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As a tech leader, you're all too familiar with the constant juggling act. Multiple projects, teams spread thin, and looming deadlines. It's a battle to prioritize what gets attention and resources vs what gets delayed or cut. 🤺 I know I've struggled with this myself, having to make tough prioritization calls and honestly, I still do everyday!
But what if there was a science to effective prioritization? A way to objectively rank your efforts to maximize impact with constrained resources? That's what I want to explore by analyzing some powerful prioritization frameworks that I leverage:
The Pareto Principle (80/20 Rule)
This timeless concept says that 80% of outcomes come from 20% of inputs. For tech teams, that means identifying and focusing on the 20% of projects or features driving 80% of the value. I strive to hone in on high-impact work rather than getting bogged down in the trivial many. One example I use is limiting major new feature development to only things impacting our biggest customer segment or stakeholder group. I NEED to know the impact and the context to ensure it's worth doing.
RICE Prioritization Model
Developed at Intercom, the RICE model provides a systematic scoring approach that I've adopted. You rate each initiative by its Reach, Impact, Confidence, and Effort with weighted scores. This gives me an objective ranking illuminating the highest priorities. For example, I recently scored down a product integration sprawling our API due to low confidence, making it lower priority.
Eisenhower Matrix
I'm a fan of this method that has you categorize tasks into four buckets based on urgency and importance. Items in the "Important/Urgent" and "Important/Not Urgent" buckets get prioritized accordingly. Unimportant tasks are delegated or eliminated. I use this framework all the time with my direct reports to help them focus on their key priorities.
Automation = Efficiency
Okay, so you've prioritized...now how to maximize output? I'm a big believer in automation being key here. My teams have invested in automated testing, CI/CD pipelines, self-service data platforms, and more. All these reduce overhead so we can pour resources into new high priorities (there's always NEW!)
By leveraging science and data-based prioritization methods, tech leaders can bring order to roadmap chaos. You'll have full transparency into your highest impact efforts
BUT also, you can easily communicate this logic to your non-technical business partners and teams.
And you'll be able to efficiently do more with your limited resources. It's all about working smarter, not harder!
✍️ Did I get this wrong or right?
♻️ Reshare if this applies to you!
📞 Reach out if you need a coach who can help you implement these frameworks so that you be more more effective.
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