Data Accuracy: Valuable raw data generated by a particular group within an organization may need to be validated before being transformed into normalized or consistent content.
Data Interpretation: Information derived by one group may need to be mapped to a standard context in order to be meaningful to someone else in the organization.
Data Relevancy: The quality and value of knowledge depend on relevance. Knowledge that lacks relevance simply adds complexity, cost, and risk to an organization without any compensating benefits. If the data does not support or truly answer the question being asked by the user, it requires the appropriate meta-data (data about data) to be held in the knowledge management solution.
Ability of the data to support/deny hypotheses: Does the information truly support decision-making? Does the knowledge management solution include a statistical or rule-based model for the workflow within which the question is being asked?
Adoption of knowledge management solutions: Do organizational cultures foster and support voluntary usage of knowledge management solutions?
Knowledge bases tend to be very complex and large: When knowledge databases become very large and complex, it puts the organization in a fix. The organization could cleanse the system of very old files, thus diluting its own knowledge management initiative. Alternatively, it could set up another team to cleanse the database of redundant files, thus increasing its costs substantially. Apart from these, the real challenge for an organization could be to monitor various departments and ensure that they take responsibility for keeping their repositories clean of redundant files.