Stanford Digital Repository
Automated File Assessment: Outcome
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•Improvement over comparable manual process
•Requires administrative system to record, manage results (administrative metadata)
•Institutional commitment
•Technology watch
•Policy maintenance
•Opportunity for federated approach?
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the comparison of the numbers suggests that the automated workflow in the Empirical Walker results in numbers quite close to that of the manual process, an indication that the Empirical Walker is a successful execution of the assessment methodology specification.

Can an automated assessment methodology, prototyped in Stanford’s Empirical Walker, help to maintain control over workflow and extend to the development of services beyond bit-preservation in the long-term? While the AIHT project provided Stanford with a only small taste of ingesting and handling “off-the-street” data in a preservation repository environment, we believe that current indications are net positive.

The preservation assessment process creates new, additional metadata to manage. The value of that metadata depends on whether the efficiency it brings to the preservation process outweighs the cost of creating and managing it over time. The costs of managing the assessment metadata we generated are currently unknown. The quantity of assessment metadata is relatively small, so the cost of their physical storage is not an immediate concern. However, maintaining the infrastructure to support the methodology is more daunting; it requires a complex layer of management that includes ongoing technology watch, format research, policy maintenance and possibly deposit agreement re-negotiations. Perhaps a federated approach to some of this activity, as a service to a community of repositories and their users, would be most economical. In any case, the costs to be borne are not inconsequential. And yet there are a number of ways in which the assessment data can inform decisions to be made at different points in the preservation cycle, and it is conceivable that, if used effectively, the data’s value in the decision-making process offsets or exceeds the cost to create and maintain it.