Last post, we established that strategy and alignment come first. Today, we address the biggest post-launch killer: 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥 𝐐𝐮𝐚𝐥𝐢𝐭𝐲.
For a basketball team, this is the difference between an average edge and a dominating one.
One of the four core data quality challenges tied to Generative AI failure is 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲—the trustworthiness and accuracy of the data.
Ask your analytics team: 𝐈𝐬 𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐭𝐫𝐮𝐥𝐲 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐚𝐫𝐲 𝐚𝐧𝐝 𝐩𝐫𝐢𝐬𝐭𝐢𝐧𝐞?
Are all player movement tracking points accurately tagged and cleaned?
Do our internal load management sensors have consistent calibration across the entire roster?
Is the model trained on our proprietary, niche competitive data, or just common public stats?
Garbage In, Gospel Out: Low-quality data leads to models that are either untrustworthy or produce generic insights—not the 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐄𝐝𝐠𝐞 you are paying for.
Share this post with your Head of Analytics. Ask them one question: "𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐢𝐧𝐠𝐥𝐞 𝐡𝐢𝐠𝐡𝐞𝐬𝐭-𝐯𝐚𝐥𝐮𝐞 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐚𝐫𝐲 𝐝𝐚𝐭𝐚 𝐬𝐞𝐭 𝐰𝐞 𝐨𝐰𝐧?"
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