The lessons learnt from planning and executing this particular part of the product lifecycle - the Product Withdrawal.

Why to keep my product "clean"? What is the impact of under-performing products and features on the brand recognition, value of other products and features in the portfolio and team's motivation?

Sample questions and considerations:
- When is the right time to retire?
- How to get aboard my team and management to retire a feature or product?
- How to communicate the decision with the users and market?
- The decision is made. How to prepare for the product withdrawal?

Learn about growth hacking - both from sharing our own experiences and listening to effective techniques/experiments of others - in particular, how to use these skills and what it might mean for Product Managers (esp in regards to evaluating & proposing product enhancements).

MatchMaker and DealMaker are the two complimentary Buzz Technology platforms that are aimed at (1) entrepreneurs and innovators who are looking for new ways to fund their startup ideas into an MVP - minimum viable product - for crowd funding or investment (2) individuals who want to monetise their spare time.

So you've got a great product, it's getting ready to be shipped, how can you get people using it and gain their feedback to help improve it so it's successful.

I'd like to discuss best practise for prioritising hundreds of competing tasks in a product backlog which meets the needs of multiple stakeholders across the business.

We are merely caretakers of this world what we do now will affect the kind of inheritance left for our children and their children. Oil is limited, but is also used in a variety of applications. Instead of rationing it's use we should be using more....

In Cynefin we talk of hypotheses which are basically ideas and the exploration of these as long there is messy coherence, that is someone can make a coherent argument for a particular hypothesis. We are not trying to establish if a hypothesis is right or wrong, only that there is a degree of coherency that supports running an experiment. The implications are that we remove the need for there to be a formal justification for an idea as we are looking to validate them as part of establishing the options.

There's an ongoing debate on how much technical a product manager has to be, or if "non-technical" product managers even exist. This is also pretty evident in job postings for PMs, where technical requirements vary hugely, from strong programming and computing background to zero experience (although the first case seems most common).

Can we identify a valid "minimum" technical level for Product Managers?

Some food for thought for the discussion:
"Getting to “technical enough” as a product manager"
http://bit.ly/1SFmUr4

"PMs, you don’t need that technical degree"
http://bit.ly/1qmpDwm

The cloud is little more than an idealised data-centre, but to use it properly you have to build properly, what does that mean for a product manager?

Not data journalism, but data viz IN your product. How to visualize and represent data to your users so they take the right next actions?