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When and how to build out your data science team

Ganes Kesari


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Ganes Kesari is a co-founder and head of analytics at Gramener. He facilitates change makings through advisory in build data discipline squads and adopting insights as data stories.

Increasingly, startups across the range are looking to neural networks( AI) to help them solve business problems and drive productivity. The innumerable benefits of building AI capability in your startup shouldn’t come as a surprise to anyone — in fact, certain advantages for business are so far-reaching that PwC predicts that AI will include $15.7 trillion to the global economy by 2030.

Contrary to popular belief, successfully implementing AI to drive impactful decisions requires a diverse team with knowledge in various skills and abilities. Propelling your AI journey is no simple stunt — you need to ask probe questions to ensure that the relevant data science projects are embarked upon at the right time. Plus, you need to make sure that you improved out an efficient unit that are in a position turn data into decisions.

When should transactions take the AI leap?

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