In mid-2019, I decided to quit my job and re-train as a data scientist. I took this life-changing decision following a significant period of reflection. During this, I came across the Japanese concept of ikigai (pronounced ee-kee-guy). Ikigai has not got a direct translation, but can loosely be interpreted as “the thing that you live for”.
According to this Japanese life philosophy, this “thing that you live for” can be found at the point at which four elements converge:
- What you love
- What you are good at
- What the world needs
- What you can be paid for
The post that follows explains my rationale for embarking upon this exciting career change, using ikigai as a framework.
But first…
What is data science?
Loosely speaking, data science brings together statistics, programming and business intelligence with the goal of creating value from data.
So with this in mind…
Why have I chosen to become a data scientist?
Let’s return to the four pillars of ikigai.
What do I love doing? Maths, Stats and Solving Problems
Whilst studying economics at university, I discovered a love of applied maths, probability and statistics. I found it deeply satisfying to use real life data to prove or disprove any number of hypotheses. However, upon graduation, driven by the need to get a job as quickly as possible, I didn’t consider that this interest and these skills could be developed into a career.
Instead, I followed a path well-trodden by students. I accepted a job on a graduate scheme. In my case, this was the graduate scheme of Accenture, a consulting firm. For the 2 years that followed, I worked as an analyst on a business intelligence project for a large telecommunications provider. By chance, this gave me an excellent opportunity to understand the complexities of managing, and gathering insight from huge volumes of data. I’m not sure I realised it at the time, but this formative experience planted the seed that would later contribute to my decision to become a data scientist.
What am I good at? Being Commercial
In 2012, I left Accenture. Whilst it had provided an excellent introduction to the field of data collection, I was craving a new challenge. I was eager to become better connected with the commercial world and learn how to run a business. Since 2012, I have therefore mainly specialised in business leadership. First, as CEO of a small business engaged in the manufacture of meteorological and environmental instrumentation, and latterly running a UK subsidiary of Eurofins, a leading provider of laboratory analysis. During my time at the helm of both small businesses, I was exposed to many different situations and challenges. These experiences have allowed me to hone my commercial acumen, build skills in stakeholder management and communication.
An effective data scientist starts by asking commercially relevant questions, and finishes with clear and understandable communication of the results to a variety of technical and non-technical stakeholders. My background in business provides a strong foundation from which I can do this.
What does the world need? Those who can bring value to data
We live in a world where the volume of data produced is mind-blowing. In 2018, 90% of the world’s data had been generated in the 2 preceding years. In that same year, 2018, 33 trillion GB was created. It is estimated that this will grow to 175 trillion GB by 2025. To put this in context, if you were able to download the world’s 2025 data production , it would take you 1.8 billion years to do so (assuming a connection speed of 25 Mb/s).
However, un-analysed, data has little value. A 2012 report by the International Data Corporation (IDC), revealed that less than 1% of the world’s data resources had been analysed. The chasm, between data availability and data exploitation represents an enormous opportunity for business, and creates significant demand for data scientists. This is an opportunity that I would like to embrace.
What can you be paid for? Data Science
Given the recent data explosion, it is unsurprising that there has been an equivalent proliferation in job openings for data scientists. A 2019 report by the Royal Society found that British employers posted 27,033 data science roles in the 12 month period July 2017 - June 2018. This can be compared to the January - December 2013 period, in which 8,157 ads were placed. This works out at an increase of 231% in 5 years.
Elsewhere in the world, 2019 saw Glassdoor rank Data Scientist as the top job in the US for the fourth year in a row. This ranking is based on three equally weighted factors: earning potential, job satisfaction and number of job opportunities.
With many other reports drawing similar conclusions, it is clear that Data Science is a robust career choice.
How can I become a Data Scientist?
So, after serious consideration, it became clear to me that data science was a career choice through which I could achieve my ikigai. In order to take practical steps towards achieving this, I signed up for, and started, a 5 month online bootcamp with Flatiron School. It feels great to have launched myself into the world of data science.