Digital Platforms & Data Systems
Industrial organisations generate operational data continuously but rarely leverage it effectively. Translating that data into commercial value requires more than analytical capability. It requires product leadership to build the platforms that collect and structure it, and data architecture to make it accessible at scale.
My work here spans both layers. At the product layer, I have owned hardware and software product lifecycles for industrial sensing and analytics systems from field validation through to commercial scale. At the data layer, I have designed and deployed the enterprise data infrastructure that makes AI and analytics viable across global operations.
Case Studies
Selected examples illustrating product management and data infrastructure for industrial digital systems.
Industrial Diagnostics Platform:
From Field Testing to Commercial Scale
Problem
A new downhole diagnostics tool had demonstrated technical potential but was encountering mechanical failures, measurement inconsistencies, and software interpretation issues during field testing. Without resolution, there was no credible path to commercial deployment.
Approach
I assumed product ownership, required additional validation before proceeding, and coordinated parallel mechanical investigation and redesign. When the critical decision point arrived, I elected to launch commercially while retaining dedicated engineering resource post-launch to manage residual risk. A deliberate trade-off between technical completeness and commercial timing, with direct oversight retained throughout. Pricing models were structured to include performance-based incentives tied to measurable operational outcomes.
Impact
The product established itself as a leading solution in its category across multiple geographies, with the tool fleet and associated revenue growing substantially from initial deployment. The commercial model, integrating sensing hardware, software, and continuous data interpretation, became the template for the broader product line.
Enterprise Data Foundation for AI at Scale
Problem
A global division held large volumes of operational and engineering data distributed across disconnected regional systems with inconsistent structures, limited governance, and no unified access model. AI workflows were constrained to small manually curated datasets, with validation cycles that made commercial deployment slow and difficult to justify.
Approach
I led the design and deployment of a cloud-based global data infrastructure incorporating structured storage, transformation pipelines, and a canonical data model unifying records across all geographies and business lines. A minimum dataset standard was defined with domain experts, embedded in division technical standards, and enforced through audit processes. Data governance was resolved systematically implementing classification, residency rules, and rights of use, across more than sixty regulatory jurisdictions, with local deployments where centralisation was legally constrained.
Impact
Operational datasets scaled from dozens of manually curated cases to thousands of structured records available on demand. AI model validation cycles shortened from months to weeks. Multiple digital workflows progressed to pre-commercial deployment as a direct consequence of the data foundation being in place.