Not all data cleanup can occur prior to migration. If possible, it is ideal as it helps to ensure data moves accurately from the current museum Collections Management System (CMS) to the new. 

However, due to technical restrictions in the current CMS or time, cost, capacity and other resource restrictions, any data cleanup and refinement may have to wait until the data has already migrated into the new CMS. If that is the case with your museum data, it’s OK—you’re not alone. This post will outline how to prioritize the work ahead.

Address Gaps in the Museum Data

First, let’s focus on the museum data that’s important and should be prioritized first. Important data is data that is required to meet best practice and/or required to be in the CMS in order for the CMS to work effectively with the data. It can also be data that helps you and your team to do your jobs better. Essentially:

1. Data required to meet best practice and/or required for the new CMS to work effectively.
2. Data that helps museum staff do their jobs effectively.

Whether the data is missing, inconsistent, or in an unusable format, these high priority cleanup items encompass any data that helps the CMS or you do your job better. When these high priority areas are addressed, they help to serve the best interests of the collection.

For Example: The museum object descriptions are rich, but there are few to no keywords (or subjects) identified to aid in discovery. The lack of keywords isn’t necessarily a blocker for data migration, but it is an excellent time to identify where gaps in the data can be addressed in order to enhance the management and use of the collection.

Refine Areas in the Data to Increase Discoverability and Use

Once the high priority data areas are addressed, we can now turn our attention to the next most important reason to further refine our data: collection discoverability and use. Refinement areas are typically areas that work as-is, but could be more fully fleshed out or entered into the CMS in a more effective way. This includes areas in our data where editing, reformatting, or expanding can improve findability for staff and discoverability for external users. Refinement activities can take place as time and priorities allow. 

For Example: The museum object data contains a “circa 1970s” date style in a free text field without an accompanying Date Range field to indicate this item can fall between 1970-1979. In the new system this date is of some help, but doesn’t support searching within a stipulated timeframe. If someone was searching for an object created between 1970-1979 the search results would not know to include the “circa 1970s” record. This impacts discoverability for both staff and external users.

How to Fit it In

So, how do we fit in data cleanup and refinement? For many museums, once migration occurs the focus and capacity dedicated to the project drops off. It’s understandable as the museum staff have many responsibilities. With this in mind, the most effective and sustainable approach I have seen is to undertake the refinement in small and focused chunks. Then, there are two approaches that can help optimize your work:

1. As part of a larger project, data refinement can occur while you’re already in and “touching” the record.
2. A dedicated time period and a dedicated person to focus on and execute the refinement of a very discrete set of data.

Approach #1: The museum receives a grant to support research and fuller description creation for a set of 100 items. As the research and description work takes place, this would be an opportune time to also improve the keywords in the records.

Approach #2: The museum receives a graduate student as an intern for the summer. The student is familiar with museum registrar best practices and has a little exposure to cataloging. Employ the intern to focus on a carve-out of the collection (say 100 items) and one or two fields for cleanup. For example: date format and keyword corrections. 

Conclusion

The good news is, if you’re reading this post, you’re already ahead. The first step is to be aware of data cleanup and refinement opportunities through the review of your data and capturing an outline of actions needed. By documenting this information while all of the data review and migration fresh in your mind, you’ll be in a better position to leverage this work in future projects.