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5 Things To Keep In Mind While Launching A Data Science Startup

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5 Things To Keep In Mind While Launching A Data Science Startup

Key takeaways

  • Launching a data science startup presents significant challenges, with varying levels of success among different ventures.
  • Creating a sustainable AI or analytics product requires understanding client expectations and the market's acceptance of new technologies.
  • Indian clients may struggle with embracing analytics due to a lack of familiarity with its lifecycle and appropriate budget allocations.
  • Founders should anticipate educational gaps among clients and be prepared to guide them through the analytics process.
  • A clear vision and adaptability are crucial for navigating the evolving landscape of data science and analytics.

Takeaways drafted with AI from the article text.

Data science startup tips
Data science startup tips
In the last six years of our being, we have covered 70+ startups in analytics, AI, big data and machine learning space. While some have fared to stand up the competition to make it big, others are still finding a way. From having covered startups on various domains like healthcare, HR, retail, legal, drug development, amongst others and having interacted with the founders of these tech geniuses, we know one thing for sure— the path isn’t paved in gold and is full of challenges. Based on our past interactions with the startup world, we bring 5 key takeaways / tips for the budding data science/ analytics startup founders who are keen on venturing into the tech space of AI and analytics.

1. Cracking the code of right AI/ analytics product and making it sustainable

Building the right product is the key. To have a successful run in an area as new and evolving as analytics, it is important to have a clear anticipation on the acceptance and success of the product in the given demographic. “From a client perspective, India is relatively new in embracing analytics. Most clients are used to IT budgets and spends and view analytics under the same lenses, which is not fair and also sets up for unrealistic expectations. Clients in India are also not very well versed with the analytics life cycle and hence have to go through a learning cycle of their own”, shares Ashish Rishi, Head Analytics at Lymbyc. It is therefore important that to remain relevant in AI and analytics market, the budding founders have to work hard constantly, keep on motivating and develop better products than those already available in the market. “Lethargy can be lethal in this constantly upgrading AI industry”, says Akash Shastri, CEO and Founder, Brainasoft. There is no shortcut to identifying areas like learning curve of the product, identifying if it is worth any investment, understanding the complexity of the product etc. As Adarsh Natarajan of AIndra System says “in a nascent field like AI, working without help of prior literature is quite hard and can be challenging to deal with at times, but it must be overcome”. It is important to continually test your audience assumptions and function accordingly.

2. With new big data and analytics platforms evolving every second, stand out of noise

“We face a lot of the common challenges faced by startups, however the biggest problem in the big data space is standing out in a sea of noise. It’s an exciting time for the industry as big data and data analytics in general as new platforms and products are emerging everyday”, says Kunal Agarwal of Unravel Data. De Silva of Botworks also believes that there is a lot of noise in the market today about AI, chatbots and digital marketing with many companies using AI as a marketing slogan without having the expertise to implement it. It is therefore important to look beyond the obvious and bring real time experience in AI and ML. “It is sometimes hard for folks to imagine all the possibilities beyond what they see for instance, in the demo, or the marketing literature, notes Rishi, but budding founders should continually strive to make bigger and better products and bring delightful experiences to the customers. Being a startup in the tech space can be similar to being on a rollercoaster ride packed with adrenaline, fear and happiness! It is one of the most fast-paced ecosystems with new ideas and projects conceptualising every day by both larger companies and startups alike. In this stiff competition, it is important to invest on unique ideas and stand out of the crowd.

3. Getting the right investor, who understands the evolving AI and tech market

After having built the right product, the next obvious phase is scaling it up—hence comes the need to find investors. Though many of the startups that we have interacted with are bootstrapped, there are many more that are funded. Many new startup founders assume that money wouldn’t be an issue after raising their first round, but there have been instances like Karyaah, an analytics startup that shut down due to lack of funds. It is therefore important that every rupee should be spent wisely. This is where the need to woo investors and venture capitalist comes into picture. Every pitch can lead to a better funding prospect, and it is crucial to have clear vision on your venture to get funding—what does your startup do, plans on using funds etc. However, while evaluating a proposed investor do not solely depends on the capital that you need to boost your business, but other parameters like alignment of core values with the prospective investors, their past record, freedom of making decisions etc. matter a lot. Our past interactions suggest that most of the startup teams use their funding amount for team building and product development purposes.

4. Investing on the right analytics team

Your team can build or break the product. As we covered in our earlier article, how lack of right talent can be deterrent to scaling up of an AI startup, it is therefore important to invest on the right team. “Getting really good talent on board is hard and expensive to say the least, which is why we invest in our employees and try and give them no reason to leave”, says Abhimanyu Godra of Bottr. Harpreet Singh of iNICU believes that the reason for the success of their startup was knowing the recipe of creating right product, hiring an appropriate team and bringing in the right mentor. While India has a lot of mathematical, statistical and econometric talent, there is legitimate crunch in the number of people who are trained and qualified to work in the highly technical AI spheres. There is a lack of talent in areas like AI and deep learning, and the founders may have to be prepped up for dealing to find just the right talent for their team. “We still struggle to find lot of good people who knows about these technologies and who are aware of what other cutting edge algorithms and research is taking place”, says Ankit Narayan Singh of Parelleldots. “Call us crazy, but we believe for any two people to connect and build something together — be it a company, a social movement, or even a revolution — it always requires them to connect on something deeper and more lasting than “skills”. Their values and their culture must fit, says Rishabh Kaul, Co-founder, Belong.

5. Having right data is the key

To run effective models in the AI and analytics space, on which most of the products are based, it is important to not just have lot of data but a relevant set of it. “A lot of AI algorithms use a large amount of data and we are trying to collect and label as many data points as we can because most of the algorithms use a lot of data inputs” says Ankit Narayan Singh of Parelleldots. Anil Goel, CTO of Oyo Rooms echoes a similar voice saying that in today’s data-driven economy, the real differentiator for startups and enterprises is the data they generate, and more importantly, the end value derived from that data. With large amounts of data being created, AI is expected to be multi functional across multiple sectors, and one of the biggest challenges of being a startup in the AI space, is access to data. Shashank Bijapur, co-founder, SoptDraft says “It is much easier for larger enterprises to gain access to data than it is for startups”. There may also be an issue with data in open-source not matching the data required for running algorithms. Being a startup in the AI space, Gaurav Tripathi  of Innoplexus shares that getting the right data, nature of data, crossing the belief barrier and using the right tools can be some of the major challenges that they face. “Data in life sciences is deep, dense and diverse where conventional NLP has always failed to deliver, so we needed to develop new methods. It is also challenging to make clients believe that the results generated by machines can be better at times than those generated by humans.” Investing in right kind of data is therefore necessary.

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Corrections: AIM corrects errors of fact and notes the change on the story. Spotted one? Tell the editors. Read our editorial standards.

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