The shift I have witnessed
When I look back at the evolution of data science, what stands out to me is how much the discipline has changed, not just in the tools we use, but in the way we think about solving problems.
In the early stages of my career, data science was often defined by the amount of work required before meaningful insights could be uncovered. A significant part of our time was spent collecting data, cleaning imperfect datasets, building models from the ground up, and manually testing different approaches to understand what worked best.
That process required patience and precision. Data scientists had to understand the details behind every dataset, make careful decisions about how information was prepared, and continuously refine models until they could provide reliable results. The work was challenging, but it also built a strong foundation for the field.
Over time, however, the world around us changed. Businesses began generating more data than ever before, and the demand for faster, more intelligent decision-making continued to grow. Traditional approaches to data science remained valuable, but organisations needed new ways to experiment, innovate, and respond to increasingly complex challenges.
