Although many organizations are using artificial intelligence (AI) and machine language (ML) tools as core enablers in their data analytics projects, and AI spending worldwide continues to rise, the hard truth is that most data science projects are doomed to fail.
There are several reasons for these failures, ranging from the inherent complexity of AI/ML initiatives and the persistent lack of skilled talent to challenges that exist in data security, governance, and data integration. These issues are collectively referred to as concerns for” data readiness,” according to an IDC global survey of more than 2,000 IT and line-of-business decision-makers, all of whom are involved in some level of AI use or development.
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(Insider Story)
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Source: News