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AI definition aside, (which is a great point regarding definitions [USER=142]@ryan.leslie[/USER]) let's assume the AI and ML the advertising companies possess is true/reliable and also capable of extracting valuable information that would otherwise go unnoticed. AI and ML could provide remarkable insight for dealers!In addition to the challenge I think [USER=4107]@jon.berna[/USER] was highlighting in great "database architecture" (buzzword 2022 prediction) is that AI can’t distinguish between good data and bad data on its own, and the algorithms powering AI must assume the data being analyzed is reliable and clean. Bad data, at best, will produce results that aren’t actionable or insightful. Bad data can lead to results that are misleading. In addition to the time and money wasted analyzing bad data, AI can encourage a company to take steps that are even more wasteful. With many dealers having a glut of data, the bigger question becomes: How is the quality of the data?How many dealers make it a priority to "clean" their data regularly? Prediction: Very Few. Even simple things like NCOA scrubs, info appends, and removing duplicates aren't common practice in automotive.***Disclaimer: I used AI and ML to make the prediction above.
AI definition aside, (which is a great point regarding definitions [USER=142]@ryan.leslie[/USER]) let's assume the AI and ML the advertising companies possess is true/reliable and also capable of extracting valuable information that would otherwise go unnoticed. AI and ML could provide remarkable insight for dealers!
In addition to the challenge I think [USER=4107]@jon.berna[/USER] was highlighting in great "database architecture" (buzzword 2022 prediction) is that AI can’t distinguish between good data and bad data on its own, and the algorithms powering AI must assume the data being analyzed is reliable and clean. Bad data, at best, will produce results that aren’t actionable or insightful. Bad data can lead to results that are misleading. In addition to the time and money wasted analyzing bad data, AI can encourage a company to take steps that are even more wasteful. With many dealers having a glut of data, the bigger question becomes: How is the quality of the data?
How many dealers make it a priority to "clean" their data regularly? Prediction: Very Few. Even simple things like NCOA scrubs, info appends, and removing duplicates aren't common practice in automotive.
***Disclaimer: I used AI and ML to make the prediction above.
