Many data professionals, particularly people focused on logical data modeling or meta-data management, will often adhere to the "one truth" philosophy. This philosophy says that it is desirable to have a single definition for a data element or business term, that there should be a common, shared definition for your master reference data and perhaps even your major business entities. The anti-pattern occurs when this philosophy is taken to the extreme and you seek to get to the one truth about all data entities and data elements within your environment. The challenge is that to get to the "one truth" about something, when it is even possible, often requires significant effort. When this effort goes beyond the point of diminishing returns and provides negative value to your organization you have a significant problem on your hands.

Taken in moderation attempting to seek the one truth can provide value, but when you take it to the extreme several negative side effects occur:
1. Your organization's competitive position can be eroded by enforcing a consistent viewpoint.
2. Development teams abandon the effort.
3. Consensus, not the actual truth, sets in.
4. The widens yet again.

"One truth" can be a nice vision to work toward in theory, but in practice you'll likely only be able to narrow it down to several reasonably similar truths. It may be important to recognize that there are several truths and to identify those truths, but trying to force a single consistent truth on all parties is futile at best. Don't let it prevent your team from delivering important business value in a timely manner. My advice is directly related to this: Take a practical approach and recognize that there is a diminishing rate of return when it comes to modeling, and that you can quickly reach the inflection point where further investment in data modeling reduces the overall value to your organization.

- Scott