India is one of the most dynamic hotbeds for startups in the world today. Over the past few years, India has been on the cusp of a digital revolution with data science startups on the rise. The two most crucial goals of data science startups are:
- To continually improve the products that are widely used by customers.
- To enhance the decision-making process of business and organizations.
A study report by Analytics India Magazine states that, at present, there are over 5,000 startups in India dedicated to helping clients across various sectors by leveraging the power of tools like data analytics and AI.
It further states that Indian companies hold a 7% share in the global analytics market. However impressive these numbers might seem, not all is smooth. As is true of every startup, data science startups too have their distinct pitfalls and challenges. And not all startups can make it through the end of the tunnel.
So, here’s a list of Do’s and Don’ts for data science startups!
Do come up with a groundbreaking idea.
Ideating and developing the right product that’ll help your customers is the catch. Since you are a new player in the analytics game, be patient enough to learn new ways to leverage the tools and data at your disposal to come up with innovative solutions.
Try to figure out your area of expertise and carefully analyze the market to identify the pain points of your potential consumers. When you tread within your field of knowledge, there are high chances that you’ll come up with pioneering solutions bearing high levels of accuracy. Besides, there are so many genuinely helpful data science certifications to help startup founders master the field.
Always strive to develop such products that bear clear-cut technical applications. Granted this is tough, and you have to spend a lot of resource and time on it, but in the end, it’ll be worth the effort.
Remember, you want to the solution to a problem that already exists because, in the future, we’ll be looking forward to industry-specific and use-case first data science startups. Divyesh Patel, the co-Founder of Turing Analytics, says:
“They [AI startup founders] must find a problem where the market demands a solution and then apply data science to try to solve it.”
Don’t get stuck in the “cold start” circle.
Being a newbie in an ever-changing ecosystem can be challenging. Although it is true that AI infrastructure will largely continue to be dominated by giants such as Google and Microsoft, it should not mean that you have to give up on finding better solutions.
When you build the first prototype of your product, it must address the “cold start” problem, that is, it should be good enough to break the ice and commence the process of data accumulation and data-driven improvement.
However, don’t get stuck in this circle. Try to find such AI applications that can help combat specific problems ten times better than the existing solutions. For instance, customer service in banks can be improved significantly by incorporating bots within the system that’ll plug into the personal banking data of clients and help create additional value by keeping track of their saving and spending habits.
Do strive to be ‘Pitch Perfect.’
Funding is the lifeline of every startup, and to receive a steady funding backup you need to be able to bring reliable and compelling pitches to the table. This will help you convince the angel investors and venture capitalists (VCs) to see that your product can bring value to organizations. And this cannot be mere theory.
You must use sample data to prove your case with the prototypes you’ve designed. The better you can pitch your ideas to potential investors, higher are the chances of you receiving funding for your startup. However, before choosing to tie up with angel communities or VCs, do a little digging first:
- Do the core values of your organization align with that of the investors’?
- Will the tie-up affect your freedom of making decisions?
- What are the records of your potential investors?
Don’t limit yourself to small markets or tread in areas already occupied by the AI magnates.
Thanks to massive tech organizations, today, the Ai infrastructure is rapidly being commoditized. So, you need to retrace your steps and dedicate your resources to hunting for ‘venture scale’ opportunities that’ll allow you to grow.
For instance, education and healthcare are the two sectors that hold a much broader analytics landscape and stand out in the ocean of noise of tools such as Machine Learning as a Service (MLaaS).
Also, try to shift your focus to B2B applications as the sheer amount of data available makes it a melting pot of opportunities. Always be on the lookout for industries that are early adopters and figure out how your product can create a revolution in those industries.
Do invest your time and resources in building a great analytics team.
Your team is what represents your organization and strengthens it. They are the brains behind the product. So, you need a strong and well-coordinated squad, one that fully understands the implications of AI and data analytics.
Your team should be a pool of talent with data scientists, data analysts, and data engineers working in close cohesion with one another. Stressing the importance of a tightly knit team, Rishabh Kaul, co-Founder of Belong says:
“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.’”
India has no dearth of talented and well-learned individuals, you only have to know where to look!
In the startup game, it is utterly essential that you always keep your approach fresh and original. As a startup, you just cannot expect that people will readily open up to your ideas, but you need to be persistent in selling what you build.
Keep these few tips in mind and believe in what you do!
Nisha Pandey
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Your work is very good and I appreciate you and hopping for some more informative posts.