Data Redundancy occurs when the same data is stored more than once. This is problematic because redundant data must be updated in multiple places. If one location is overlooked, the data becomes inconsistent. Redundant data also requires additional space to store it raising overall storage costs. File systems were notorious for having redundant data like names. One file may store a first name as William and a second file may store it redundantly as Bill or Will. The data has become inconsistent. Ideally each piece of data should be stored once and only once.

Data redundancy can create significant problems. For example, an e-commerce site may store customer address information in the customer table, the order table, and the shipping table. If the address is updated in one place but not the others, the records become inconsistent.

The consequences of inconsistent redundant data can be severe. For instance, financial reports may be inaccurate if revenue figures are stored redundantly across systems and get out of sync. Important business decisions are then made on incorrect data.

Data normalization techniques are crucial for minimizing redundancy. By storing data like customer addresses in a single table and using identifier keys to link it to other tables, redundancy is avoided. This maintains consistency and integrity across the entire database.

Best practices for preventing data redundancy include establishing database rules to ensure addresses are only stored once, using unique customer IDs instead of names/addresses, and instituting data governance checks through validation rules. Following guidelines like these is key to maintaining quality data.