Building Data Science Teams by DJ Patil

Author:DJ Patil
Language: eng
Format: mobi, epub, pdf
Tags: COMPUTERS / Data Processing
ISBN: 9781449316235
Publisher: Radar
Published: 2011-09-15T04:00:00+00:00

Organizational and reporting alignment

Should an organization be structured according to the functional areas I’ve discussed, or via some other mechanism? There is no easy answer. Key things to consider include the people involved, the size and scale of the organization, and the organizational dynamics of the company (e.g., whether the company is product, marketing, or engineering driven).

In the early stages, people must wear multiple hats. For example, in a startup, you can’t afford separate groups for analytics, security, operations, and infrastructure: one or two people may have to do everything. But as an organization grows, people naturally become more specialized. In addition, it’s a good idea to remove any single points of failure. Some organizations use a “center-of-excellence model,” where there is a centralized data team. Others use a hub-and-spoke model, where there is one central team and members are embedded within sponsoring teams (for example, the sales team may sponsor people in analytics to support their business needs). Some organizations are fully decentralized, and each team hires to fill its own requirements.

As vague as that answer is, here are the three lessons I’ve learned:

If the team is small, its members should sit close to each other. There are many nuances to working with data, and high-speed interaction between team members resolves painful, trivial issues.

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