Poor data quality: Irrespective of being aware of the data defects, new deficiencies are often uncovered after extraction.
Missing data: Mandatory fields in source systems turning out to be blank or null.
Mismatched data: Field overuse is a classic problem of incorrect data. Sometimes, two or more different domains of data can be found in one field that is repurposed after its original use becomes obsolete.
Missing data requirements: Data fields or values are not captured properly. Business and data transformation rules are not sufficiently researched or documented to the breadth or depth necessary for consolidating multiple systems into one target.