Data integrity is broken down into three attributes: accuracy, completeness, and consistency. Issues with any of these attributes reduce the overall confidence of reports run on this data. Recognizing data integrity issues early in the acquisition process gives you time to mitigate and budget for the clean-up effort. Here are some warning signs to watch out for before you get access to the full dataset.
The first hint you may have data integrity issues is if the asset has changed ownership multiple times over a short period (i.e., three times in five years through mergers or bankruptcies). If the assets have been acquired by different entities in a short period, each owner of that asset likely didn’t have the time to clean up the data. Companies often prioritize clean-up of core and high-priority assets and plan to do the other assets at a later phase. When you finally receive the data, you’ll likely get a mix of what the previous owners entered, as well as some of the seller’s assets mixed in, which can result in data consistency issues.
You can further flush out data integrity issues by requesting updated reports. Examples are well master or lease exhibit reports with additional columns. Specifically, pay attention to the following:
Finally, a review of the code tables and related record counts can give you an idea of what to expect when the deal closes. If the seller has a lot of dropdown values for status, users are more likely to choose the first one that’s close enough. If “Varies”, “Miscellaneous”, or “Other” have large associated record counts, you’ll likely need to review what each of those records should be grouped in later.
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