Who is more strong?…. Yes, that same old debate!

The existing skill-oriented gap between academia and industry is well known to all of us! It is believed that the gap is related to mostly methodological competencies. In reality, the gap can’t be categorized as only gap in methodological competency anymore. A revolution has happened in terms of data, quality of data and also the way it is stored. This digital revolution has come up with the buzz words like big data, artificial intelligence, machine learning and cloud computing.

In the domain of data science, massive inclusion of AI and machine learning have triggered the question of skill compatibility for industry. Industry gives the business problem and researchers provide the algorithms, and data scientists find the best possible solution to the on-going and upcoming business problems with help of these algorithms. We can’t deny that data science has got its top focus now-a-days. In recent times, our Finance Minister has announced that there will be good amount of investment in AI and automation of R&D. The emergence of need for AI compatible skill is coming from the business houses and Govt is also keen to promote it.

For last 30 years, India is the provider of cheap labor to global service sectors. Now the need of hour is skilled labour from a country like India as the day is not that far when mass level of manual work in the market will be replaced by advanced algorithms. India is quite far behind the countries like China, Japan where AI is the top priority in industry and more than sufficient amount of investment has already happened. It is high time when it should be realized that there is going to be a drastic change in the nature of business problem and accordingly the labour force is not equipped with proper knowledge to internalize this digital shock.

Are we techies or just tech-savvies?

In our country, a person begins her/his data science journey being an employee of a company and it is very common that a fresher is even completely unaware of the terms which are algorithm-specific of industrial business problems. It is observed that even at Masters level, students are not aware of on going issues about the new emerging algorithms, though the students are from computer science, statistics, mathematics and economics and may have specialized in econometrics, statistical analysis and operations research like disciplines which are the most focused areas of data science. In the general academic programmes, there is still no provision for data science. There are very few targeted executive business programmes where only overview of the domain can be shared. But this digital revolution requires inclusion of this domain in all possible main stream degree programmes, so that the students get entitled with the knowledge of algorithms in their basic degree course and then only a labour force can be built with fresh brains empowered with the knowledge of digitization in a multi-dimensional platform. This advancement of skilled labor will cause a delta shift in terms of state of economy as a whole and this can happen through a direct channel of academic curriculum itself. In the present circumstances, there already exists a huge gap in academic research and industry requirements. It is high time for India to bridge this gap as soon as possible by introducing digitization in academics in a direct manner. Now at the first step academics lacks in terms of proper faculty of resources to begin this process. Here the attention of big business houses is required as these corporates can come forward to give a big push to the existing curriculum by making their own research officials involved in the academic research institutes. Collaboration of industry specific research teams and academic research teams can work jointly to enhance the digitization in algorithms and reconstruct the older ones and invent newer solutions.

The Costly Trade-off and Hirschman’s Missing Linkage…

Ideally there should not be any trade-off between academics and industry. It is time when academics should get updated on a mass level and that can be possible by the joint effort and symbiotic association of academics and industry.

The core objectives of academia are development and transmission of knowledge and it should be reconstructed according to the changing factors of contemporary global issues. In a developing country like India, economic growth is of unbalanced category which was proposed by Hirschman (The Strategy of Economic Development, 1958). Hirschman mentioned about backward and forward linkages in the context of development. If a project encourages subsequent stages of production then it has forward linkage and if a project encourages earlier stages of production then we have backward linkage. In the development of an economy these two linkages play an active role to induce growth. In the present scenario, the economy is having forward linkage in terms of industry through digitization but it lacks either its backward linkage or the backward linkage is not properly facilitated which is caused by the existing gap in academics. Hirschman’s concept helps us to understand the present scenario in data science industry where huge amount of investment is required in academia and research institutes to sustain the capability of labor to cater to the upcoming needs of the industry. 

Old Problem and New Challenge!

Once Dean Kamen said ” Every once in a while , a new technology, an old problem and a big idea can turn into an innovation”; the basics of business problems are core to the industry but the nature and the way we approach these problems are changing with the dynamic characteristics of technology and hence targeted pool of labour force is required to address the old problems with newer approach and new problems with innovation. 

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