When I first took on the challenge of modernizing our data management, our team relied heavily on Power BI’s semantic models. While this setup worked, it wasn’t sustainable. Data was scattered across our ERP, spreadsheets, and various SaaS platforms, and we struggled with long refresh times, manual data maintenance, and limited collaboration. Moving to a cloud data warehouse—specifically Snowflake—became a necessity. Here’s how we made the transition, the lessons we learned, and the impact it had on our business.
The Problem: A Fragile and Slow Data Infrastructure
Before Snowflake, our data management was spread out. Our best tool was Power BI, where we loaded financials and transactional data from our ERP via OData and other business data through shared spreadsheets. We then established relationships between tables directly in Power BI.
This approach had major drawbacks:
- Manual Maintenance: Someone had to constantly update and clean the shared spreadsheets while ensuring column names remained consistent.
- Slow Performance: Large data volumes and complex transformations caused Power BI refresh times to exceed an hour. Making even minor adjustments required an hour-long wait to test changes.
- Collaboration Issues: Analysts had to take turns downloading, modifying, and re-uploading Power BI files, which slowed down teamwork.
Why Snowflake?
The long refresh times were frustrating, but the real turning point was the growing demand for a Customer 360 view. Our existing setup couldn’t handle it—we needed a system where analysts could access all SaaS data in one place. Additionally, we wanted to leverage AI-driven insights, which required a more scalable and efficient data platform.
Snowflake stood out because:
- Minimal Maintenance: It didn’t require a full-time data engineer, making it ideal for our small team. I manage Snowflake alongside other responsibilities, and it doesn’t demand constant oversight.
- Scalability: Snowflake’s elastic compute meant we could scale up or down as needed, avoiding unnecessary costs.
- Seamless Power BI Integration: Given our heavy reliance on Power BI, Snowflake’s connectors made it an easy choice.
Implementation: Challenges and Wins
Convincing Stakeholders
Proving Snowflake’s value was straightforward. We conducted a POC comparing Databricks vs. Snowflake, and all analysts found Snowflake to be the more intuitive tool. That consensus made getting buy-in easy.
Biggest Challenge: Data Cleanup
Once we ingested all our data through an ETL tool, we were suddenly dealing with 100x more data than before. This required an extensive effort in metadata management, data transformation, and pre-processing to ensure our production views were clean and usable.
User Adoption: An Easy Sell
Once we demonstrated the types of new analyses we could run with Snowflake, user adoption happened naturally. Stakeholders were eager to tap into the richer insights.
The Impact: A Game-Changer for Our Team
The shift to Snowflake transformed our data workflows:
- Power BI Reports Load Faster: Our most-used Revenue report went from a 1.5-hour refresh time to just 5 minutes.
- New Insights, Faster: We built our first-ever Customer Service Power BI report in record time.
- Customer 360 Became Reality: We successfully modeled our Customer 360 view, giving the business a unified look at customer behavior.
- AI-Powered Decision Making: Snowflake now powers our churn analysis and demand forecasting models.
Lessons Learned & Advice
Looking back, my biggest lesson is that I should have started with just one use case. By tackling too many objectives at once, we lost some early momentum. If I could do it again, I’d focus on one key win, deliver that success, then move on to the next.
For anyone considering a similar transition, my advice is: Start small, deliver value quickly, and build from there.
What’s Next? AI Enablement
With our data warehouse in place, the next step is AI enablement at scale. We’re currently working on our Snowflake semantic model to leverage Snowflake Cortex (formerly Snowflake Analyst) for more advanced, AI-powered insights.
The journey to Snowflake wasn’t just a tech upgrade—it was a transformation in how we think about, access, and use data. And we’re only getting started.
Brent Claypool 