Models for integrating data science teams within companies

A comparative analysis

At our inaugural DS Crit meeting.

A DS team isn’t just the people, it is the process and the interaction of the team with the rest of the company.

dj patil

The goal of work is some output—a strategy, product, marketing plan, budget, account plan, sale, feature, etc. Communication is a way of incorporating stakeholders into a plan *before* it is too far along to change or the cost is too high (or coworkers too angry!)

Steven Sinofsky on Twitter

The center-of-excellence model

My view is everyone is on the same calendar/cadence. That’s a huge thing for me. If you don’t have that then split resources (all of them) by cadence. Teams on difference cadences can’t collaborate.

Steven Sinofsky on Twitter

Accounting model

The consultant model

The embedded model

The democratic model

The product data science model

Pete Skomoroch on Twitter

Since there is no optimal or perfect organizational structure […] then the most important thing is to know the weaknesses of your structure and to compensate for them.


All large organizations with a common business purpose end up in a hybrid organizational form.

We cannot expect that this problem will be solved by first communicating all this knowledge to a central board which, after integrating all knowledge, issues its orders. We must solve it by some form of decentralization. But this answers only part of our problem. We need decentralization because only thus can we insure that the knowledge of the particular circumstances of time and place will be promptly used. But the “man on the spot” cannot decide solely on the basis of his limited but intimate knowledge of the facts of his immediate surroundings. There still remains the problem of communicating to him such further information as he needs to fit his decisions into the whole pattern of changes of the larger economic system.

Embedded for context, relevance, communication efficiency, and to be in sync; centralized for hiring and promotion purposes, for peer review, for sharing and maintaining best practices [, for global optimization, and to align on strategy].

—@djpardis on Twitter



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