Strategies for Effective Museum Data Cleanup – Part 1

Data remediation is a critical aspect of our work. Incorrect data affects our ability to take care of and manage the collection, and it impacts users who engage with our collections. 

Fortunately, no matter what type of Collections Management System (CMS) you have, there are strategies you can use to help your data cleanup efforts. This week we’ll cover the Record-by-Record Strategy and the Spreadsheet Strategy. For each strategy we’ll review required elements, offer directions, and note recommendations and pitfalls.

The Record-by-Record Strategy

The record-by-record strategy is a strategy to use if your CMS doesn’t support data export into a spreadsheet, and it doesn’t support data cleanup tools. In order for this work to be sustainable, a doable pace will need to be established. 

Notes about this strategy:

• Use when there is no tool or vendor support available.
• It’s labor intensive.
• Requires a plan for how to evaluate the data.

Directions: Identify required fields that appear to have data missing. Next, identify fields that appear to have incorrect data, such as data in the wrong field or entered in incorrectly. Because this work has to happen record-by-record, you can make these corrections now. If a record requires more research, mark the record and the data needed so that you can come back to it later.

Recommendation: Identify small and achievable cleanup areas and perform a review of the work before moving to the next item.

Possible Pitfalls: The pitfalls for this record cleanup stem from the labor-intensive act of reviewing and performing cleanup on a record-by-record basis. The monotony paired with reviewing an extensive amount of information can be mentally exhausting and should only be performed in short time sprints for the best results.

The Spreadsheet Strategy

The spreadsheet strategy can provide a comprehensive view of all of your data. It makes it easy to spot records missing data in required fields and data that was entered incorrectly. In addition to reviewing data, the spreadsheet can also facilitate any data edits needed. The majority of CMS platforms allow users to export their data into an editable format, typically .CSV. Check your CMS documentation to see if data export is a supported function and review the steps you need to take for that particular system.

Notes about this strategy:

• Can be used for both data evaluation and data cleanup.
• Requires the ability to export and import (typically via .CSV).

Directions: Export data from your CMS and evaluate. Make a game plan for how to tackle the identified data imperfections. The most effective way is typically focusing on one field across all records, or a subsection of records. Fill in data that is missing but fairly easy to create. Correct the data that was identified as incorrect. Make a note of any records that will require future research. These records can be worked on further at a later stage in cleanup.

Recommendation: Evaluate the data in the aggregate and work field by field (columns) for consistent and easy data cleanup. Review all work before importing so no new mistakes are introduced.

Possible Pitfalls: This strategy isn’t without its risk. When editing a spreadsheet there are no controls that would prevent further mistakes being made, such as misspellings, data in the wrong field, deletion of data accidentally, etc. This strategy relies on a strong review of the data in the spreadsheet BEFORE ingesting the data back into the system. 

Conclusion

Data remediation can be an overwhelming experience, but when approached in bite-sized chunks we can perform the cleanup in a more accurate and sustainable way. With your knowledge, the tools you have access to, and a data cleanup plan, you make measurable progress and a real difference in your museum data.

Additional Reading

Evaluating the Format of Museum Data

Evaluating the Quality of Museum Data

How to Chart a Course Toward a Better Museum CMS 

How to Prepare for a Museum Collections Management System Migration