Dirty data kills more automation projects than bad technology ever will. The failure is usually hidden until the bot has already been built and quietly produces the wrong results. This is the most expensive moment to discover the issue.

We learned this the hard way while automating searches for a single county, agent-owned, title plant. From the outside, the client’s decades of indexed records looked pristine. As we dug into the project, we found that indexing standards had shifted repeatedly across 30 years. Critical instrument numbers were formatted differently depending on when they were entered, so our search bot missed pertinent records that didn’t match the latest convention.

The bot ran exactly as designed, but the underlying data was the problem. Inconsistent records will defeat even well-built automation.

What Dirty Data Actually Looks Like in a Title Plant

Title plant data built over decades is rarely uniform. Staff changes, software migrations, and evolving county conventions all leave their marks on how records are entered and categorized. A document that should be retrieved in a title search, is overlooked because the field used to find it doesn’t match the value(s) stored against it.

For bots that rely on structured search logic, dirty data becomes a systematic failure point rather than a rare exception. The bot does what it is trained to do. When the underlying records do not behave consistently, the output can’t either.

The problem is subtle enough that clients sometimes do not catch it immediately. Processing looks normal and volume keeps moving through. It surfaces only when someone digs into a specific file and asks why a document is missing, and the root cause finally becomes visible.

Discovery Questions That Surface Data Readiness Issues

Before any automation project begins, a handful of questions need honest answers.

How long has the data lived in this system, and how many teams have been responsible for entering it? Has there been a production system migration, and was the data validated after the move? Are there known inconsistencies in how records were categorized across different time periods or even different offices?

For title plant work specifically, the key question is whether the indexing structure is consistent enough for a rules-based lookup to return reliable results. If the answer is, “we’re not sure” that uncertainty should be resolved before development begins.

We also pay close attention to who the client-side point of contact is during discovery and documentation. A project where the engaged stakeholders lack good familiarity with the underlying platform, or they aren’t fully engaged upfront, is a project where issues tend to surface late. The feedback loop between the automation team and the client team needs to stay active.

What the Honest Conversation Looks Like

When we identify data consistency concerns during discovery, we point them out. The conversation isn’t comfortable, but it’s necessary. Telling a client that their data needs to be cleaned up before automation can succeed is better than building a bot that runs unreliably which can kill confidence in the entire project.

Sometimes partial automation can be achieved while data quality enhancement work is underway. Other times the cleanup effort is larger than the client anticipated, and our timelines have to shift. We work to avoid discovering these issues weeks into development, and after significant resources have been spent by both parties.

You can read more about how we scope and evaluate automation projects in our breakdown of when a process is not ready for automation, including the criteria we use to decide whether a process is ready to be automated.

Treating dirty data as foundational work, to be handled before the build, separates successful automation projects from automation projects that grind to a halt. Industry research backs this up, with Gartner putting the average annual cost of poor data quality at $12.9 million. The vendors who skip this step are the ones whose projects tend to end up on the shelf. Just like the one we had stall out on us.

Jimmy Lewis is the co-founder of TrueFocus Automation, a specialist in RPA and AI-driven workflow automation for the title insurance, mortgage, and real estate industries. TrueFocus has developed 840+ automation bots supporting more than 2,500 workflows and has returned over 1.3 million production hours to clients.