It has always been a dream of mine to create a successful startup, especially one that improves people’s lives by leveraging on Data Science and Analytics technologies. At this point, you may be wondering why should you be listening to my advice when I do not have any experience (yet) on building a successful Data startup? Well, the following tips are points I found really useful discussed in this podcast and I have summarised them so that you don’t have to spend 1 hour++ listening to it all over again, you’re welcome ;)!
Before I begin, let’s look into the profile of the speakers in this podcast series. Each of them has taken the path less travelled of leaving their comfortable 9 – 5 job and chose to create their own successful Data Science startups:
- Ines Montani, Explosion AI
- Matthew Rocklin, Coiled
- Jonathon Morgan, Yonder AI
- William Stein, Cocalc
”We should not have started as VC funded, we would not have been nearly as successful.William SteinCocalc
The experienced startup founders have brought up so many points during the discussion and it seemed impractical to write about every one of them on this article. Hence, I have summarised 6 points that truly resonate with me and I would love to share it with all of you.
I would imagine that not everyone is lucky enough to be able to come up with a fantastic startup idea right after graduation and I believe that many of us have our current 9 – 5 job in the day. Likewise, all of the founders had a full-time job before they decided to quit and focus on their startup. I have learnt that all of their ideas started off as a side project initially and their decision on quitting comes at a time when their startup has gained enough traction and requires their full attention. In my opinion, this is a wise strategy as it minimizes the risk of your venture failing which would make you jobless.
It’s great that your side project has been gaining traction and more and more users are signing up for your service/product. But the million dollar question – are they paying? Your idea may not need to be profitable, but to ensure that you are providing great value to your customers, they have to be paying you for your services. Never be afraid to charge your customers! Want to offer your service for free before letting your customer decide whether they are willing to pay? Make them put up their credit card details and provide them an X month free trial where they can unsubscribe anytime.
This is something the founders cannot emphasise enough, it is important to know the right connection and network. With the right connection many things can go in your favour; securing a VC funding, finding your co-founder, etc. There is a plethora of avenues for you to meet new people, such as local meetups, clubhouse, Facebook interest groups. Stop finding excuses already, go out there and rub shoulders (virtually *ahem*😷 ).
There are 2 school of thoughts on Open Source and I for one believe that Open sourcing your software could bring about more benefits than harm. Firstly, your software would be tested and bugs if any will get fixed by the developer community. Secondly, by gaining traction via community adoption would be equivalent to free advertisement for your company. Lastly, with huge adoption, you can offer chargeable premium services.
I would like to clarify that this point does not serve to discourage you from outsourcing your task. The two keywords are – “core” and “start”. At the start, you should attempt to bootstrap and complete the core tasks internally. This would ensure that you have a strong understanding of the core tasks being outsourced. Which would translate to better design specifications and requirements during the scoping phase of the tasks.
Don’t have a breakthrough idea for your Data startup? Fret not, there are multiple ways to get inspired so that you could come up with an idea. Consider taking up consulting gigs which will allow you to interact with multiple customers with different problems. Once you have identified a common problem faced by numerous customers and this problem has not been fixed by an existing solution, your startup could come up with a solution to fix it.
Many of the points they have brought up sounds very much like common sense, but more often than not we tend to overlook these seemingly trivial points. If you have 1 hour to spare I highly recommend you to listen to the podcast and I would love to hear your thoughts on the discussion. To create a Data startup effectively requires the founder to have at least a strong grasp on the concept of Data. This is where Big Data Theory comes into the picture, we offer courses that will be useful in the design, development and ideation phase of your startup. Head to our course catalogue to find out more! Who knows, your future co-founder might very well be one of your classmates in Big Data Theory.