Data and documents are typically spread about in different parts of the organisation making effective archiving very difficult.

Information can be categorised as follows:

• Raw data. For example, this could be measured data from a laboratory, a market survey, operational notes or observations.
• Working data. This typically consumes the raw data and produces aggregated information such as analysis on the efficacy of a new drug or market quality.
• Published data. This might be a formal report for higher management or for submission to a regulatory authority.

The need for effective data organisation during the operational life of this information is really a records management issue. The problem is often that no single system is used (e.g., raw data is likely to be held in files on a departmental project folder, aggregated data might be held in a database while documents might be managed in a EDRMS system) and its production and consumption will be performed by different people in different parts of the organisation.

Depending on future usage scenarios, all of this information (and the relationships between them) might need to be maintained, leading to implications for successful archiving. Trying to build a coherent information store at end-of-life archiving is much harder than doing it during production.