What Problem Does This Automation Solve?
Shane opens the episode by framing a challenge that affects technology providers, MSPs, and most data-driven organizations: collecting a large number of data points per client is only half the battle. Knowing which fields are missing, which entries are outdated, and which values are formatted inconsistently is the harder problem. With hundreds of potential data points per client, gaps tend to go unnoticed until they cause real service issues.
What Kind of Data Gaps Were Showing Up Internally?
Hunter shares two concrete examples from their own documentation platform. For a vendor record, the name and website might be present, but the support phone number is missing entirely. For a customer location, the full address might be crammed into one line while the city, state, and zip code fields sit empty. In both cases, the information technically exists somewhere, but it is incomplete or hard to use in practice.
How Does the Automation Work?
The automation pulls every asset from their documentation platform on a scheduled basis. Assets are structured layouts that organize documentation by category, such as phone systems, passwords, and locations. Once the data is ingested, the automation checks two conditions for each record.
First, it checks for blank fields. Any asset with one or more empty required fields is flagged. Second, it checks the last-updated date. If a record has not been touched in more than three months, that also triggers an alert. When either condition is met, the automation creates a ticket and notifies the team to review and update the record. The entire process runs on a cron schedule, so the checks happen automatically and consistently without any manual effort.
Why Does This Apply Beyond the MSP Industry?
Shane points out that the same two failure modes (missing data and stale data) appear in CRM systems, financial records, customer databases, and any other platform where structured data accumulates over time. He adds a third failure mode that is easy to overlook: formatting inconsistency. A phone number entered as 417-334-XXXX, 417.334.XXXX, or 417334XXXX may all represent the same contact, but non-uniform formats can break integrations and automated workflows that depend on that data.
What Happens When Documentation Goes Three Years Without an Update?
Shane raises a risk that MSPs in particular face: technicians rely on internal documentation to provide support every day. If a process or software configuration has changed but the documentation was never updated, the team may be working from incorrect information. Regular automatic updates, software version changes, and busy schedules all contribute to documentation drift. Without a mechanism that surfaces these aging records, the problem is invisible until it causes a failure.
How Did Site Photos Lead to This Automation?
Hunter explains that this approach was already in use for site photos. Photos were going stale because they were not being updated on every site visit. An earlier automation was built to fire a ticket as a reminder whenever a photo record aged past the threshold. The new documentation gap automation extends that same logic to all other asset types in the platform.
How Do You Build on This Foundation Over Time?
Shane describes the pattern the team follows across all of their automations: get the data, raise awareness, create action, then build on that. Once the initial gap-detection automation is running and producing reliable output, additional automations can be layered on top to act on the results in more sophisticated ways. The first step is simply making the problem visible on a predictable schedule.