How Macquarie Bank Built an Efficient Data Infrastructure for Digital Transformation
In this interview, Ashwin Sinha – Executive Director & Chief Data Officer at Macquarie Bank discusses the strategic topics at Macquarie Bank, such as engineering delivery acceleration, data governance, data literacy, and artificial intelligence. To stay ahead of the rapid pace of change in their field, Ashwin emphasises the importance of continuous learning.Digital transformation depends on the quality of your data and your ability to handle it.
In this interview, Ashwin Sinha – Executive Director & Chief Data Officer at Macquarie Bank discusses the strategic topics at Macquarie Bank, such as engineering delivery acceleration, data governance, data literacy, and artificial intelligence. To stay ahead of the rapid pace of change in their field, Ashwin emphasises the importance of continuous learning.
Data strategy is crucial in digital transformation. Transformation is an ongoing process and highlights the critical role of data quality and processing speed in digital transformation. There needs to be alignment between digital transformation strategy, data strategy, and technology modernisation strategy to avoid operating in silos.
By simplifying data interfaces, extracts, and dashboards, data strategy eliminates complexity in the data landscape. Modernise technology and consolidate data warehouses, including transitioning to cloud platforms. To support the bank’s overall simplification and modernisation goals, a streamlined and efficient data infrastructure is needed.
Ashwin discusses the importance of continuously uplifting risk management processes to meet regulatory requirements proactively. For management and board-level discussions, having accurate metrics to track progress and ensuring their reliability are crucial, especially for stakeholders who might not be data-savvy.
Key Takeaways:
- Generative AI fundamentally changes responsible AI. Update your AI and ethics governance framework. Artificial intelligence therefore requires broad ranging considerations.
- There has to be a very clear customer benefit in every use case of AI, a level of explainability and a broad range of risk considerations. Make sure you understand the responsible use and impact to customers and employees before using it.
- Data is a board level topic from both a risk and disruption perspective. Keep your communication with the Board simple. Describe where the data is now, where it will go in the medium term, and where it could go in the long term. There needs to be a focus on the organisation, the business, the risk you carry, and the innovation you can bring.