Generating meaningful insights from data is a challenge for many organisations, and it is important to manage expectations and define value in data initiatives. Trust and culture play a crucial role in data initiatives, and organisations need to foster a culture of trust and collaboration to ensure the quality and usability of data. Legacy organisations face challenges in managing data growth and need to address issues such as silos, data formats, and data quality. Agility and a data mindset are essential for organisations to effectively leverage data and drive growth and innovation. Choosing the right data tools requires aligning them with the organisation’s goals and ensuring they support the management and exploitation of data. When operating on a living production process, it is important to carefully consider the outcomes and the potential risks of transitioning to new systems.

Transitioning legacy systems requires parallel running and careful planning to minimise disruption and ensure a smooth transition. Building a bi-modal operating model can help balance the need for maintaining legacy systems while also driving innovation and building new systems. The organisational implications of transitioning include building the right operating model, organisational structure, and training programs to support the transition. The impact of Chat GPT on data management is still evolving, and while it has potential for refining and sharing insights, it is not yet ready for enterprise use.