Do you wonder how Big Tech companies build and deliver Digital products? What framework, if any do companies like Apple, Google, Facebook, Amazon, Netflix, Uber use to build highly scalable products? Do they have the same challenges with Product Owner role, Scrum Master vs Project Manager role, Kanban vs Scrum debate, user story slicing and infamous story point estimates? Or they have figured it all out and that is why they are at an advanced stage of their agility journey? If you are interested to know more about their flavour of agile SDLC, please join me in this learning journey on what we can extract from their core practices.

The Core theme of their framework is built on 3 disciplines

1. Software Engineering
2. Product Management
3. Technical Program Management

We will deep dive on each discipline and highlight some of the principles and differentiating factors so that we can understand and apply them for our businesses.

We have seen so many Agile methodologies and scaling frameworks in the industry but even after rigorously implementing them, very few organizations are getting real benefits. True agile organizations are not big followers of these out of the box frameworks or methodologies rather we observed four key trends that has emerged to be the game changers:

• Hyper-Decentralization
• Data Intensive Decisions
• Change as a non-event
• Next-Gen Cross functionality

In this session, we will discuss the key emerging trends that are driving agility and how organizations can embrace them without the need for any framework or methodology.

Every Business is betting big on Data Science and making it an essential part of the enterprise's decision making mechanism whether it is marketing, risk analysis, fraud prevention, cyber threat management or even investment choices.

However, the surprising trend we observe is that, organizations are taking old school sequential and silo-ed approach to organize and deliver data driven business decisions despite Data mining being very iterative and non-linear in nature.

As a result, the time between a derived insight and the organization’s time to action can create large gaps that breed a paradigm of striking oil without knowing how to monetize it. That causes the data driven insights to become useless; as it is too late to execute, or need to rebuild the models to find relevant insights. However, with agile principles that favors incremental changes driven by small cross-functional empowered teams; data science can truly be the driver for building insight driven organizations.

In this session, we will discuss the need for enterprises to embrace agile principles for their data analytics work and share the story of how we transformed a Data Analytics endeavor at a Big Canadian organization.