The Hidden Costs of Cloud Data Lakes

This blog series from Cazena's engineering team investigates the hidden costs of cloud data lakes. Learn the top three hidden costs of cloud data lakes!

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The Bardess Data Science Maturity Curve: 5 Phases Explored

Hannah Smalltree, Cazena & Daniel Parton, Lead Data Scientist, Bardess

The Data Science Maturity Curve

The Data Science Maturity Curve is a tool developed by our partners at Bardess Group. This maturity model helps teams visually plot their progress and set goals.

The Bardess Data Science Maturity Curve

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The Data Science Maturity Curve is a useful tool for organizations thinking about their data science evolution. The curve plots the key milestones, stages of growth and transformation. The Cazena team often uses this in our conversations with those in a Chief Data & Analytics Officer or similar role. This particular model resonates with many data teams, for one big reason.

This is based on real-world, hands-on data science experience. That means it’s holistic, addressing people, process and technology issues, not just one topic. The background knowledge was gained in the trenches, by the savvy team of data scientists and elite consultants at Bardess Group. The company has 20+ years of experience at improving outcomes from analytics in a variety of industries. They are, as we say in Boston, “wicked smart,” with lots of interesting stories to share.

So, we recently interviewed Daniel Parton, Lead Data Scientist with Bardess, to ramp up on the Data Science Maturity Curve, and learn the stories behind these stages. It was a highlight of the week. Daniel is the primary author of the Maturity Curve and very much a practitioner – logical, grounded and genuinely interested in helping his fellow data scientists get better at their craft. We talked one afternoon on Zoom, and I started with a slow pitch.

Why create the Data Science Maturity Curve?

“There’s obviously a lot of hype in the industry in general around data science. I think a lot of companies find it quite difficult to understand where they’re at with data science, or if they’re not doing it currently, then how can they get there?

We wanted to map out what we had been seeing across industry, in terms of how different companies are tackling data science as they grow into it.”

Importantly, Daniel clarified, the journey is not just about evolving your data science platforms or the most recent tech. While tech capabilities are part of the equation, the curve also highlights key organizational issues and potential cultural pitfalls for data science success.

For this series, we’ll share more insight about each stage of the Bardess Data Science Maturity Curve, with live interview snippets for more detail. Along the way, we delve into some common misconceptions and risks to avoid at all costs.

Explore the Series: Understanding the Data Science Maturity Curve

Phase One: Learning. Everyone has to start somewhere — jumping into new jargon, technology and cultural shifts. Read more about data science in phase one.

Phase Two: Emerging. One organizations have started resourcing and making progress, programs move to the “emerging” phase two. However, now they face a new set of challenges.

FAQ: Can You Use Laptops for Data Science? Spoiler alert: It depends. Hear a data scientist’s perspective on the laptop question, and when organizations need to consider a more formal tech stack.

Stay tuned for more segments and please share your feedback!

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