New Century and Integrity Plus Blog

Staging: A Dunder Mifflin Case Study on Data Protection

Posted by Rachael Webster & Sarah Spaulding on Sep 20, 2018 12:24:21 PM

For data to be meaningful, it must be complete, accurate, and valid. We strive to protect the data that drives reports and analyses to ensure that organizations can make the best decisions. However, those who are responsible for data management of pipeline systems understand the challenges involved in balancing data security with making sure that it can be updated in a timely fashion.

It is not enough to merely lock users out once your data is in a good state.  As maintenance is done on assets in the field, it is essential to update the information in your GIS as quickly as possible to make sure that the data consumers in your organization have access to is the most accurate, complete, and valid information possible.

Enter: Staging, a permissions-based approval system that allows lower-level users to safely interact with data in a protected environment, and then submit it to a higher-level user for review before the changes are implemented in production.

For demonstration purposes, let’s pretend that instead of being a paper sales company, Dunder Mifflin was actually a pipeline operator. Let’s take a look at how some of your favorite characters would utilize Staging to increase efficiency while protecting their data!


In Staging, there are three primary roles: Decisionmakers, Gatekeepers, and Database Users.

Michael, the Decisionmaker, uses the GIS in his analysis and decision-making process.  He expects what he sees in production to be a real representation of what is out in the field.

Jim, the Gatekeeper, has been around a while and has a deep understanding of the pipeline data and the GIS.  He is trusted to manipulate the data, but he is swamped with lots of reports and needs some help catching up on the backlog.

Ryan has just been hired to help Jim out, but he is new to GIS and pipelines. He needs some supervision to ensure he doesn’t corrupt or misrepresent data.

Jim and Ryan are using a PODS database to hold all the data for their system.  This PODS database has been expanded to hold Staged edits.  Gatekeepers like Jim have been granted permissions to make updates directly in PODS using tools like Facility Manager and Express Loader; while editors like Ryan are not allowed to update PODS directly.  Ryan’s edits must be staged and reviewed before they can affect the production database.

Ryan can see and view production data, but as soon as he tries to insert a new record or update an existing record, a copy of the production record is made in Staging.  While the data is in Staging, Ryan can make any changes that are needed.  Once he completes his changes, he submits them to Jim for review.  Jim has the opportunity to review all the changes that Ryan made and can either approve or reject those changes.  Rejected edits go back to Ryan for corrections, while approved edits are pushed back to PODS where the information can be seen by Michael.

Here is a graphical look at the workflow, so we can get a better idea of how the data flows through Staging:


Following this data management cycle will help ensure the accuracy and organization of your company’s data.

Looking for more information on how to get this data cycle implemented in your organization? Contact us today! See Staging in action in our Staging: An Enhanced Editing Experience webinar. 

Topics: Data Management, data cleanup, Staging

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