A startup journey is no less than a roller coaster ride, replete with ups and downs and unexpected turns.
Getting data science and analytics right can smoothen the ride. Most startup founders know that data science can enable significant business wins. Yet, over-dosed by information, not too many know how to kick off the function within their organization.
Here’s our take on common mistakes that startups end up making, while chasing the data science dream:
Too often, startups get lost in the scramble of real-time analytics and end up chasing tactical, moving targets.
Or, they end up focusing on vanity metrics instead of the larger business outcomes they set out to achieve. Without focused pursuit of data science outcomes, they run the risk of rewriting history as new data emerges.
Early-stage startups are constantly making trade-offs — visibility vs cost, revenue acceleration vs customer retention, scale vs specialisation. If you focus on long-term, you won’t see the tidal wave until it’s right in front of you. If you get too caught up in the short-term, you’ll create your own tidal wave.
Balance is key. Your metrics may be down one day or month. Short-term fluctuations are normal. Making snap judgements isn’t going help. Take an informed decision basis your long-term goal.
Shiny tools are launched every other day. Currently, over 7000 tools compete for attention. But changing over to the newest shiny tool isn’t going to guarantee your strategy a high success rate.
Instead, the simplest of tools, when applied effectively, can deliver outcomes you expect from your Data Science function.
Use tech that works for you, not vice versa. When evaluating tools, remember the ‘Occam’s razor’ principle : “The simplest solution is usually the one that requires making the least assumptions”
Want to know how to avoid these mistakes? Get in touch with us at email@example.com.