Defining "Value" – the Key to AI Success

I recently conducted a 3-day, remote "Data Monetization: Thinking Like a Data Scientist" workshop for a transportation agency in the Middle East. Doing this training remotely is a personal challenge as I miss the face-to-face interaction in ideating, validating, and prioritizing the business areas that can benefit from data and analytics. However, conducting the workshop remotely did provide some valuable learnings for me. One learning was my "Thinking Like a Data Scientist" visual was outdated (Figure 1). Figure 1 portrayed the "Thinking Like a Data Scientist" (TLADS) process as a linear process, where you would complete one step and then cleanly move onto the next step. But in reality, the process is highly iterative where it is common for learnings from one step to impact an earlier step such as refining the KPIs against which the targeted business initiative's progress and success will be measured.