| | JULY 20259CIOReviewThe idea is brilliant, and there are many examples of where it has been successfully implemented, especially for new `digital-first' companies as they scaletesting. Open lines of communications from in-market support to upstream R&D to ensure new requirements are captured and actioned. · Lifecycle management. For data, systems, and models. Fully documented.On the deep technical side, key questions business leaders may include: how do we better utilize our existing assets and tap into the emerging AI capabilities such as; large sparse tabular dataset modeling, graph neural networks to connect `puddles' of data and models, transformers for natural language understanding and computer vision, generative / diffusion models that are leading to `creative computing' applications ... AND CREATE BUSINESS VALUE. The reality is that much of the data in the enterprise is not structured, not accessible, not documented sufficiently, and was likely not collected with the intent to be consumed by an algorithm. This doesn't mean it should all be ignored, but know that additional effort will be needed to refactor this data so that it might be useful. Likely costs include re-basing the data to be fed to an algorithm, annotating data for new features of interest to the business question to be answered, and investments in re-training the workforce to work in a new way to better feed the algorithms and to integrate results from models across modalities or disciplines to gain insights.
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