3 common reasons for analytics & AI project failures

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3 common reasons for analytics & AI project failures
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Read on for tips on scaling AI from Dataiku and IDC

Tip From IDC:"Consider the role of data scientists along with knowledge workers and industry expertise. Empowering knowledge workers will accelerate time to value."What team's can't afford in this macro-economic climate is for AI budgets to be reduced or cut entirely.

AI Governance delivers end-to-end model management at scale, with a focus on risk-adjusted value delivery and efficiency in AI scaling, all in alignment with regulations. Teams need to make distinctions between proof-of-concepts , self-service data initiatives, and industrialized data products, as well as the governance needs surrounding each.

An end-to-end platform brings cohesion across the steps of the analytics and AI project lifecycle and provides a consistent look, feel, and approach as teams move through those steps.

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