How And Why Data Engineers Need To Care About Data Quality Now - And How To Implement It

As companies look to incorporate AI and ML into their data strategies and roadmaps, there is a new opportunity to refocus on data quality.

Regardless of how fancy or sophisticated a company's AI model might be, poor data quality will make the outputs of these models useless at best, and misleading and company destroying at worst.

So, as your company is rolling out its internally developed LLM or implementing a dynamic pricing model, it’s a great time to review your data quality strategy.

The first key point to cover is that data quality is not limited to one pillar, like accuracy. But instead, several key pillars need to be considered when developing your data quality system.

Again, special thanks to decube for sponsoring this video! If you're looking to improve your data quality you can check out the link below https://bit.ly/3Sndk1B

If you enjoyed this video, check out some of my other top videos.

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About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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