As organisations take a thoughtful approach to developing unique analytics strategies and drive impactful outcomes, the requirement for analytics professionals is growing. While, so far the hype has always been centred around Data Scientists, billed as the “sexiest job of the 21st century”, there is also a booming demand for a cross-functional data science team that includes Data Analysts, Data Architects, DevOps engineers, Data Engineers and Data Visualization professionals.
As companies plow in more investments to realise impact from analytics initiatives, the need for cross-functional teams and data science talent has never been more acute. In our recent Analytics & Data Science study, we highlighted the overall growth in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000.
Data Science Job Market Is Booming
Analytics professionals command higher compensation and outearn their peers considerably. As per data from our Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Furthermore, this year, the entry level professionals ((0-3 years experience) saw a slight increase in salaries, with compensation going up from ₹5.2L median last year to ₹5.3L per annum. At the entry level, almost 76% of analytics professionals earn under 6 lakh per annum.
Enterprises Double Down on Analytics Initiatives
As organisations continue to widen their analytics efforts, there is a rising demand for analytics professionals who work at the intersection of data and business, can prioritise business problems, and can help organisations achieve real impact from their data-driven initiatives. From expertise in programming to querying, modelling and expertise with large datasets, analytics professionals are also required to have a deep understanding of industry dynamics to gain value from data.
Here’s What the Industry Demands
Strong educational background: In order to work with agile teams, organisations look out for candidates with a Bachelor’s degree or MS in quantitative fields like engineering, Math, Statistics and more popularly CS. In most cases, an MS is a huge advantage.
Tool-chain: Knowledge of programming like Python and R is a must to break into the data science field. In addition to this, one should also have proficiency in statistical packages such as SAS or SPSS that are also used in organisations.
Experience working with large datasets: As data grows by leaps and bounds, enterprises are dealing with both structured and unstructured data. Our study confirms that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Domain knowledge: Data analytics professionals have to work at the intersection of business and technology and drive result-driven projects. One of the key aspects is to understand key operational metrics and the impact on business. In addition, knowledge of common use cases such as churn prediction (telecom), customer acquisition (e-marketplaces), inventory management (e-marketplaces), predictive maintenance (manufacturing) can be a plus.
Soft skills: In today’s business landscape, soft skills have emerged as one of the most in-demand capabilities to bridge business and technology agenda, communicate effectively with multiple stakeholders, discuss findings with co-workers and clients and drive result-driven projects.
Need for Standardization in Training and Education
While data is the next frontier of innovation, industry leaders have often talked about the shortage of talent in data science field and the hurdles faced in attracting and retaining talent. The gap has driven a fierce competition amongst enterprises and high-growth companies for scarce analytics talent. This in turn has spawned a slew of Learning & Development initiative to nurture and retain the right talent and build high-performing cross-functional teams, drive a data-driven culture and lead transformational change.
Now as the need for data science grows, educational stakeholders need to play a critical role in addressing the talent gap and building an industry-relevant curriculum that churns out job-ready professionals who fulfil the requirements and have the qualifications for data science roles.
“India has a great opportunity to reposition its (perhaps failing) higher education for the new data economy by leveraging its varied and mammoth talent pool from under-graduate level onwards with assessment credits more on pertinent problem-solving activities through analytical capacity-building as part of University curriculum currency in tandem with Industry 4.0,” said Professor Krishnendu Sarkar, Director, NSHM School of Computing & Analytics.
Also, as the buzz around Data Science grows, we believe that professionals looking to transition into this field find it hard to understand the requirements for a certain position and set out on their own learning and development programme, which may not add value to the organisation in the end. This has resulted in a highly fragmented market which can be confusing for candidates as well as recruiters. And this has driven a need for standardization of data science education which can also help companies find the right match for a job. In addition to this, standardised degree programmes can help in advancing the career path for students and also support in building a healthy analytics ecosystem for organisations.
