Row 89267

Row ID: 89267 | Dataset Entry | Axioma AXP Content Repository

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Today? Tomorrow? Probably not a lot. It’s a colossal investment to get a data science team off the ground. First you need a mature data analytics team, then a mature data engineering team, then a mature data infrastructure… *Then* you’re ready to benefit. A lot of companies are pushing to implement AI before they even have a mature data analytics team, much less the data infrastructure to support CI/CD AI development. Without that support structure, yes, AI (and DS in general) is useless.

But, that long-term investment is useful for the same reasons that data analysts are a useful investment. As data scientists, we’re not here to tell you how to do business things. That’s your job. I always yield to my stakeholders’ domain knowledge. We’re here to understand which decisions you need to make and provide analytics tools to equip you with more information to make those decisions. *That’s* the value of AI: a well-supported team can provide great information to aid in decision-making. The guy in the article who said that it “ultimately comes down to learning [the new tools]” is spot on. I can’t tell you “this provider is committing fraud and you need to sue them” - but I can build a tool which says “based on previous patterns, this provider looks suspicious and it sure seems like we should sic a fraud team to investigate.”. That suggestion is based on a lot of data scrutinized by data analysts and structured by data engineers. It takes a village, and a lot of companies ignore that fact in favor of a catchy marketing term.

Also, telling your employees to check out ChatGPT as a way to write more eloquent emails doesn’t count as “implementing AI”. A lot of people seem to get that confused.

FieldValue
text Today? Tomorrow? Probably not a lot. It’s a colossal investment to get a data science team off the ground. First you need a mature data analytics team, then a mature data engineering team, then a mature data infrastructure… *Then* you’re ready to benefit. A lot of companies are pushing to implement AI before they even have a mature data analytics team, much less the data infrastructure to support CI/CD AI development. Without that support structure, yes, AI (and DS in general) is useless. But, …
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
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Raw Record

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  "text": "Today? Tomorrow? Probably not a lot. It’s a colossal investment to get a data science team off the ground. First you need a mature data analytics team, then a mature data engineering team, then a mature data infrastructure… *Then* you’re ready to benefit. A lot of companies are pushing to implement AI before they even have a mature data analytics team, much less the data infrastructure to support CI/CD AI development. Without that support structure, yes, AI (and DS in general) is useless.\n\nBut, that long-term investment is useful for the same reasons that data analysts are a useful investment. As data scientists, we’re not here to tell you how to do business things. That’s your job. I always yield to my stakeholders’ domain knowledge. We’re here to understand which decisions you need to make and provide analytics tools to equip you with more information to make those decisions. *That’s* the value of AI: a well-supported team can provide great information to aid in decision-making. The guy in the article who said that it “ultimately comes down to learning [the new tools]” is spot on. I can’t tell you “this provider is committing fraud and you need to sue them” - but I can build a tool which says “based on previous patterns, this provider looks suspicious and it sure seems like we should sic a fraud team to investigate.”. That suggestion is based on a lot of data scrutinized by data analysts and structured by data engineers. It takes a village, and a lot of companies ignore that fact in favor of a catchy marketing term. \n\nAlso, telling your employees to check out ChatGPT as a way to write more eloquent emails doesn’t count as “implementing AI”. A lot of people seem to get that confused.",
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  "datetime": "2024-05-25",
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