The clause says data is rarely ready for training and that preparation can take a large share of the effort. It lists the activities: exploring the raw data to understand its shape and distribution; wrangling it into a structured table; cleaning duplicates, missing values, invalid entries and outliers; imputing missing values; normalising or scaling numeric features; composing a dataset from several sources; splitting it into training, validation and test sets that do not overlap; labelling samples; and de-identifying personal data. It also names data poisoning as a check, because contaminated training data can cause harmful behaviour, and it notes that incomplete or unrepresentative data can produce biased or discriminatory predictions.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.