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The Need for Occupational Maps to Build Reskilling Frameworks

Many of the new age roles do not have proper levelling and understanding of progressions.  To combat this issue, we are developing what is called Occupational maps.  This will help enterprises navigate the new-age job families and related skills to standardize job roles and build viable career paths.

The advent of digital technologies has increased the need for advanced skills in both industrialized and emerging economies. There is a huge need for enterprises to build reskilling programs to maximize opportunities at the workplace and stay ahead in the talent market.

Enterprises need to have high awareness about these new-age job families, skills and related career paths to frame an efficient reskilling module. Occupational Standards defined by various skills Councils are not effective in defining new-age digital roles.  Some of the common challenges with the existing taxonomies include the following:

  • Lack of classification of these new technology job families. They are broadly classified under IT or Software Development.
  • Existing taxonomy does not showcase the differences between organizational and business contexts in job roles.

Occupational map is the first step towards workforce development with scanning of new-age job families, workloads and skills across company/industry level. At Draup, we have come up with an analysis of AI/Big Data job roles corresponding to various job families:

  • Architecture
  • Administration & Governance
  • Engineering
  • Analysis
  • Strategy

Building an occupational map for AI/Big-data job families will help enterprises gain clarity on the skill clusters and the related career-paths. These occupational maps also help in identifying the various reskilling paths job roles that can be transitioned towards a different job family.

Occupational maps help organizations realize that not every job role transition requires a reskilling framework. The idea is to build customized learning modules by identifying the adjacent job roles/skills and forecasting the possible career paths.

L&D leaders can leverage occupational maps to recognize the prominent AI/Big Data career paths and tailor offline/online learning courses based on specific industry requirements.

Here are some examples that will give you an idea on the changing job roles within/across AI/Big Data job families:

  • Same-family transformation: Switching to job roles within the family overlaps some skillsets and requires minimal training to attain a new career path. For e.g. – A ‘Business Intelligence Analyst’ can handle both ‘Data Quality’ and ‘Visualization’ workloads as it has adjacent skills and organizations need fewer resources to fulfill these tasks.
  • Cross-functional transition: Elevating a ‘DevOps Engineer’ towards a ‘Data Architect’ profile by leveraging custom-built reskilling path with relevant micro/macro learning modules.

Draup’s understanding of digital job families and skill clusters across AI/ML, Big Data, etc. is paving way for the development of occupational maps. We are on-road to help enterprises gain clarity and comprehend the digital job families that can assist HR leaders in creating a robust skill development framework.