Data science, artificial intelligence (AI), and machine learning are no longer just buzzwords; they have become realities in today’s technology-driven world. The term ‘data science’ emerged in the early 1960s within the fields of statistics and computer science. It was initially introduced to highlight the crucial process of extracting valuable insights and knowledge from data. Fast forward 50 years, and we see this field rapidly evolving, with the Data Scientist profession being recognised by Forbes as ‘The Sexiest Job of the 21st Century’.
As technology has continued to advance and the volume of data has grown, organisations have recognised the increasing potential to collect, analyse, interpret, and extract valuable insights from data. As a result, this expanding scope has driven executives to carefully consider their data strategies. Considering that as early as 2018, a staggering 2.5 quintillion bytes of data were already being generated daily, it would seem silly not to jump on the bandwagon, wouldn’t it?
Despite the promises of a streamlined and efficient future with data science advancements, where automation and instant insights are within reach, reality often fails to live up to the hype. In fact, in 2015, Gartner Research estimated that 60% of big data projects would fail. In 2017, this figure was revised to 85%.
While the potential that resides in data science pursuits is massive, it isn’t simply a magic bullet that will single-handedly solve an organisation’s most pressing issues. Leadership needs to seriously consider whether or not data science is right for the organisation, which involves far more than simply hiring a professional or two and selecting software to implement. To be successful in these endeavours, leaders need to be able to think like data scientists. The first step in this journey, however, is to understand what data science is about, both as a general concept and, more importantly, for the organisation.
