Refocusing on the Organization: Vital Steps — Tip #20
Recall our journey. In Tip#10, I introduced how to organize a data team within the company. Given the dynamic nature of businesses, I’d like to discuss when it’s essential to revisit and potentially restructure the data team. 🔄
Service-Oriented from the Start 💼
Like many data teams, mine began as a centralized unit. We naturally transitioned into a service-oriented relationship after setting up the ETL pipeline and introducing a BI tool. With the training sessions and showcases of the BI tool, people approached us with requests like: “Can you build XX dashboard 📊 for me so I don’t have to manually prepare the report?”
While catering to these requests and understanding their report structures, we found ourselves swamped. After creating models for various reports, we had an epiphany — 80% of these models were quite similar! 😲 We realized we hadn’t invested enough time in understanding the business.
Synergizing Through Pairing 🤝
A turning point came when a member of the operations team, who showed keen interest in data analysis, created a dashboard on the BI tool even before our training session. After several discussions, he transitioned to our data team as a data analyst, filling a vital business domain gap. This experience led us to pair a data champion from each team with a member from the data team, expecting this close collaboration to amplify synergy. 🚀
🗝️ Keys to Success:
- The paired individuals must work closely.
- Their goal alignment is crucial: achieving team objectives through data-driven insights.
- Collaboration across domains to derive rich insights is a must.
However, not all pairings were smooth. The data maturity level of each data champion varied. Plus, there were distinct learning curves and individual preferences. One data analyst remarked, “I don’t have an interest in marketing; that’s why I’m a data analyst and not a marketer.” 🙃 Point taken!
Embracing Close Collaboration 🌐
Inspired by successful pairings, we started to proactively provide analysis reports, offering fresh perspectives in hopes of stimulating discussions. Initially, our efforts were met with applause but not much engagement. But persistence paid off, and our approach started making waves. 🌊
One such pairing delved deep into collaboration, with the DA sometimes questioning if he was overstepping his boundaries. 😅 But it’s all part of the journey, fostering an environment of respect and transparency.
While we couldn’t replicate this deep collaboration across all teams due to resource constraints, it served as a model for potential endeavors.
Reflections on the Journey 🌌
Changes, as you’ve seen from the narrative above, were evolutionary, not sudden. Different approaches yielded varied results with diverse teams and members. 🌀
Every member of the data team, not just the leaders, should engage in introspection. Reflect on collaborations, set expectations, and analyze what’s working and what isn’t. Minor tweaks can lead to significant shifts. For instance, during our centralized phase, recognizing one team’s high data literacy led us to more in-depth discussions and further evolution. 🌱
Up Next ⏭️
In Tip#9, I emphasized the value the data team brings to the business. I’m excited to revisit this and share some actionable methods. Stay tuned! 🚀
🤩 I’m happy to hear how you handle product development process? what tool do you use? Feel free to reach out to me on LinkedIn Karen Hsieh or Twitter @ijac_wei.
🙋🙋♀️ Welcome to Ask Me Anything.
