In each model development, we will need to obtain enough data to build the model. It is very common to read the expression garbage in, garbage out, which relates to the fact that if you develop a model with bad data, the resulting model will be also bad.
Especially in machine learning applications, what we expect is to have a huge amount of data, although in many cases that's not the case. Regardless of the amount of information available, the quality of this data is the most important issue.
Moreover, as a developer, it is important to have structured data, because it can be immediately manipulated. However, data is commonly found in an unstructured form, meaning that it takes a lot of time to process and prepare for development. Many people consider machine learning applications to only be based on the use of new algorithms...
