on 12 December 18
With analytics and data science being projected as a continued area of growth within the business world it’s no surprise that it is increasingly being viewed with interest as a career option for many. Just like any field that is relatively new but has been growing fast since it began to develop, it’s also natural that in the early stages there’s a relative shortage of talent available to fill job openings. This obviously reflects in the compensation packages on offer for these jobs. Look at any listing of skills that are most in demand this year and you’ll find at least one related to analytics or Big Data.
The result, especially in many countries in Asia, Latin America and Eastern Europe, is that many people, both fresh out of university or even already working in other fields show interest in moving into a career in analytics. How easy is it going to be for them to do that, and how should they prepare themselves for such a move.
While it is true that there is a large and growing demand for analytics, one must first understand the chicken and egg situation that exists in this field as far as the supply and demand skillset gap is concerned. New jobs are created when companies decide to invest in analytics. But not every company has deep enough pockets to invest in this area over time when they cannot be sure of achieving a business benefit. To increase their chances of getting that benefit, they need the right people. But would the right people be those who want to switch tracks from other careers, or those who are fresh out of college. It’s possible, but the odds reflect a struggle for the aspirants. Therefore either the companies that are willing to invest must be aware that they’ll need to do so for some time, or the aspirants must already be fully qualified for the job. So what’s different about the qualifications required for these jobs?
The challenge for both parties is the peculiar mix of hard and soft skills that go into applied analytics. Analytics ideally requires a blend of three and sometimes four different types of knowledge-based skills: technology skills (particularly Big Data), statistical analysis skills, business analysis skills, business domain knowledge, plus a number of soft skills.
It’s the nature of the soft skills that might really tip the balance away from the aspirant, along with adequate experience in the hard skills. Analytics requires experience in working with data problems, not just a knowledge of the technicalities of working with data. In addition, at more senior levels, working with analytics requires being able to communicate well, being creative, and being able to create structured approaches and methods when confronted with vagueness. While it is relatively easy to acquire additional technical skills in either technology or statistical tools and analysis, the job requirement is all about drawing on experience and using an open mind to create an approach to potentially uncover insights, usually aided with hard skills.
So while the skills can come from doing a course or two, it’s really the experience, usually accumulated within a single business domain that counts for much more. It’s the same with the PMP courses for project management, or technology courses for a language like Java. Acquiring a knowledge of project management techniques is great, but becoming an effective project manager requires years of experience working in projects.
At the same time, industry needs to move on with its experiments with analytics, so for now, many companies either try to group people with the right individual skillsets together or they recruit less experienced people and train them by deploying them on activities that are lower down in the analytics value chain until they are ready to move on to higher value tasks. Aspirants who are passionate and determined enough to rough it out until they get to where they want to be will eventually make it, but they need to take the plunge with their eyes open to the realities.
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