About the Author:  Efrain Rios is the founder of Fortress Oil & Gas, LLC, a full-stream asset integrity solutions provider for the oil & gas industry.  For the last 20 years he has specialized in risk management, regulatory compliance, operational efficiency improvement and cost reduction strategies for mechanical integrity programs.  This includes the direct management and accountability of the asset integrity technology systems and governing processes for upstream operations spread over 12 countries.  Efrain is a member of NACE International and the API Sub-committee on Inspection and Mechanical Integrity (SCIMI).  He is also the leading expert on software solutions utilizing asset integrity management (AIM) systems and mobile data collection technology.


The last part of this series, “Filling Functionality Gaps Through Development” listed companies’ options when they need certain features to support their business processes, but those features are missing from their IDMS.  Also, the pros and cons of each option were provided to help readers understand the differences between options. In this fourth part of the series, we will discuss how standardizing IDMS usage helps integrity teams perform more efficiently and effectively.


There are many reasons to standardize a process, and reliability is at the top of the list.  In the world of asset integrity data management this means that data owners can have confidence in the data they use to make critical decisions, often related to safety and compliance. Decisions such as whether a pressure vessel should be opened for an internal inspection or whether a new tower should be purchased with a two-year lead time for replacement during the next turnaround can have a significant impact on a business’s operations.  It’s important that these decisions are made using reliable data.

The lack of standardization creates a database environment where information can be disparately managed. This negatively impacts reporting quality and, in the more extreme cases, creates blind spots that increase safety and compliance risk without users knowing until it’s too late.  In the example below, the equipment types have been entered differently by different data clerks although they should all be identical. A report has been run to identify all equipment with Equipment Type = “Vessel” and a Next Inspection Date in 2019.

Equipment Type Next Inspection Date
Vessel 01/30/2019
Vess. 07/15/2019
PV 02/18/2019
Press. Vess. 11/29/2019

In this scenario, the report will only return the first item on the list and thereby allow the other three items to go unaddressed.  With the same issue existing in a report for Overdue Inspections, the three pressure vessels will remain in operation, unaddressed, until their eventual failure.


These types of hidden issues are often discovered by accident, elevating an inspection team’s concern over data quality and reporting accuracy, and causing them to begin campaign style clean-up projects. These can be quite inconvenient given that the database is still being used for daily operations. Also, any amount of clean-up is essentially rework. Companies are paying twice for the data to be entered properly. Where possible, users should document their data management requirements before implementation so that the data is entered correctly the first time. Having a documented standard for data entry helps ensure that the database is managed consistently, preventing later large-scale clean-up projects.

Standardization can also help data entry staff become more efficient at their job by allowing them to perform the same task the same way each time. This has a potential side benefit as well. Faster data entry typically reduces the time required to determine if there is a mechanical integrity concern on a given equipment item. Reduced on-boarding time is another benefit. When new employees complete training but still have questions, they now have a document they can refer to for assistance.

The final benefit, albeit not an immediate one, is that standardized data facilitates later implementation of bolt-on technology such as a mobile data collection device or integration with external systems (e.g. work management, risk assessment, accounting, etc.).


As we saw in Part 1 of this series, the absence of a well-defined and documented data management practice is one of the leading causes of IDMS issues.  Experience has proven that it is not enough to simply document data entry procedures. Instead all other key aspects of information management must be defined and documented as well. This includes areas such as work processes, roles and responsibilities, information storage locations, data access protocols, and permission levels, to name a few. A robust data management standard will help teams to address each of the concerns raised herein and when coupled with a role-based training program, it becomes a very effective tool for applying the necessary rigor to properly manage an IDMS.


Many companies use only the built-in structure of the IDMS to drive standardized usage, however, it should now be clear that this is not only insufficient, but there are several other benefits to creating a detailed and rigorous data management standard.  Standardization is a critical stepping stone on the path toward a world class data management program.


Next is the final part in this series, “The Implementation Hurdle.”  In it, readers will gain insight to the largest obstacles facing a successful IDMS implementation.