There are multiple factors involved in the meteoric rise of data science.

First, the amount of data being collected keeps growing at an exponential rate. According to recent market research from the IBM Marketing Cloud (https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=WRL12345GBEN) something like 2.5 quintillion bytes are created every day (to give you an idea of how big that is, that's 2.5 billion of billion bytes), but yet only a tiny fraction of this data is ever analyzed, leaving tons of missed opportunities on the table.

Second, we're in the midst of a cognitive revolution that started a few years ago; almost every industry is jumping on the AI bandwagon, which includes natural language processing (NLP) and machine learning. Even though these fields existed for a long time, they have recently enjoyed the renewed attention to the point that they are now among the most popular courses in colleges as well as getting the lion's share of open source activities. It is clear that, if they are to survive, companies need to become more agile, move faster, and transform into digital businesses, and as the time available for decision-making is shrinking to near real-time, they must become fully data-driven. If you also include the fact that AI algorithms need high-quality data (and a lot of it) to work properly, we can start to understand the critical role played by data scientists.

Third, with advances in cloud technologies and the development of Platform as a Service (PaaS), access to massive compute engines and storage has never been easier or cheaper. Running big data workloads, once the purview of large corporations, is now available to smaller organizations or any individuals with a credit card; this, in turn, is fueling the growth of innovation across the board.

For these reasons, I have no doubt that, similar to the AI revolution, data science is here to stay and that its growth will continue for a long time. But we also can't ignore the fact that data science hasn't yet realized its full potential and produced the expected results, in particular helping companies in their transformation into data-driven organizations. Most often, the challenge is achieving that next step, which is to transform data science and analytics into a core business activity that ultimately enables clear-sighted, intelligent, bet-the-business decisions.