How do you identify the insights or outcomes that best showcase the value of your data and analytics function? What kind of framework or criteria do you use to evaluate possible data narratives?

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Chief Technology Officer in Software2 months ago

From my personal experience, it's crucial to consider the different stakeholders involved. Some are quite technical, like our data engineering and data science teams, and the application team. However, there are also stakeholders who come from the business side. The narrative or storytelling is very important. You need to present your study or new findings in a format that can be digested by your different stakeholders.

Before going into top management meetings, we usually create a consensus among other stakeholders who are probably the first level deputies or the skip level reporting of the top management like CIO or CTO. We make a story and narrative around the governance model or how the data can be given to the AI and ML team so that they can use it for their applications.

We don't use a specific framework, but we follow the best practices available in the industry.

This includes considering the security framework you have, what kind of access policy you have for your data, and how the applications are actually consuming your data.

For example, in a banking environment, there are various applications consuming your data. We show the value we're creating as a data and analytics practice by providing insights to the entire organization and to each department.

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Sr. Director, GenAI Program Management in Healthcare and Biotech2 months ago

I think it's really important to have a robust and easy-to-use intake process that caters to all personas across the organization. This allows them a portal to convey their ideas that revolve around data and analytics solutioning.

We lift the visualization of the funnel up to leadership. The framework we use for visualization, ranking, and priority is through a cross-section of risk, value, and feasibility of whatever the ask is.

In today's climate with AI law being written daily, risk is at the forefront of any use case that is submitted in that vein. We want to ensure safe and ethical use of AI. At the same time, we want to know whether it's a feasible solution and what the value proposition of it is.

There are ways visually to articulate this at a use case level as well as at a broader portfolio view. Your organization can then begin to consume this at large. They can start to have meaningful and intentional conversations about where to prioritize and sequence those efforts.