Abstract: Agile for data-focused teams is gaining momentum. Data industry thought leaders offer prescriptive practices such as specific data-management tools or evolutionary data modeling techniques, and others don’t believe these things are as important as simple Agile principles and practices. Some data teams successfully apply pure Scrum, Kanban or XP; others find combinations of or adaptations to these frameworks work better for them. Some create brand new “Agile” methodologies, while others simply give up and go back to their traditional methods.
Given the wide variations we face in work that can be simple, chaotic, complex and/or complicated, it’s no wonder we struggle to adapt to agile principles and practices while keeping the wheels on our bus (pun intended).
Some of the questions we will consider in this session include, but are not limited to:
• What problems are we trying to solve by becoming more Agile in data-focused work? What problems get created in the process?
• Is Agile really applicable to data work, or is it not worth the effort in the end? Is it better to do things the way we’ve always done them, and get the same results we’ve always achieved? Is predicable underperformance better than unpredictable success?
• What’s really different when we are working with data instead of mobile, workflow, and web technologies? Does Agile work the same way with data-focused efforts, or are there important differences?
• Is team culture more important than technical practices, or are certain technical practices baseline enablers for agility?
• Where can the data world contribute to thought leadership for Agile endeavors?
Bring your own experience and ideas on increasing agility in data-based products; what has worked well and what hasn’t. What you think might work well for a data-focused team to become more Agile, and what you know won’t. Engage with others who care about the future of data work as we learn together. Many of us will continue to explore these ideas beyond this session and after this conference!
Learning Outcomes: