Let's get straight to the point from the start: I strongly think that the answer is yes.

However, that was not always the case. A few years back, when I first started hearing about data science as a concept, I initially thought that it was yet another marketing buzzword to describe an activity that already existed in the industry: Business Intelligence (BI). As a developer and architect working mostly on solving complex system integration problems, it was easy to convince myself that I didn't need to get directly involved in data science projects, even though it was obvious that their numbers were on the rise, the reason being that developers traditionally deal with data pipelines as black boxes that are accessible with well-defined APIs. However, in the last decade, we've seen exponential growth in data science interest both in academia and in the industry, to the point it became clear that this model would not be sustainable.

As data analytics are playing a bigger and bigger role in a company's operational processes, the developer's role was expanded to get closer to the algorithms and build the infrastructure that would run them in production. Another piece of evidence that data science has become the new gold rush is the extraordinary growth of data scientist jobs, which have been ranked number one for 2 years in a row on Glassdoor (https://www.prnewswire.com/news-releases/glassdoor-reveals-the-50-best-jobs-in-america-for-2017-300395188.html) and are consistently posted the most by employers on Indeed. Headhunters are also on the prowl on LinkedIn and other social media platforms, sending tons of recruiting messages to whoever has a profile showing any data science skills.

One of the main reasons behind all the investment being made into these new technologies is the hope that it will yield major improvements and greater efficiencies in the business. However, even though it is a growing field, data science in the enterprise today is still confined to experimentation instead of being a core activity as one would expect given all the hype. This has lead a lot of people to wonder if data science is a passing fad that will eventually subside and yet another technology bubble that will eventually pop, leaving a lot of people behind.

These are all good points, but I quickly realized that it was more than just a passing fad; more and more of the projects I was leading included the integration of data analytics into the core product features. Finally, it is when the IBM Watson Question Answering system won at a game of Jeopardy! against two experienced champions, that I became convinced that data science, along with the cloud, big data, and Artificial Intelligence (AI), was here to stay and would eventually change the way we think about computer science.

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.