TL;DR - Precision measures how much of your predictions are actually correct. Recall measures how much of all the correct ones you have correctly predicted. High Precision and Recall is obviously great, but you seldom can have both. The optimal balance between the two is purely based on the situation.
Based on a small experiment where we cold emailed 100's of technical (e.g. CTO) and non-technical executives (e.g. CFO) in our target market, we saw much higher email Open Rate and Response Rate from the non-technical executives.
There are tasks or items that seem to be really important due to it's seemingly large impact if not addressed (immediately). But in reality, can be can be easily addressed later with little to no consequences. The only real way to identify them is to learn from direct and indirect experience from other startups.
TL;DR There are 3 kinds of advice that can be dangerous and needs extra care when considering. Logical advice, indirect advice and direct advice. What makes them so potentially dangerous is that they are easily disguised as great advice when in fact they are not.
TL;DL - To be able to charge for POCs in markets that typically do not pay, structure your POCs so that the customer gets value even things don't work out. And don't call it a POC.
Data driven decision making. Everyone talks about it, knows its critical and says they're doing it. But when push comes to shove, and decisions need to be made, as many would have experienced, it's far from straight forward.
Digital Transformation!! AI ML! 5G! IOT!! Intelligent Edge! Insert buzzwords.
As we enter lockdown again across the country, it left me thinking of all the uncertainty that business owners are dealing with in attempting to plan and forecast their business into the future.
In our previous post, we covered survivorship bias, a tendency to focus on things that have survived and overlook those that didn’t. It misleads data scientists and distorts their reasoning. Far from being an isolated problem, however, survivorship bias is part of a broader selection bias. The latter encompasses various instances when data sets are not representative of the population intended to be analyzed.