Data scientists have really came into their own in recent years. As predicted nearly 10 years ago by the then chief economist at Google, Hal Varian, who stated in an interview in October 2008:
I keep saying the sexiest job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexiest job of the 1990s?
Varian was one of the first to recognise the strategic importance of extracting information from data, and not just at a corporate level. “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades. Not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it”.
A highly expected professional
The truth is that in 2008 a few companies had already incorporated a professional to manage very large volumes of information, of vast variety and scope, in a quest to uncover useful insights relevant to the business. But until then nobody had called them “data scientists”. The first to do so were DJ Patil and Jeff Hammerbacher, then the respective heads of data analytics at LinkedIn and Facebook.
Nine years later, Varian’s predictions have definitely hit the mark. According to the McKinsey Global Institute report Game Changers: Five opportunities for US growth and renewal, the Big Data industry in the United States could increase annual GDP by 325 billion dollars by 2020. According to the same report, the United States alone will face a shortage of up to 190,000 data scientists and 1.5 million professionals with enough proficiency to use Big Data effectively. The report estimates that 40,000 exabytes of data will be created by 2020 and highlights the need for organisations to ensure they have the resource and talent to conduct in-depth analysis. Only in-depth analysis offers the chance to reveal patterns and trends, for insights to help direct a business, streamline processes and optimise decision making.
Many of the larger and pioneering companies already have a data head within the business. The recent appearance and high demand expected for these professionals over the coming years, confirm that there is a growing need for organisations to process large volumes of information and transform it into a valuable asset, as data in its “raw state” is simply meaningless. Only in-depth analysis offers the chance to reveal patterns and trends, for insights to help direct a business, streamline processes and optimise decision making.
Data science and data scientist: a clear mission
Data Science stands as the process that enables the collection, preparation, analysis, visualisation, management and preservation of large volumes of data. Extracting valuable insights from all types of sources provides answers to vital strategic questions for any business, such as those related to time and cost savings, new product development, the optimisation of offers and faster and more accurate decision-making processes.
But what does a data scientist exactly do? Here at Good Rebels we wanted to outline a profile of this recent profession, with the help of various industry leaders from academia, business and institutions. We could conclude that the main tasks of a data scientist are to identify data, transform them when incomplete, group them together, prepare them for analysis, perform the analysis, visualise and communicate the results. To do that, the data scientist must have a technical and analytical training, not forgetting the ability to focus on creating value for the company. This is why, in a competitive scenario where challenges are constantly renewed and data doesn’t stop flowing, the data scientist’s work enables managers to move from ad hoc analysis to an ongoing conversation with the data.
In the process of recognising the status of the data scientists, it’s vital to mention a fundamental advance in their professional acknowledgement: they have taken on the crucial responsibility to commit towards improving company results. Their mission is no longer limited to guiding or advising the actions of other departments, nor to crunch data to later present it to managers responsible for decision-making. The data scientist’s work culminates with the delivery of new business opportunities founded on the comprehensive inspection and translation of data.
A hard to find species
What kind of professional is able to perform this mission? The data scientist is a sort of hybrid between a programmer, analyst, communicator and an advisor. With proficiency in Statistics, Technology, Maths and Data Architecture. All this without forgetting empathetic virtues. A skill set very difficult to come across in one person…
Professionals refer to this ideal person, given the practical impossibility of finding one on the market, with tags such as “El Dorado, “Unicorn”,” The Data Science Superhero”, “The Dark Beast” or “The New Renaissance Man”. An extremely powerful combination… and very hard to find, because demand is growing and professionals are in short supply. The solution: training, recycling and creation of multidisciplinary teams, which together can integrate a profile like the one described.
This article is part of the study “Data Scientists: Who are they? What do they do? How do they work?“, coming soon from Rebel Thinking.