Never lose sight of the importance of teamwork in building a winning product, says Aha!'s Ron Yang.
In Summary: The best Product Managers bring people together. Building great products requires cooperation across the organisation. Reflecting on what he learnt during his career as a PM, Ron outlines the 6 principles that most often underpin success.
From continually focussing on 'why' the team is building the product, to sharing the vision, to recognising individual effort, Ron emphasises how the dynamics of great teams ultimately lie behind great products.
A list within a list, from Ken Norton.
In Summary: Machine learning is a big deal. Google is betting the company on it and some have predicted it could be 'the future of everything.'
Product managers need to understand the current state of ML and AI, and the opportunities that techniques such as deep learning provide for developing product.
Ken has taken the time to document the best content for getting up to speed on the basics and then diving into the details. Updated regularly on his website, this an essential resource for Product Managers.
Rian Van Der Merwe chats to Ravi Kumar on Yours Productly.
In Summary: The question of when a startup needs a Product Manager is contentious and the answer unclear. Not all founders are product people, even though they provide the vision that establishes what the product does.
Rian draws on his experiences at eBay and subsequently at Postmark to provide his assessment of where and when Product Management should occur in early stage product companies. He covers fear of process, lack of clarity about the role and the importance of getting to know the customer.
In Rian's view, a PM is necessary once it's no longer clear what the Product Team should work on next.
Marcus Frödin, Spotify's Director of Engineering, on Intent-Hypothesis Feedback loops.
In Summary: Spotify wants to be 'embarrassingly parallel' and 'good at failing'. To deal effectively with failure, Spotify aims to be a learning organization with short feedback loops.
Spotify uses a concept that they call Data Insights Beliefs Bets (DIBBs). DIBBs are 'things they believe about the world, where they want to understand why they believe it." One DIBB is the 'belief' that speed of iteration beats quality of iteration: you have to ship often to gain value.
Another is 'over-reacting beats under-reacting': when in doubt ship it, put doers closest to value delivery, and hire for capability to change.
Spotify maintains a board with company bets; the things that they have to do right now.