You don’t need to dig through your systems to recognize just how much data the automotive retail industry has accumulated over the years. Data is spread across your DMS, CRM, service, sales, marketing, and other systems, creating a wealth of information that can help you sell and service more effectively. But these systems don’t always structure or update information the same way. This may have caused a few problems in the past, but it largely didn’t impact your day-to-day operations.
Until now.
With the explosion of AI in the automotive retail market, dealerships are saving time and boosting productivity like never before. But there’s a catch. AI is only able to deliver those efficiency gains when it has clean, unified data to pull from.
Imagine an AI tool answering a service call. A customer asks about a vehicle’s service history, but the vehicle or customer record appears under multiple names across systems. Which record should the AI use? If the underlying data isn’t clean, the AI can confidently give a completely wrong answer.
Even if you currently have an accurate database, there’s only a slim chance it will stay that way long-term. During the
Amplify workshop it was revealed that, annually:
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Email addresses change about 30 percent.
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Phone numbers change about 25 percent.
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Home addresses change about 14 percent.
Outdated information doesn’t just affect your employees when they’re trying to pull information. There’s a good chance it will impact any marketing or sales initiatives at your dealership. Without a centralized data hub, customer outreach can go to the wrong person or outdated contact information. Or sales and service teams may be working from different versions of the customer story. These impacts may feel small at first, but they’re likely to lead to wasted time, missed sales, or lost opportunities to build lasting customer relationships.
So, how do you get a clean database and keep up with the information that seems to change constantly? The answer is simple, but many dealers aren’t leveraging it: A unified data layer.
The Structure of a Unified Data Layer
You may have heard the term “unified data layer,” but what does it actually mean? A unified data layer is a real-time layer that sits between source systems and the applications, including AI tools, that need to use that data. In other words, it connects existing source systems, resolves and organizes information, provides a clear view of the data, and gives AI tools access to accurate, contextual information.
One of the best ways to understand the different aspects of a unified data layer is to think of it in terms of human anatomy:
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The Skeletal System (Data Structure): Organizes data consistently across systems so AI can understand how fields, customer records, vehicle IDs, and deal IDs relate to one another.
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The Nervous System (Identity and Context): Connects information to the right customer, vehicle, or household by considering the context behind records, not just matching names or contact details.
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The Circulatory System (Data Connectivity): Moves information between systems and gives AI tools consistent access to the data they need to work together, rather than operating as disconnected tools.
Individual AI tools may have some of the “systems.” Still, without a unified data layer that brings all three together, data can become fragmented, customer information can be misinterpreted, and AI tools may produce incomplete or inconsistent results.
One Connected Experience
Many individual AI tools are entering the market, but a unified data layer is the differentiator. It creates a foundation that enables:
From there, the possibilities are endless: Your team can make more informed decisions, reduce manual work, improve customer interactions, and so much more.
