Do I focus on optimizing cost for our data systems or providing higher availability? Do we prioritize the productivity of our ML engineers or our data engineer cohort? Do we reduce our tech debt and toil, or provide user facing value?
Choosing one invariably implies not choosing another at that point in time. And both time and our engineer bandwidth are finite resources. The opportunity cost, therefore, of going down a potentially wrong path, can be significantly high. And consequently, the impact we collectively drive for our users can be severely diluted.
Hence, driving precision in what we are shipping, and why, becomes crucial in building trust and credibility with our users, driving value for the business, and most importantly, leading a high performing team!
In this talk, we’ll cover:
Why does Prioritization matter & why is it hard?
An ideal backdrop of a prioritization conversation; an established Product mindset, Tech Strategy, and related roadmap and OKRs
The three phases of a Prioritization Conversation - Discovery, Evaluation & Decision - and an effective framework to navigate each phase
Some Dos and don’ts on how to navigate these conversations effectively
As EMs, and leaders, having effective prioritization conversations is crucial in driving focus for our teams, shared direction for our organizations and most importantly, optimal business outcomes for our users.
Smruti Patel leads the Data Platform engineering organization at Stripe. Her group builds Stripe's user-facing Search API, and hosts the underlying data warehousing, the BigData, asynchronous & stream processing infrastructure for Stripe’s mission-critical business, while ensuring data security, reliability and cost efficiency. Prior to that, she led multiple teams at VMware, including backups and disaster recovery. Her interests include mentoring and coaching, hiking with her boys, and traveling.