic data task.

The proliferation of AI at the edge (IoT devices, smart sensors) creates massive new data streams, often sensitive, that cannot be easily transferred to the cloud. Generating synthetic data directly on these devices, or using synthetic data to train edge models, will become crucial for privacy, bandwidth, and latency. This opens up new product opportunities and privacy challenges.

On-device generative model training · Resource-constrained synthetic data generation · Data minimisation for edge AI · Privacy-preserving data aggregation from IoT devic · Synthetic data for robust edge model testing

• This year: Initiate a strategic review of our current and future edge AI initiatives and their data privacy implications.
• Next 18 months: Fund a research project to explore the feasibility of generating synthetic sensor data on low-power IoT devices.
• Next 2-3 years: Develop a strategic roadmap for integrating synthetic data into our edge AI development and testing workflows.
• Ongoing: Collaborate with product teams to identify early-stage edge AI projects that could benefit from synthetic data.

Quick win: Engage with a relevant industry consortium focused on privacy in IoT to understand emerging standards and best practices.