The 7 Steps of the Data Science Lifecycle - Applying AI in Business

AI is not IT- and adopting artificial intelligence is almost nothing like adopting traditional software solutions. While software is deterministic, AI is probabilistic. The process of coaxing value from data with algorithms is a challenging and often time-consuming one. While non-technical AI project leaders and executives don't need to know how to clean data, write Python, or adjust for algorithmic drift – but they do have to understand the experimental process that subject-matter experts and data scientists go through to find value in data. Last week we covered the three phases of AI deployment, and this week we'll dive deeper in the seven steps of the data science lifecycle itself – and the aspects of the process that non-technical project leaders should understand.