Enterprise data modeling has recently been in the crossfire of attacks asserting that it is completely useless. Many of the arguments can be attributed to improper use of data modeling equipment. Many financial institutions and other organizations are reconsidering their data modeling practices in light of the bad press it has been receiving. A formal definition of goals and objectives for data modeling may seem bureaucratic. A Data administration group can be of enormous use to project teams, or it can be an enormous source of frustration. The quality of the contents of a data model is often intangible. Typical errors can be seen in existing data models — for example, an account number is the number of an account. Physical data integration across several applications is an indicator for good data modeling practice. A one-to-one implementation of a logical data model can be successful, but in many cases it leads to slow implementations.