It is almost certain that any data encountered in the real world has data quality issues. In simple terms, this means that values are invalid or very different from other values. Of course, it can get more complex than this when it is not at all obvious that a particular value is anomalous. For example, the heights of people could be recorded and the range could be between 1 and 2 meters. If there is data for young children in the sample, lower heights are expected, but isn't a 2-meter five-year-old child an anomaly? It probably is, but anomalies such as these usually occur.

As with missing data, a systematic and automatic approach is required to identify it and deal with it and Chapter 5, Outliers, gives some details.