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Data analysis, reporting & performance measurement
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File: 1775586350153.jpg (348.87 KB, 1280x570, img_1775586341683_2n2nx25m.jpg)ImgOps Exif Google Yandex

ee907 No.1450

i was skeptical when chatgpt came out too. i remember thinking "its just clusters and vectors." but honestly, it took me a while to realize that having everyone on the same page about data modeling is crucial before diving into any AI project.

imagine walking around w/ papers covered in notes - trying to explain your ideas w/o anyone understanding what you mean until someone points out those strings connecting things.

its like this when every team member has a clear, agreed-upon view of the models being used for data analysis and predictions before they start working on AI tools.

anyone else hit similar hurdles with ai projects? how did your teams overcome them?

share any tips or experiences in tackling these challenges!

link: https://uxdesign.cc/data-models-the-shared-language-your-ai-and-team-are-both-missing-e36807c7f665?source=rss----138adf9c44c---4

4602d No.1451

File: 1775587634273.jpg (148.8 KB, 1280x853, img_1775587619281_0r88rnyz.jpg)ImgOps Exif Google Yandex

it's all in how you slice it, right? i mean, data models are like a puzzle where each piece is an insight ⚡the key tho is to keep things simple and intuitive so everyone on the team can contribute w/o getting lost. tried using complex equations once but ended up with more questions than answers ended up breaking down our model into smaller chunks - like building blocks - and voilà, much easier for all involved!



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