Entrepreneurship is a tough journey. It comes with a high degree of uncertainty and unpredictability, which makes it different from the conventional 9 to 5. While many of us dream of building something that we can truly call our own, very few can successfully make this transition.
There is intense competition among start-ups in the field of analytics and AI due to the huge opportunities offered by the industry. To motivate more and more people to start their own business in this area, we’ll look at some successful entrepreneurial paths.
Today, we take a look at the journey of Venkat Raman, co-founder of Aryma Labs, a data science consultancy, and understand what it’s like to start an analytics company in the current scenario.
Never easy to quit a high paying job
Raman has eleven years of industry experience in software engineering, industrial engineering, marketing and advertising, with a solid background in statistics.
“It’s never easy to quit a high paying job. My co-founder Ridhima Kumar had launched Aryma Labs a year before my arrival, ”adds Raman. The motivation behind starting Aryma Labs came from the realization that many companies have huge treasures of data but don’t know how to extract actionable insights from it. There is also a dearth of talent who can properly apply data science to business problems. This void led to the creation of Aryma Labs.
Aryma Labs is a data science start-up specializing in three key areas of market efficiency, time series forecasting and NLP. “We aim to provide customers with effective machine learning-based solutions that truly deliver long-term value. We provide these solutions in different fields and industries – CPG, manufacturing, e-commerce, supply chain, travel and hospitality, ”added Raman.
A huge shortage of good data scientists
Raman believes that the biggest challenge he has experienced with Kumar is the hiring and the current situation of COVID-19 (which has severely affected many sectors, around the world).
He says, “We have the numbers, but the quality just isn’t there. Choosing a good data scientist is literally like looking for a needle in a haystack.
Since data scientists are at the heart of any analytics business, Raman had to dig deep to solve the problem of hiring quality data scientists. The company has streamlined its process to allow for faster interviews. They hire people with good skills and then train them in statistical concepts. Sometimes it also requires unlearning the bad things they have learned. Once they have properly trained the interns and employees, they are put on real projects.
The first year is the biggest litmus test of an entrepreneur’s courage
As a young founder, Raman reports some important lessons he has learned over the past two and a half years of starting up. They are:
- Patience – Patience is a virtue at this time of expectation of instant results. As the young co-founder of a bootstrap start-up, he realized that patience is the key.
- Survive and never give up – The first year or so is the biggest litmus test of an entrepreneur’s courage and determination.
- Once the chasm is crossed, things gradually get easier.
Imperative to know your profession
If anyone wants to start a data science consultancy, a solid knowledge of statistics / machine learning and strong domain expertise is required for the business to take off. “The team grows around you much like a crystal grows around the first crystal particle,” adds Raman.
Data science as a field will evolve towards a policy of ‘doing more with less’
Raman believes that given the push for a smaller carbon footprint, data science as a field will evolve into a policy of “doing more with less”. This means that we are going to go back to basic statistical techniques. Statistics tend to emphasize the parsimony of models. In the 20th century, this emphasis was more due to technical constraints. But in the future, it will be for reasons like carbon footprint and data privacy, among others.