Many organizations fail to maximize the benefits obtained through data science projects. One crucial aspect that often goes unnoticed is not the technical barriers, but rather to the gap that exists between stakeholders, CEOs, and data experts.
This gap is visible in several ways. Firstly, there will be an overestimation of Machine Learning capabilities by the top management. Recent industrial statistics reveal that approximately 70% of CEOs anticipate significant outcomes from ML, yet only 20% of projects manage to achieve the goals.
Secondly, there is a notable lack of understanding about the data science and its limitations. A staggering 60% of CEOs confess to having limited knowledge of the data science and ML.
A critical challenge arises as CEOs often struggle to coordinate with data science departments due to their terminology. Similarly, data scientists face a huge difficulty in translating technical jargon into non-technical business terms.
To tackle this issue effectively, it is crucial to educate CEOs and the top stakeholders on non-technical ML and AI concepts. This empowers them to provide effective guidance projects. Apart from that, non-technical organizations can bring in external AI and ML experts to evaluate, monitor, and support projects. This approach frees up valuable time for busy business stakeholders and CEOs.
I’d love to hear examples of the challenges you’re currently facing and help you to addresses these issues.