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.
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.
Customers often ask us how much data they need to run successful artificial intelligence (AI) and machine learning (ML) projects. This question is hard to answer in simple terms. A functioning ML model requires clean and large data sets, but their optimal size is affected by a range of factors including the complexity of the model, training method, and tolerance for errors. Fortunately, there are several ways of calculating your data needs and overcoming the lack of data.
Across industries, businesses of all sizes are embracing digital transformation (DX or DT). Using advanced technologies to improve operations and delight customers has become a source of competitive advantage. And companies spare no expense in these efforts. Worldwide spending on DX is forecasted to reach $2.3 trillion in 2023, according to the International Data Corporation (IDC), a global market intelligence provider. The COVID-19 pandemic is set to only further accelerate this trend.
The logical error of only considering the information that is seen.
Kurvv’s AutoForecast product provides customers with a variety of quantitative sales forecasting methods, so they can simply connect their data source and receive a customized, accurate forecast in seconds. Once data is uploaded, AutoForecast tests out several different time-series forecasting methods including decomposition, exponential smoothing, ARIMA and regression (see below for more details about each). Finally, an average of all forecasts is computed and output with the final results. Customers can select the forecast output(s) they feel most comfortable with and customize forecast time horizon and validation time windows.