Detecting Data Integrity Issues

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Find out why recognizing data integrity issues early in the acquisition process of an M&A transaction can save time and money.

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.

READ MORE: Top 5 Tips When Converting Business Associate Data In Oil & Gas Transactions

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:

  • Report requests take a long time to turn around — The report is likely being manually manipulated compared to a canned report out of the enterprise resource planning (“ERP”) system, which runs at the click of a button.
  • Row count variances — For lease and well master reports, the record counts shouldn’t change unless something is expiring or plugged and abandoned (“P&A-ed”). Look for additional non-operated wells, missing producing wells, or held by production (“HBP”) leases.
  • Duplicate rows — Look for the same row showing up twice in a report. Examples would be a well showing up twice with different operators or a lease with different counties.
  • Missing required/critical fields — Check for missing required/critical fields. For wells, check for field API numbers, well status, and operator. For leases, check for agreement numbers, lessor, lessee, status, and important dates.

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.

READ MORE: Why An Effective Master Data Strategy Is Key To Digital Transformation In Oil & Gas

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