A job that grows with automation

Data Analytics as a career is growing in importance with each passing day.  The demand–supply gap of talent in the field remains a huge concern. This is what, where, how one can make it.

In a world that is rapidly trotting towards digitisation or automation, data management, its analysis and practical interpretation has become vital for organisations. While platforms such as Big Data and Cloud have efficiently taken care of data storage and data management — including data cleaning, sorting, updating and guarding — there is one aspect that still relies on the human brain, and that is data analysis and interpretation. Therefore, futuristic organisations are eagerly seeking and developing talent for data analytics.

However, these organisations face a huge challenge as the supply for talent in data analytics is quite limited while the demand for the same is rapidly growing. Shakun Khanna, senior director, HCM Transformational, Oracle says, “The field of analytics faces a massive talent crunch, specifically HR analytics, as people are aware of data analytics in financial terms but HR analytics is relatively new and the domain is still seeking to develop talent and capabilities. It is, in fact, among the top five most difficult fields from a talent crunch perspective.”

Nathan S.V, human resources leader, Deloitte, says, “There is a huge demand for people with data analytical skills. The last four years have thrown up a four-fold increase in the demand for such people. Data analytics engineers, who can understand, analyse and interpret data, are in big demand, and data scientists who define the analytics and solve problems are in even greater demand.” Nathan also shared that organisations are so keen to develop data analytics professionals that some companies have started their own training programmes to create data analysts using their own tools. Learning Hadoop is getting to be a big leg up for such people.

Bhuvan Nijhawan, regional director, education & academia, SAS South Asia, says, “There is a lot of demand across the globe for talent in analytics.” He shares that there are various studies showing the talent gap in analytics. As per Mckinsey, by 2018, the US alone may face a 50–60 per cent gap between supply and requisite demand of deep analytic talent. McKinsey estimates that for every data scientist, organisations will need ten data-savvy managers with the skills and understanding to make decisions based on data analysis. Similar trends are available across countries. As per Accenture Analytics, 2016, more than 90 per cent of those surveyed (sample included leading Indian and MNC companies across industries) said that they planned to hire more employees with expertise in data science—most within a year.

So what makes for a qualified data analytics professional? Of course, a background in maths, physics and statistics is useful. Nathan says, “A fondness for calculus, algebra, software engineering and solving problems makes the life of such a person interesting.” In the context, Khanna says, “When organisations seek data analysts, they don’t look for technicians but those with business acumen and proficiency in statistics. The person should have a natural analytical bent of mind, a sense of inquisitiveness and an ability to connect the dots.” Khanna feels that it is an understanding of both the art and science of data that makes one a balanced data analytics professional. In addition, it is also crucial for one to be always up-to-date on the latest tools and technologies in analytics.

Nijhawan shares that the graduating courses one can take for making a career in analytics include – Bachelor’s/PG/PhD in mathematics, science, computer science, physics, engineering, econometrics, statistics, pharma, management. Apart from this, a formal course, such as a certificate or degree course in the area of business analytics or data science from a leading institute or corporate academy would also attract employers.

The leading institutes that offer courses in business analytics include, IIM-B; IIM-L; IIM-Ranchi; Indian School of Business (ISB), Hyderabad; Narsee Monjee Institute of Management Studies (NMIMS), Bangalore; Great Lakes Institute of Management, Chennai; IMT, Ghaziabad; MISB Bocconi, Mumbai and a few more.

The money matters
Since the demand supply gap of talent in data analytics is quite broad, the compensation offered is naturally lucrative as organisations desperately look for efficient resources. The pay packages offered to data analytics professionals easily start from about Rs 5–10 lakh per annum and can go up to Rs 75 lakh and beyond for highly experienced and skilled professionals. Nathan reveals that depending on the experience profile, a data analytics professional can earn anything from 6 lacs to 15 lacs in the first five years. The average cost is going up by around 20 per cent each year, given the excess demand. Moreover, those who come from better schools like the Indian Statistical Institute command a premium in the market.

Nijhawan says, “The average compensation varies depending on the experience of the candidate and also the quality of knowledge.” For freshers, at the associate consultant or junior consultant level, it can be anywhere around 5-10 lakhs per annum. For senior consultants or managers, it varies across organisations, but typically ranges between 10 lacs–30 lacs per annum. For senior managers, directors or VPs, it may range from 30 lacs–75 lacs+ per annum.

The growth path
Organisations are now realising the importance of data scientists, which is why it is not so unusual now to find the position of a chief data scientist in most far-sighted organisations. Even HR analytics has become an area of extreme importance as organisations look to optimally utilise their most expensive resource–people. Khanna says, “Most organisations these days have an analytics head or chief analytics officer just as they have a talent head or a CLO.”

With regard to the career ladder of a data analytics professional, Nathan explains that people usually graduate once they grow intellectually in their jobs and careers. In a way, it is not the number of years but the ability of people to understand data, interpret it, and then represent it to communicate the findings in the form of stories and powerful insights. “This comes through several years of research and the ability to use data as a friend to tell the truth and sell the truth,” Nathan says. “It is an exciting field, but calls for a great amount of curiosity and of course patience, besides skills and problem solving,” he concludes.

Professionals in analytics can graduate from being an analyst, to senior analyst, data engineer, senior data engineer, then project manager and finally a chief data scientist. Designations may vary across organisations but the role certainly gets more exciting over time.