Four fundamentals to building a data strategy that supports generative AI

Organisations that already use data throughout their businesses are better positioned to move faster and more effectively with generative AI. Those organisations with a mature data strategy can approach generative AI by encouraging organisation-wide learning about the capabilities of the technology, where it fits and doesn’t, and how it can potentially free up time for us humans to undertake those activities that we are uniquely positioned and motivated to do. Change management sits at the heart of this approach and is led from the top.

For organisations that haven’t yet established a data strategy, here are the four fundamentals I recommend:

First, embrace the power of the cloud. The cloud provides a cost effective, scalable, secure, and sustainable means to bring together vast amounts of structured and unstructured data. Getting your infrastructure established in the cloud will give you access to an array of tools and resources for gathering, sorting, analysing, and using your data to find efficiencies and drive innovation.

Second, implement an end-to-end data strategy that treats data as a strategic resource and includes investing in the right solutions, people, processes, and tools. Dismantling data silos, for example, to enable sharing data across departments is one way to treat data as a resource and make it actionable.

Third, promote data literacy from the top. More often than not, organisational problems arise not from technical issues but from leadership behaviors that don’t encourage the use of data. Whether you want to experiment with generative AI, gain insights into customer behaviors, or use machine learning to drive efficiencies in areas such as supply chain and logistics, everyone in your organization needs to understand how to work with data to drive innovation.

Finally, for organisations at any stage of the data journey, building and deploying responsible AI has become a priority across all sectors. Individuals tasked with leading responsible AI efforts are shifting their focus from establishing high-level principles and guidance to managing the system-level change necessary to make responsible AI a reality. In other words, when it comes to generative AI, turning responsible AI from theory into practice is more important than ever.

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