Creating great products powered by big data can be challenging.
Data science work is often ambiguous, which can make results unpredictable and scheduling almost impossible.
Many of the popular software engineering processes just won’t work for these innovative and ambitious projects. Waterfall falls apart; it just doesn’t make sense to define the product before understanding the limitations of the data and technology. And shoehorning data experiments into tight agile sprints is both difficult and doesn’t necessarily lend itself to discoveries that involve a lot inspiration and perspiration before a light bulb moment.
Even with a working process, few teams collaborate truly effectively. Projects that involve machine learning, algorithm development, or other deeply technical endeavors, are filled with advanced math and complicated terminology, which leaves plenty of teams with communication gaps that prevent the synergy realized when working cohesively.
Thankfully there are solutions to these problems! Based on personal experience, and interviews with many other leaders spearheading big data initiatives, this session aims to distill these lessons into actionable strategies you can use to improve process and communication for your own team.