What signals maturity on an analytics team?

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Process flows that are set in place for any requests that come in and allow for seamless delivery frequently

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Seems legit, but requires users to proactively ask. E.g. a backlog for offshore data warehouse analysts doing SQL pulls passes this definition, but is generally very far from meeting user needs when compared to some pipelines

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Not assuming everything is super easy lol. Sounds stupid but it’s true.

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Also willing to pay for the tools necessary.

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Cannot agree with this more. Tools like fivetranand and astronomer save you so much engineering hours

1. They are not doing analytics only but have moved on to predictive analytics, automated discovery and machine learning.
2. They also have an MLOps team or at least a few engineers backing them up.
3. The tech stack isn’t just compromised of SaaS offerings like Sagemaker but your team is also leveraging OSS and has built their own toolset which fits your niche better.

I’ve been working as an MLE for the past 4 years.

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Using Google Cloud Platform

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Lol

But also, don't think a modern tech stack reflects maturity in itself. Lots of startups with the latest greatest tools but not really doing anything with it

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A monocle

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Great documentation to onboard new people or services

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Totally agree! Some place don't even have a data directory or an ER diagram

Not just being SQL monkies

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Really? I was looking at CVS but their tech stack was a lackluster, I wonder if it has gotten better. You guys still use EMR clusters and that old Hadoop stack?

Mastery of DataOps, e.g. by delivering high value impact quickly and continuously, and by efficiently addressing issues early.

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Best practices for how Data Teams work are still maturing. That said, I think they’ll resemble software engineering teams more than anything in the future. Process for analytics requests is not it, a mature team won’t be a part of IT (will have a strong relationship/trust there though) but rather have their own roadmap, goals, and value prop for why they exist. They’ll have a design process in place that works with the business to build stuff that will be useful and have product managers just like a software development team does. Just a couple things I haven’t seen mentioned in this thread yet.

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Besides modern tooling and best practices, well defined Analytics objectives with a direcr tie-in to business objectives and clear tracking of progress towards those objectives.

Well defined roles, collaborative processes and unified tools. Reusable & accessible assets and libraries to accelerate development. Automated processes, testing, and monitoring set up for fast deployment & operational excellence. Has a centralized, managed data Lake with built in governance, lineage, and quality control. Has moved beyond ad hoc analytics to production ml for both cost reduction and revenue growth use cases. More focused on optimization and scaling than science projects. Clear vision of value for new use cases.

Wider teams trust and use the outputs of the analytics team

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