How do I start a data science business?

 there are plenty of opportunities to start a business in the field of data science. Here is the ‘10 step approach’ of building your own business in this field:

  1. Plan in advance – Plan ahead of time. Before you take the plunge, ensure that you have good knowledge of data science technologies and their business applications. Work experience of at least a couple of years would be an advantage. You should also do the appropriate financial planning to ensure that you can devote full time to the business before it starts generating revenue. Stay informed of the latest happenings in the field of data science. Subscribe to blogs, meetups etc while you are in the job or in college.
  2. Build the Idea – Prefer products over services – There are many big players in the market who claim to provide services in the field of data science, with strong sales muscles. It is easier to get early customers and investors if you have a product. The idea should solve an existing problem, or open up new opportunities that never existed before. Watch out for disruptions in the market, and build data products that will facilitate or accelerate those disruptions. Typical disruptions are eCommerce, large scale urbanisation problems, internet of things etc. Note: Never build the product idea in isolation. Meet a lot of people who can validate the idea, agree on the problem statement, and potentially express a desire to pay for a product that solves the problem.
  3. Design the Platform – While you are working on your idea, simultaneously start building the key blocks of your data processing platform that will enable you to quickly build multiple prototypes and showcase them to early customers. The platform should enable you to easily acquire some data, store it, process it and display some insights on a website. Go with open source products to keep the costs low, and finalize the platform architecture and preferred technology stack for data acquisition, data storage, data integration, data exploration and data reporting. Explore hardware devices that can either help to generate data or take some action based on data.
  4. Build a Prototype – Once an idea is finalised, build the prototype for a small section of the population.. maybe a school, a class, a small city, a department, a store etc. The main purpose of the prototype is to communicate the idea effectively, that is it! Define your target customers. As obvious as it may sound, ensure that you are providing them something that is not already available to them at the same price point.
  5. Validate the Idea – Reach out to your target users / customers, and pick those who can help you with your journey. Typically choose local users / customers who are easy to approach. Talk to them to see if the idea is generating interest. Send emails, get ready to do some cold calling, some spamming, some stocking… Use referrals and friends to whatever extent you can. Note: For someone who is technically inclined, reaching out to random people can be hard, and you may want to get in touch with a co-founder who is really good at this.
  6. Engage with people – Understand the difference between users of your product, and your customers. Approach both of them differently. Apart from reaching out to potential users / customers, additional networking is necessary at this stage. Find an early stage accelerator program who can help you with the validation process and provide you the network, mentorship, feedback etc. Engage with people of your interest via meetups, conferences, social network etc. Partner with other startups to synergize and expand.
  7. Experiment with Ideas – If you find it difficult to generate interest of customers, it could be a sign that you may have to scrap the idea or radically change it. Be prepared to scrap a couple of initial prototypes before you see some interest getting generated on your idea. Leverage interns to work on building new prototypes or modifying existing prototypes.
  8. Build the MVP (minimum viable product) – If the idea generates interest from potential customers, approach angel investors to fund the MVP (minimum viable product). Show them the traction you have generated, list of potential customers, their feedback, their interest to buy the product etc. Keep the potential early customers engaged through the process of building the MVP. Make them your early investors, if possible.
  9. Scale your product – Now you are making some money, but nowhere close to the investments that were put in. This is where you will need external help to scale your product. Connect with the right accelerators to get in touch with marketing vendors. If your product is working well for a small city, expand it to other cities. If its working for a small school, expand it for other schools. Start thinking on managing your technical infrastructure to handle higher loads. Use AWS infrastructure to ensure optimum flexibility.
  10. Build a company – Building a company is different from building a product. Make sure you have the board of advisors, insurances, registrations, separate banking accounts, legal teams, tax teams, technology vendors etc. Build strong online presence to expand globally. Continue getting more customers, more investors and expand your offerings. Start thinking of new product ideas that will compliment your existing products. Repeat the above steps for your new product ideas. Ensure that you have a strong and consistent platform for all your products, which will make onboarding and managing of new products easy.

The process building an MVP (steps 1 to 8) is time consuming, and will require your savings or some kind of funding to sustain through a period of at-least 2 years. Many initial activities can be done while you are in job or in college. You have to however take a plunge full time at some point in time.

Reference Link: https://www.quora.com/How-do-I-start-a-data-science-business

May 17, 2019

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