What if your repair data actually told you what to do next? We sit down with Austin Ledgerwood, National Director of Sales and Insights at Motor Information Systems, to trace how messy service records become clean, standardized, decision-ready intelligence that changes pricing, warranty calls, stocking, and fleet strategy. From a Mountain Dew green Kia lesson at the auction to AI models that harmonize “OC W32” and “oil change,” we dig into the quiet work that makes insights possible—and profitable.
We explore how Motor’s Repair Optics and the Navigator dashboard expose true costs by zip code, break down parts and labor with clarity, and give teams without a data science bench the power to ask sharper questions. Think warranty adjudication grounded in reality, pricing that reflects local markets, and dashboards that surface seasonal and weather-driven shifts in tires, brakes, and routine maintenance. Along the way, we challenge the habit of chasing data that confirms a pet theory and swap it for root cause thinking that actually fixes problems.
Then we look ahead: EV cost of ownership at high mileage, rideshare and car-sharing models, fleet maintenance at scale, and why timely, standardized inputs will decide who gets ahead as autonomy and consolidation reshape the aftermarket. The takeaway is simple but urgent—most companies don’t have too much data; they have too much bad data. Clean the inputs, frame the right question, and let the market speak through the numbers.
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