Create a Plan for Museum Data Cleanup

Rachael Cristine Woody

Rachael Cristine Woody

April 26, 2023

This month we’ve reviewed the most common museum data messes and the strategies most used for data remediation. Today’s post will conclude our data cleanup series with guidance on how to create a plan for your museum data cleanup project.

What are Your Resources?

Before embarking on a data cleanup project, it’s important to consider the resources available in order to help scope the cleanup work appropriately. A data cleanup project too large to undertake can easily burn out staff and volunteers, and can also lead to even more messy data. Review how many people are available for the data cleanup project, how much of their time will be allocated on average per week, and for how many weeks. 

Support Cleanup Efficacy with Bite-Sized Work Plans

Data cleanup is the most effective when it’s performed with laser focus. For example, focusing on a subset of the collections, and even just one field commonly used in every record will help to scope the cleanup work into easily completable sections. This approach helps to keep the data cleanup team focused and allows for the inevitable breaks in cleanup to perform other activities.

Select the Standard to Follow

If the museum hasn’t already identified the data schema and data content standards it intends to follow, or that are available in the CMS, then it’s time to select the standards that make the most sense for the museum. Please see Common Museum Data Messes to Look For published on Lucidea’s Think Clearly Blog for more discussion on data schema and content standards.

What is the Knowledge and Technical Skill Level?

Next, there needs to be an evaluation of the people tasked to work on data cleanup. Are they knowledgeable in data content standards? Do they know how to use the CMS? Consider the knowledge and skills needed in order to accurately perform data cleanup. What are reasonable expectations? What documentation or instructions are needed? Or, does expertise need to be brough in from an external source?

Strategies for Data Cleanup

Finally, determine which of the strategies listed below (and covered by Lucidea’s Think Clearly Blog) are to be used, and craft your instructions accordingly: 

How to Create a Set of Easy-to-Follow Data Cleanup Instructions

The following steps should be taken to develop a sustainable set of easy-to-follow instructions:

  1. Select the content standard(s) that make the most sense for your collection type and capture in the instructions which catalog standards are to be followed.
  2. From the standard(s) you’ve selected, identify the fields with indications of “required” and “recommended” and capture these fields for your instructions.
  3. Include the data standards for how data should be entered into each field, including standardized language, avoidance of abbreviations and acronyms, and appropriate writing style.
  4. Using the content standard guidelines, create specific examples of data from your collection.
  5. With the data cleanup strategies you’ve selected, identify which steps your team should take for their data cleanup process.


With a game plan in place, you and your team can now confidently embark on your data remediation journey. By following standards, leveraging CMS tools, and creating strong processes you can help ensure future data cleanup is unnecessary.

Additional Reading

Establishing Museum CMS Best Practices

Evaluating the Format of Museum Data

Evaluating the Quality of Museum Data

How to Prepare for a Museum Collections Management System Migration

The Importance of Sustainable Museum Cataloging & How to Achieve It

Museum CMS 101: Workflow and Record Construction

Rachael Cristine Woody

Rachael Cristine Woody

If you’d like to learn more, please join us for “Strategies for Museum Data Remediation”, presented by Rachael Woody May 3, 2023 at 11 a.m. Pacific, 2 p.m. Eastern. (Can’t make it? Register anyway and we will send you a link to the recording and slides afterwards). Register now or call 604-278-6717.

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