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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Understanding the Bardess Data Science Maturity Curve: Phase Four “Integrated”

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. I recently interviewed Daniel Parton, Lead Data Scientist with Bardess, for more insight about each stage of the curve. 

The Bardess Data Science Maturity Curve

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Understanding the Data Science Maturity Curve – Phase 4: Integrated

As data science programs mature through Phase 1, Phase 2 and Phase 3, the opportunities and challenges shift. Mileage may vary, explained curve author Daniel Parton, lead data scientist with Bardess Group. He said some of the greatest changes happen in the move from what Bardess defines as “Phase 3 – Functional” to “Phase 4 – Integrated.”

“This transition between the functional and integration, the third and fourth stages, is probably the most risky of them all. At that point, you have a lot of skin in the game. You have dedicated data scientists. You have dedicated teams, perhaps. I’ve seen quite a few cases of companies who get themselves to that point. But then, a couple years pass and they feel like they’re not getting that much value.”

Why the plateau? In many cases, companies have failed to integrate data science teams both technically and culturally.

On the technical side, there are a few common culprits:

  1. Not providing data and/or easy access to data.
  2. Lack of a solid data architecture.
  3. Not figuring out how to productionize the results and insights uncovered by data science teams.

On the cultural and management aspects, Parton cites the biggest issue as “isolated” data science teams who talk to each other, but find it difficult to communicate with business stakeholders. In this phase, he recommends that both sides put more effort into learning each others’ jargon, and that organizations hire for fluency in both data science and business.

Many programs get stuck in Phase 4, and are in danger of one major problem, Parton said.

“The biggest risk of all is that just people start to lose confidence in data science itself. Maybe the team gets some initial wins, but after two years, has maybe nothing that’s really lasting. The company might be wondering sort of why are we spending all this money on this data science team?”

Avoiding this “pit”, as Parton calls it, and addressing technical and cultural challenges are requirements for companies to progress through this “Integrated” Phase 4. This is where companies need data leaders, and the right technology foundation, to elevate themselves to the Maturity Curve’s final stage, where data science is “Cultural.”

Watch this Data Science Maturity Curve interview excerpt to hear Daniel share more about “Phase Four – Integrated”  – and hear more about how to mature an enterprise data science program from functional, to integrated, to cultural. 

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