Data Upskilling! The World Economic Forum forecast in its 2018 report titled “Future of Jobs” that 133M new jobs will be generated by the Fourth Industrial Revolution in 2022. Simultaneously, in the wide business subset alone, it would make 75M jobs redundant. The process would displace vast numbers of workers who lack the necessary valuable skills, like data analytics skillsets. “Industry 4.0” is almost upon us, says the World Economic Council, Accenture, and a number of global organizations and experts.
This industrialization includes increasing changes in technology, such as automation, digitization, and Artificial Intelligence, disruptive new business strategies, and increasing the complexity of the market.
These reforms will bring about a drastic shift both in the nature and quality of potential jobs as well as in the workforce structure. For current work positions, new skills and expertise will be required, while other conventional jobs and talents will merely become obsolete. This technical transition would cover the overall workforce, from low-skilled workers to high positions of competence.
IMPORTANCE OF UPSKILLING DATA AND ANALYTICS
The value of reskilling and upskilling workers to deal with the evolving nature of job responsibilities and procedures over the near future is increasingly being recognized by some leading companies. Collaboratively, Accenture, JP Morgan, and AT&T have promised more than $1.5B to develop and strengthen their employees to comply with potential job requirements.
Amazon has committed itself to upskilling a third of its workers. What is crucial to note is that many of these fast-growing work sectors are heading towards a stronger data and analytics emphasis, such as Marketing, Sales, and Content, Culture and People, the Service Economy, and the Environmental Economy.
The employment opportunities that involve skills in data analytics would be more common than in the area of data and Artificial Intelligence alone. In fact, about 2M new jobs will be affected by analytics in many other emerging job growth clusters found by the World Economic Forum.
7 REASONS UPSKILLING IS CRUCIAL
1. Data and analytics are omnipresent.
Big data has been widely regarded as a a result of digital devices and services over the last decade. Companies concentrated mainly on how to safely store, clean, and handle data. Businesses are now relying on data as “the new oil.” They perceive it as a resource which must be broken down further and optimised to produce revenues through data analytics and data science.
The problem is that data is all around us and the amount of data is skyrocketing. When it comes to the amount of data that will be generated and that will need to be analysed for business purposes, we are just at the tip of the iceberg.
2. The shortage of talent
Organizations are actually facing various challenges in the operationalization of advanced, reliable data use and are planning to upgrade more skills for data intelligence and analytics. They are currently facing a challenge in seeking the requisite talent to accomplish their goals.
While companies are seeking to be more data-driven, a lack of data expertise is holding back analytics and data initiatives, mostly due to a hyper-competitive employment market, high wage demands or extended needed to process data-related positions. Moreover, although with the initiatives of educational institutes to produce college grads with analytical degrees, it is not enough to address the talent gap.
The outcome is that data analytics and data science talent demand greatly outstrips availability. Reskilling and upskilling workers will help businesses develop the internal talent pool they need for data science and analytics and make themselves less dependent on costly talent that is difficult to recruit. Get yourself in-demand certs such as Microsoft data science certification from datascienceacademy.io to fill the talent gap.
3. Artificial Intelligence
It was clear from the WEF that artificial intelligence will be a major, if not the main, factor that drives a need to recruit and retain the workforce. Not only would AI displace a large proportion of the industry, but it will also become simply a part of administrative, business segment, and operational strategy and functions. This involves legal team to the production floor of business strategy teams..
4. The skills gap
One explanation for the skills shortage is that a broad range of skills are required to carry out the role of a data analyst or other market research professional, or to operate with data analytics as an operational workforce. The ideal individual for such roles is sometimes described as having abilities that are “T shaped.”
This implies that in one or two fields, such as programming, data science, or domain knowledge, and also related skills, such as analytical thinking, communication, and visualization skills, an analysis professional preferably has highly specialized skills. This is a difficult trait to find throughout the current work market.
5. Self-service data analytics
The marketplace has reacted to the shortage of automated systems for data scientists and data engineers to enable non-technical corporate users to participate in data discovery and exploration of informational insights. Many of these systems have data science capabilities that could go misused or unused without training on data principles. Business users would need to grasp data principles, such as data visualization, associations and correlations, and data exploration, to manage these systems.
6. Cross-functional teams
For most businesses, it has become quite evident that data science teams must be cross-functional to be able to accomplish data science goals and enlist the variety of expertise that data project teams require. Everybody on the project, including company and operational members of the team, needs to be informed about data principles.
7. The bottom-line cost
It is probably much easier to retrain current workers with the increasing cost of Data Scientist wages, the long time to recruit Data Scientists, and the increased turnover expense of a bad data science recruit. The added advantage of additional training the employees is that current workers are loyal already, have knowledge of the profession and industry, and fit into the culture of the business. Businesses get more value for their dollar by upskilling workers to be data educated.