From 462d3577292b94f4c28b0206eb8749d403d64cdd Mon Sep 17 00:00:00 2001 From: Pelayo Arbues Date: Wed, 15 Jan 2025 12:53:05 +0100 Subject: [PATCH] vault backup: 2025-01-15 12:53:05 --- content/notes/Pride of my team.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/notes/Pride of my team.md b/content/notes/Pride of my team.md index 854222d87a0a8..b84b8531ccab4 100644 --- a/content/notes/Pride of my team.md +++ b/content/notes/Pride of my team.md @@ -7,9 +7,9 @@ tags: --- Lately, I’ve been feeling extremely proud of my team. Of course, I’ve always been proud and happy to spend my workdays with such talented people, but recently, I’ve found myself reflecting on this more deeply. The Data Science and AI initiative is also growing in its impact on the company, a journey that has been far from easy. As is often the case when companies embrace change and introduce new roles or functions, the path has been filled with challenges. -Every team member has faced some degree of frustration in their projects. I’m talking about the usual hurdles a Data Science team might encounter: lack of stakeholder buy-in, [understaffed projects](Reference-System/_Sources/Data/Strategy/Team%20Structure/Data%20&%20AI%20team%20structure%20Case%20studies.md), slow engineering integrations, lack of high-quality data, [underinvestment in data governance](Reference-System/_Sources/Data/Strategy/Governance/How%20to%20make%20data%20governance%20a%20team%20sport.md), a flood of [ad-hoc requests](Reference-System/_Sources/00.%20Unread/Growing%20data%20teams%20from%20reactive%20to%20influential.md), outdated systems for managing data, and constant shifts in priorities. Sound familiar? If you’re a data leader reading this, these challenges probably sound all too familiar. But at the micro level, when you’re deep in the mud, I know firsthand how overwhelming it can feel. +In the past every team member has faced some degree of frustration in their careers. I’m talking about the usual hurdles a Data Science team might encounter: lack of stakeholder buy-in, understaffed projects, slow engineering integrations, lack of high-quality data, underinvestment in data governance, outdated systems for managing data, and constant shifts in priorities. Sound familiar? If you’re a data leader reading this, these challenges probably sound all too familiar. But at the micro level, when you’re deep in the mud, I know firsthand how overwhelming it can feel. -Despite these struggles, in 2024, things started rolling in the right direction. It’s been incredibly rewarding to see how well we’re now aligning with the company culture. Even more satisfying is watching us build mutual trust with other teams and stakeholders, a bidirectional trust that’s driving us to new heights in terms of results. But what makes me happiest of all is seeing how resilient and strong my team has become. +Despite these typical struggles, in 2024, things started rolling in the right direction. It’s been incredibly rewarding to see how well we’re now aligning with the company culture. Even more satisfying is watching us build mutual trust with other teams and stakeholders, a bidirectional trust that’s driving us to new heights in terms of results. But what makes me happiest of all is seeing how resilient and strong my team has become. New hires might not fully appreciate this transformation because they’re stepping into a greener landscape, they don’t have the context of just how tough those early days were. Yet for those who have been here a while, or even those who’ve moved on, it’s gratifying to witness how far we’ve come over these past four years.