Gartner, in its recent 2015 Hype Cycles report for emerging technologies, took Big Data out of the hype. The reason for this “bold” action is quite logical; Big Data is no longer a class of technology per se. It is has become widespread in our lives across many hype cycles. Big Data is used on a daily basis, meaning we all have to learn new skill sets and knowledge.
Innovation brings significant changes in how technology and humans interact. Emerging technologies also tend to be very heavy on programming and computing so they require full specialization from the human teams in charge. Thanks to Big Data, we are learning a lot about humans and human interactions, and we understand more human behavior. As a consequence, we are able to adapt Big Data technology to become more user friendly, flexible, technically reliable, and it also has scalable performance and the capacity to integrate faster.
We are learning how to adapt technology to humans instead of humans adapting to technology. Having said that, by no means is there any intention to discredit the data scientists´ importance for businesses and innovation. In fact, human resources analysts have pointed out that there are not enough of these professionals in the market and salaries and fees are therefore increasing exponentially. Many universities have launched Data Scientist Specializations and Masters, however, it takes a number of years to acquire said degree and in the meantime, the world is spinning faster than ever. Oftentimes, universities deliver fresh data scientists with shiny diplomas but no “hands-on” experience.
However, even if we had a perfect team of data scientists on board, they alone cannot implement Big Data or data-based decision making in a business. Big input from Operations and Marketing is needed in order to succeed. For this reason, companies are delegating more and more Big Data projects to Development and Operations with IT coming as a support team. When it comes to innovation, the ideal situation is to work as a multidisciplinary team building bridges of skills and knowledge.
Everyone should have the right skills and mindset to cope with Big Data innovation as it takes place in all areas of an organization. IT must provide qualified data scientists while management needs to show leadership, understand new metrics and learn new ways of deployment. And employees must accept the importance of measuring everything and become more data-oriented.
How do we go about getting the right set of skills in place? How do we get our otherwise experienced management learning about Big Data? How do we train our employees? Sending management to full-time or even part-time university classes is not an option for tight professional agendas. So, how do we fill this knowledge gap?
The answer came in 2012 when open online courses positioned online learning as a powerful and fast way of education. Massive Open Online Courses (MOOCs), in addition to traditional course materials such as filmed lectures, readings and problem sets, also offer interactive learning platforms and user forums to support community interactions between students and teachers. Coursera and edX (open-source) are two examples of leading MOOCs providers; Khan Academy offers a range from general education to specialization courses in a wide variety of languages; FutureLearn is a leading UK learning platform. You can find all the specializations needed for Big Data trends in any of the aforementioned platforms: computer and machine programming, statistics, data analysis, regression models, agile project management, project management, critical thinking and so forth.
MOOCs have proven to be competitive and specialized. You can focus on learning specific skills that you want without the need to go through a lot of irrelevant information to your subject, typically included in some forms of traditional curricula. Also, most of MOOCs grading are project based, which forces you to interact with other students, form groups and learn how to work in teams – an essential skill for Big Data learned by any collateral effect. Therefore, by using a combination of universities and online courses, it is possible to fill in the skill gap in your workforce. Don’t discriminate based on an institution’s name or the learning mode. People interested in Big Data might surprise you, because no experienced manager normally wakes up on a Saturday morning deciding to learn how to do “bootstrapping” and engages actively in an advance statistical class online just because he has time off. This manager would be extremely motivated to do so, and frankly, this motivation is the skill you need.