Abstract: If we just leave teams alone, we’ll succeed!
If people just did Scrum right, we’d succeed!
Code craftsmanship will save us!
There’s a growing movement against “agile”. Team-focused approaches repeatedly fail to deliver meaningful organizational change. Frustrated agile coaches blame the client for lacking ‘courage’ to really commit to agile values. Clients frustrated with lack of 'real world' pragmatism from agilists.
Effective data-driven approaches to change have a more significant impact than shouting at leaders and product organizations about “doing agile right” and a big piece of the puzzle has been missing for some time.
That missing piece is the product. The product is what we build to delight the customer who then remunerates us for our efforts in hopes we’ll continue delighting them.
Product portfolio management is foremost about understanding ways to maximize returns on a limited number of investments available to our organizations. In order to do that we need to understand what is possible and make hard choices about what not to build.
Many approaches to discovery have emerged in the Lean and Agile communities. Don Reinertsen’s second-generation lean product development has offered some great answers to making value-based decisions regarding portfolio investments while understanding how to effectively optimize flow through a delivery system.
Come to this session and learn how to use analytical approaches to understanding value and communicating strategy to change what has long been an adversarial relationship between business and IT into a collaborative one where everyone wins.
Learning Outcomes: - How to take an oblique analytical approach to changing organizational culture.
- How to use 2nd Generation Lean Product Development principles and approaches to create the great agile organizations/teams they're seeking.
- How to use probabilistic forecasting to understand the limited capability their organization has in order to visualize the scarcity that necessitates prioritization.
- How to use statistical analysis tools for understanding the size of large initiatives.
- How to frame experimental discovery using the DIBB model (Data, Insights, Beliefs, Bets)
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