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  • Ming Tang, Chief Data and Analytics Officer, NHS England

Ming Tang, Chief Data and Analytics Officer, NHS England

Describe your career to date

I started my career as a pharmacist working in operations for GSK, as a graduate trainee. After my MBA studies, I joined Accenture as a Strategy Consultant working with companies on improving their supply chain and commercial capabilities. My experience spans global clients in consumer goods, retail, pharmaceutical, manufacturing, and utility sectors. As an Accenture Partner, I led transformation programmes delivering large-scale change and implementation of new operating models in complex and challenging environments. I joined the NHS in October 2009, initially leading commissioning support services in the West Midlands as the managing director for Healthcare Commissioning Services, and then as the managing director for South Yorkshire and Bassetlaw Commissioning Support Unit.

My current role is the chief data and analytics officer for NHS England, and I am responsible for delivery of Data and Analytics services with around 1,700 staff, and responsible as the Head of Profession for Data and Analytics Capability development across the NHS. I am also responsible for development of data infrastructure for NHS Research and Development, digitrials, and genomics medicines. My team provides data and analytical expertise to support NHS transformation, working as part of the Transformation Directorate to provide data and digital capabilities to enhance decision making and improve health and care services. Our aim is to lead the health and care system to be more strategic in the use of data and insights, providing the evidence to support decision making to improve health and care for all, now, and for future generations.

How are you developing the data literacy of your organisation, including the skills of your data teams and of your business stakeholders?

I have developed a programme to improve use of data focused on three key areas, our data and analytics workforce; business users and development of advanced analytics.

Firstly, for our data and analyst workforce, we have developed a competency model to better understand the skills of the workforce and are creating a data and analytics academy to improve skills and provide competency related learning that is orientated to career paths. As data and analytical skills are applied roles, we are building in experiential learning and development with support on the job, as well as online and action learning in groups.

Secondly, for business users of data and analysis, we are experimenting with artificial intelligence (AI) to explore how users can better interact with data assets and dashboards that are already available. Development of AI enabled navigation of existing assets, using AI navigator that uses large language models (LLM) to support users to facilitate navigation to the correct place and page for pre-set analysis and improve user experience. Using LLMs so that users can ask the digital assistant to interrogate the data models to answer specific questions and to create new visualisations. The importance of the two-stage approach, is to help business users, firstly understand what is already available and to familiarise and support the user towards self-service. This will also encourage the business users to work with analysts and develop more informed questions, that can then use to train the AI and LLM in the background. We anticipate that the approach will lead to better adoption and more experiential learning between the business user and the analytical workforce.

Finally, for advance analytics, we are also in the process of developing a digital twin of the NHS, so that we can create better predictive analytical models and tools to support planning and scenario management. A digital twin will provide the capability for simulation and in future should provide the basis for operational decision support tools that can be used as part of a workflow for optimisation. We also completed the procurement of a new data platform; this is a major investment providing a foundational capability to allow the NHS to experiment with AI at scale. From the pilot work, we need to do more on ethical considerations from AI and look at development of a segmentation model for different usecases. I would like us to have a clear operating model to support AI usecase assessment. I am keen that we co-develop with users a set of AI augmentation tools for operational and strategic decision support. All this work will require training and development of our teams to provide best outcomes from adoption of AI with concurrent evaluation, so that we learn, iterate, and continuously improve our capability.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function?     

I have set a clear vision for transformation through data and have used the narrative to create a business case for change and investment in data infrastructure. With the merger of NHS England, NHS Digital, NHS X, and Health Education England, the NHS has been in state of change for past year. I am very proud that we were still able to make the case for investment in a new federated data platform (£330 million over seven years) which will provide a significant opportunity to support the delivery of data vision and strategy. The vision for data embraces the whole of the NHS and has been created to put data at the heart of the NHS transformation journey. As we learn and develop more applications the Federated Data Platform provide the NHS with a standardised operating system for app development – similar to iOS for apple iPhone. Effectively a solution exchange to leverage innovation and co-development with new suppliers (particularly SMEs), providing standard process to access NHS synthetic data and metadata for application development. The solution exchange will also enable a more effective way for NHS organisations to adopt these new applications through a standard gateway for procurement of these apps and adoption services.

Ming Tang
has been included in:
  • 100 Brands 2023 (EMEA)
  • No. 1 100 Brands 2021 (EMEA)
  • No. 2 100 Brands 2022 (EMEA)
  • No. 9 100 Brands 2024 (EMEA)