1. Companies are Facing Real
Challenges in Organizing Data
Throughout
the IT boom of the late 1990’s and early 2000’s the focus was on ‘lift and
shift’ of offline business processes into automated computer systems. Over
these two decades, there has been consistent electronic content creation,
transactional data generation, and streams of data logs. This means every
organization is now sitting on a pile of data that it knows can be of rich
value but doesn’t know how. This gets reflected in Glassdoor’s recently
released report which highlights the 50 best jobs in recent times.
Unsurprisingly, Data Scientist Jobs is at the top spot for the second year in a
row with a score of 4.8/ 5.
2. Shortage of Skilled Resources
A
McKinsey Global Institute study states that the US will face a shortage of
about 190,000 data scientists and 1.5 million managers and analysts who can
understand and make decisions using Big Data by 2018. The
demand is particularly acute in India, where the technologies and tools now
exist but not the skilled users. In fact, co-founder and
CEO of Fractal Analytics, Srikanth Velamakanni says, “There are two
types of talent deficits: Data Scientists, who
can perform analytics and Analytics Consultant, who
can understand and use data. The talent supply for these job titles, especially
Data Scientists is extremely scarce, and the demand is huge.”
3. The Pay is Great
A
Data Science job is among the top-paying in the industry right now. According
to GlassDoor, the national average salary for data scientist/analyst tops more
than $62,000 in the U.S. In India, the experience strongly influences the pay.
Those with the right skillset earn as high as 19 LPA (Source: PayScale).
4. The ‘X’ Factor
Being
a Data Scientist is hailed as being incredibly cool with top data scientists
working at Google, LinkedIn, Facebook, Amazon, and Twitter. It is no wonder
that the Harvard Business Review branded Data Scientist as ‘the sexiest
job of the 21st Century’. The responsibilities of a Data
Scientist are exceptional and unique to the job role. The nature of their work
allows them to advance in their career, incorporating multiple analytical
skills over various domains such as machine learning, big data, etc. This broad
pool of knowledge provides them with an irreplaceable repute or X-factor.
5. Democratization of Data
Scientists
The
need for data scientists is no longer restricted to tech giants. The Harvard
Business Review had reported long back that, “Companies in the top third of their
industry in the use of data-driven decision making were, on average, 5% more
productive and 6% more profitable than their competitors.” This
has finally led even mid to small startups to look towards data
sciences. In fact, many smaller firms look to hire entry-level data
scientists at decent pay. This works well for both. The scientist finds a
significant ground to hone his/ her skills while the organization can afford to
pay lesser than what it otherwise would have to.
6. Low Entry Barriers for
Existing Professionals
Since
data science is, comparatively, a new field, it allows entry to diverse
professionals from an array of different backgrounds. Many current Data
Scientists hail from mathematics/statistics, computer science, engineering, and
natural science disciplines. Some even have degrees in economics, social
science, and business. They have all managed to foster a problem-solving spirit
and have upskilled themselves through online or formal courses.
7. Omnipresence of Jobs
Since
industries from manufacturing to healthcare, IT to banking are leveraging data
science in some capacity, there is no dearth of Data Science Jobs for anyone
who is interested and is willing to work hard. This is not just limited to
industries but also across geographies. So, irrespective of someone’s
geographical placement or current domain, data science and analytics is open
for everyone to pursue.
8. Plethora of Roles
While Data
Science Jobs is an overarching term, within its larger meaning many other
sub-roles are available. Roles such as that of a Data Scientist, Data
Architect, BI Engineer, Business Analyst, Data Engineer, Database Administrator,
Data- and Analytics Manager are in high demand.