Tossing a low-quality data set of incomplete and outdated contact information into an AI platform is the fastest way to get fictional stories about your customers’ behavior. It seems simple enough to upload customer information that hasn’t been touched in years into an AI processor and get something worthwhile out of it. But when it seems too good to be true, it usually is.

Data needs to be cleaned and ready before AI can interpret it and provide real direction. Here are three principles to help frame the mission:

1. Data Stacks Need to Be Built in a Linear Order.

All data stack layers are not created equal. The order in which you build the data stack decides what you get out of it.

Most dealers buy the stack from the top down. AI, chat, attribution and a customer data platform are piled on top of a base that nobody’s audited.

The base determines the end value. When you take a closer look at that foundation, it begins with clean, unified data. The next layer is identity. Signal and intent follow. The workflow completes at activation.

Ryan Rohrman, CEO and third-generation leader of Rohrman Automotive Group, was committed to improving his data stack quality across his 22-franchise enterprise. After discovering that roughly half of the group’s customer data was outdated or inaccurate, Rohrman took action.

The result was lead conversion rates exceeding 30%. His takeaway from the experience is straightforward: “Data will monetize if the foundation is strong.”

Treat AI as the final layer. Successfully constructing your stack should reinforce each layer above it.

2. What Does “Bad Data” Actually Mean?

Bad data usually means something more specific than dealers expect. The phrase implies that the records are compromised in a way that is obvious to anyone who takes a quick glance.

In practice, it’s usually a record that looks fine. The name is right, the phone number is right, the vehicle is right. But the email field holds a placeholder somebody typed in years ago.

If email is the channel your campaign runs on, that record is now unusable. Similarly, you may have no way to reach individual drivers in households with several vehicles attached to a single contact.

Records may look complete, but a single discrepancy in the data can cause your campaign activation to fall flat.

3. Data Hygiene Is a Healthy Habit.

A database you clean once doesn’t stay clean forever. Record accuracy becomes vulnerable over time. Corrosion happens through ordinary activity at the service drive and the sales desk. According to Cox Automotive’s 2024 Power of Data study, “54% of dealers have experienced conflicting data across multiple sources, making it difficult to determine which data is accurate.”

One healthy habit is to conduct cleansing, enrichment and enhancement on a monthly basis. Consumers don’t stay static, and neither does their contact information.

In reality, cleaning data is a heavy lift. Some dealers expect that cleaning data will be an overnight fix. But for the longest-lasting results, the process can take up to 18 months.

It’s also beneficial to follow a “less is more” approach. Focus on cleaning records actively in use rather than the entire database. That scoping is what keeps the cost reasonable and sustainable.

The more AI solutions you layer into a bad data environment, the worse your solution is going to get. Take some time to evaluate what share of your DMS and CRM is accurate. That percentage is what every dollar and every tool you own has to work with.

Mike Morgan is chief revenue officer at Launch Labs.