I'm not arguing that this is in fact where the value of the data scientist's work lies. It is what makes a difference in the business and what you talk about when you want to convince your boss - or the boss of your boss - to invest in data science projects. But this is not where the actual battle is fought. In my experience, every project involves some form of data analysis hinges on good data pre-processing and feature generation. Whether you use complex neural networks or a simple linear regression, you'll find that irrelevant features or features based on incomplete or erroneous data will always lead to poor results. On the other hand, when your data is clean and reliable and you generate the right features, most of the available models will do a fairly good job. In other words, the process of preparing the data is not only the biggest factor influencing the project duration, but also a key factor in determining whether your project will be successful.