Introduction

With the advancement of new technologies and constant breakthroughs in artificial intelligence and data science, the nature of work is constantly changing. This is leading to the change of the labour market in general and other businesses in particular. In the present times, businesses are also responding to the value of digital innovations spurred by automation processes.

As such, emerging businesses need to adapt to the changing dynamics of the business market as data is constantly influencing information technology and the care economy. As a corollary, we are seeing the elements of this new economy constantly being influenced by sectoral skills of data science.

The data lens to derive insights

It appears that the various roles and skills related to data science are not a very critical part of the emerging economy in the present times. Paradoxically, when we look at the market share of jobs that are directly or indirectly related to data, we find this number to be around 45%. This is not astonishing because information technology is constantly influencing the sectors of media, finance, retail, manufacturing, logistics, education, and healthcare. This means that data science skills are being utilised by these sectors in various ways knowingly or unknowingly. This makes us conclude that the scope of data science is not limited to small and big industries alone. Its scope is getting diversified as the face of the economy is changing in various ways and through various factors.

Increasing sectoral demand of data science skills

Skills of data sciences are perfectly aligned to the novel innovations in various sectors. As per the Future of Jobs Report 2018, the skill cluster of data science that includes Oracle Big data, Hadoop and Hives would be pertinent for Information and communication technology, media, entertainment and professional services. This report also points to a high growth in data skills from 2013 to 2017. Sector wise, this growth has been about 35 percent for communication technologies, 46 percent for media and entertainment and 59 percent for financial services.

A blueprint of sectoral data science skills

Broadly speaking, there are six major data science skills that are crucial for various sectors. The skill of data management is finding applications in the healthcare, insurance, manufacturing, and automation industry. The skill of data visualisation is finding application in education, healthcare, and public policy making. Machine learning is being applied in manufacturing, e-commerce, insurance, and professional services. Statistics, programming, and business intelligence is finding its application in all the above mentioned sectors. A report by Coursera Global skills predicts cutting edge competition in the above sectors and that would undoubtedly be triggered by numerous data science skills. This report also calls for an upskilling and reskilling revolution so that the existing jobs in these sectors are safeguarded. If short term courses in statistical analysis, data management, and data visualization are introduced, it would help in coping with the demand supply mismatch cycle of data scientists.

The customized composition of data science skills

The beauty of data science is that it adapts to the skills needed in a particular sector. This is what we call the customized composition of data science skills. Some of the emerging roles that require customized data science skills include relationship consultants and sales development executives. Such skills that need to be customized according to the job requirements are deemed human centric soft skills. Apart from technical experience, such domains require a long lasting engagement with customers for understanding their requirements.

In the present times, we are witnessing the arrival of such domains that require a blend of data science and soft skills. An example of such domains is computer vision related jobs.

Future of data science jobs

In the future, we would expect the integration of data science with all other contemporary jobs. We may expect a major part of big data analytics to be carried out in the cloud environment. As such, cloud environs would also see the migration of other functionaries into its sphere. This means that the cloud ecosystem would serve as the interface of data science and other professions. This would lead to digital mobility, integrated marketing, customer personalization and better brand positioning in the long run. In addition to this, the domains that don’t seem to have a relationship with data science would be attracted towards it. For instance, aviation, travel and tourism, oil and gas, mining and minerals would all come under the garb of data science. This would become possible as analytics would become the next big thing for small and big companies worldwide.

Categories: Technology

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