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Building Cross-Functional Teams to Create Innovative Data Products (Anu Tewary)

Overall thoughts on the presentation:

Anu’s presentation is relevant to all teams working in data space and building products with a data driven focus. I looked her idea of having candidates interviewed by members from diverse backgrounds and cross roles. Her idea of pods is also interesting and coupled with cross pollination, it paves the way for automatically empowering team members to become experts.

Key points from the presentation

3 steps to Risa(star trek) –
– Build a great team
– solve a big problem
– get out of the way

What does it mean to build an awesome team:
–  hire people that are nice,
– people being hired for a certain position should be inherited by other disciplines like data’s scientists and PMS,and women to interview men.
– finding the right mix is important when hiring a team. Her company makes sure product managers are able to write their own hadoop job and query. Similarly the other roles.

– Her company then forms pods around products, and then hires people that are needed to meet a product goal. The pods are such that they can work autonomously and members can pick up different tasks depending on needs. This is where team members with basic skills in data science, engineering and PM.

Identify the big problem
– make sure that the problem you are solving is uniquely positioned and then break it down to form pods.

-keep score. !measure the right things that actually have any impact on the product, not the feel good metrics.

– change , pivot and reassess every quarter.

Get out of the way
– trust the team to become experts. Move members across pods so they become experts in their area. Let them make the decisions.

– anyone can represent the team. Let engineers and data scientists represent the team.

Industry Examples
1 multinational banking team that focused on technology first, and had no data scientists.
2. A multimedia team that had a strong engineering team but not data team. The data dn engineering teams were lossely coupled. There was a huge disconnect between product and data.
3. A large advertising team that had a data driven team and great vision. But their engineering WS not on par and they din’t have a vision.

Key to a good team is to have product data and engineering to work together. They solve hard problems for individuals and businesses.