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Accompanying Challenges In The Campus And Analytics Domain

Accompanying Challenges In The Campus And Analytics Domain

Krithivasan S

In today’s high-tech era of technology, analytics is a relatively young area of work. It has evolved and expanded exponentially in the last few years. More and more companies are convinced about the immense value that analytics adds to the decision-making process in an organisation. In this increasingly complex time, when the life of data is becoming short, analytics is becoming increasingly useful day by day. This, of course, increases the requirement for analytics talent in the organisations. Analytics as a field has been growing at a very fast pace from the past couple of years and is expected to grow one and a half times from its current base over the next two to three years. However, IT industry faces a lot of challenges while hiring talent for analytics and some of the prominent setbacks which the industry has encountered are:

Mismatch In Skillsets

It is of pertinent importance that anyone who aspires to get into analytics field should be skilled in machine learning, deep learning, R, Python, TensorFlow and Spark ML, among others. However, students from engineering colleges do not have the coding background which makes it difficult to deploy them to critical areas without the basic training.

Programmer’s Mindset, Not A Manager’s

In most of the B Schools, analytics is a widely spoken topic. However, candidates who are truly interested in it are almost negligible in number. To have a successful career in analytics, one must possess the mindset of a programmer. The role of a data scientist, ML programmer and data analytics project is that of an individual contributor in the field of AI. Students are often not trained to possess a technical mindset towards Analytics field which is why they prefer to take up managerial roles.

Consulting Is Only A Part Of A Data Science

In a predominant number of B Schools, analytics is a course offered with main objectives towards building consulting positions. It is important to understand the fact that consulting is a part and parcel of a Data Scientist’s job and it’s not the only vital skill set which one should possess when he/she aspires to get into Analytics field.

Restricting Domain Exposure

An essential aspect of a successful analytics professional is to gain expertise in more than one domain and not to restrict oneself from exploring through and learning on the job. Analytics as a field has been growing in major domains like BFSI, healthcare, energy and utilities, among others. By developing exposure across domains, employability increases along with opportunities.

Deeper Knowledge Not Transformed

To achieve excellence in analytics and to become a skilled data scientist it requires expertise in mathematics, statistics, probability, and ML. In today’s scenario, an aspiring data scientist is only provided with the basic knowledge of these subject matters thus curbing the exposure of entering in the field.

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Lack Of Hands-On Training By Faculty Members

Data science as a program is being offered to the students by faculties who are often not equipped with sufficient hands-on training and industry exposure. This casts limitations on the knowledge that is being transferred to the students.

How Can These Challenges Be Overcome

To curb the above issues, companies have started working on various models to address these issues at the grassroot level. Organisations have started initiating unique programs which help train both the faculty and the students at the college level, programs such as:

  • Train The Trainer: A unique program that aims at upskilling faculty from the college campuses. This immersive learning is a new training technique that focuses on providing a practical experience rather than theoretical. In such programs, faculties undergo complete overview of various new age technologies such as Data Science, big data etc. Different use cases are provided to the faculties, to work on and they receive instant feedback from the trainers. These programs facilitate a natural shift in the knowledge of the faculty and enable them to undertake more practical formats of teaching.
  • Establishing COEs: Establishing Centers of Excellence at select campuses across the country, which has specialised labs for the learning on new age technology like data analytics etc. Different candidates are selected from the college campuses and are trained by the faculty who have already received industry-values training through train the trainer program. These COEs help attract the right talent, who are inclined towards becoming a data scientist and help them groom in the right direction. This model addresses the currently spoken dilemma of industry-academia gap.

As next steps towards progress, industry should work on creating similar COE models with B Schools, which will ensure that the current lack of deeper and valued course material is addressed, and the students are trained to face the field of Analytics from an industry perspective.

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