We often make our own dichotomous data from outcomes that are not truly dichotomous, so that they are easier to manage and understand. For example, converting blood cholesterol (measured on a continuous scale) to 'high cholesterol' or 'not high cholesterol' dichotomised around a clinical threshold above which you would consider the cholesterol to be high; or converting pain measured on a short ordinal scale to 'absent or mild' or 'not absent or mild' (by which we mean moderate or severe). Generally long ordinal scales, or scales with a large number of discrete categories, are treated as continuous data for the purpose of analysis.
Sometimes, censored data are converted into dichotomous data by counting the number of people who have had the event by a particular time (such as the number of people who have a recurrence of cancer within 5 years of an operation). This should only be done when all participants have been followed up to the particular time point.
The benefits of converting non-dichotomous data into dichotomous data relate to ease of analysis and interpretation. Of these, the more important is ease of interpretation. Dichotomous outcomes may be easier for decision makers to understand and make judgements about.
The down side of converting other forms of data to a dichotomous form is that information about the size of the effect may be lost. For example a participant's blood pressure may have lowered when measured on a continuous scale (mmHg), but if it has not lowered below the cut point they will still be in the 'high blood pressure group' and you will not see this improvement. In addition the process of dichotomising continuous data requires the setting of an appropriate clinical point about which to 'split' the data, and this may not be easy to determine.