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.
Making matters worse, while most companies routinely maintain large amounts of data, it is often stockpiled in functional silos and not easily accessed or used across these boundaries. Advances in cloud computing, data engineering tools, and machine learning algorithms are also coming faster than products and new processes can be deployed. Then there are the competitive challenges that come from both traditional channels and new, disruptive technologies.
To overcome this reality and create new value for customers and shareholders, IT leaders must create a community and culture that can accelerate and sustain the growth of data science and analytics throughout a company.