Stake is on a mission to make real estate investing accessible to everyone. Their app lets anyone in the world invest in rental properties in Dubai through fractional ownership. Users just have to complete a quick KYC check to start investing income-generating real estate without massive down payments and complicated paperwork.
Behind the scenes, though, things weren’t always so clear-cut. When Abdel joined as the company’s first analytics hire in 2022, Stake’s data team was not set up for scale.
“Stake is full of curious minds constantly looking to understand the business better, but the flood of data requests became impossible to keep up with. That’s when it became clear we needed a scalable, self-serve approach.”
- Abdelmounim Boufous, Senior Data Analyst
Like most startups, Stake began with a single data source: MySQL. They plugged it into Looker Studio and built dashboards directly on top of raw tables. But as Stake grew from 13 to over 100 employees, their data sources multiplied. The team was using multiple tools, each generating valuable insights, but their analytics setup couldn't keep pace.
Stake's naturally curious culture was creating an unsustainable cycle. What started as healthy data curiosity became a constant stream of requests flooding their Slack channels. The small analytics team was drowning in mundane work, and business users were stuck waiting for answers to basic questions.
The team knew they needed a complete overhaul: a proper data warehouse, better modeling with dbt, and a BI tool that could empower their team to find answers themselves.
When evaluating BI tools, Stake had clear priorities. For starters, they wanted to move away from per-user pricing models that would limit access as they scaled.
“With Looker pricing, we’d be paying a lot for self-serve because it goes up with how many users we have. It's not like Lightdash, where you pay a fixed price and you can invite as many people as you want."
- Abdelmounim Boufous, Senior Data Analyst
But pricing wasn't the only consideration. They also needed true self-service capabilities. And after a demo call with Lightdash, the decision became clear.
"It was clear that Lightdash would help us scale self-serve. It’s easy for business users to browse models and get the answers they want by dragging dimensions and metrics and adding filters. They could also double-click and drill down on each metric. That was exactly what we needed."
- Abdelmounim Boufous, Senior Data Analyst
Rather than attempting a risky migration, Stake took a measured three-month approach to transitioning from Looker Studio to Lightdash.
Their strategy was well-structured:
While Stake successfully recreated all their existing dashboards, the most significant change was cultural. Stake moved from a centralized to a decentralized data culture by creating power users in marketing, operations, and product teams. These data champions could fulfill requests from their colleagues, creating a network of self-sufficient analysts across the organization.
Here are some of the features that made this transformation possible:
1. Drill into: Instead of creating new requests every time they want to dig deeper, users can simply double-click and explore.
2. Multi-model joins: Users can join multiple models and break down data by dimensions from different models.
3. View underlying data: Users can understand what's behind the numbers and build trust in the metrics through one click.
4. Intuitive exploration: Business users can navigate data as naturally as browsing a website.
The impact was immediate and measurable. Stake saw an 80% reduction in data requests, but the transformation went far beyond fewer Slack messages.
"Now there's this luxury of teams showing up to meeting rooms, pulling up Lightdash, and answering questions on the spot. Questions that used to take hours or days to get answers for."
- Abdelmounim Boufous, Senior Data Analyst
The company shifted to real-time decision making while maintaining data quality. Because everything was built on top of their dbt models, every metric remained consistent and trustworthy.
Perhaps most importantly, self-service analytics freed up the data team to focus on higher-value work while serving more stakeholders than ever before.
"Self-service is actually more useful for data analysts than business users. Now I don't have to write lots of queries for things that can be grabbed instantly. As the most powerful user of the tool, I can customize things exactly how I want them."
- Abdelmounim Boufous, Senior Data Analyst
Stake's successful migration to Lightdash created the foundation for future innovation. The platform's ability to join multiple models has opened new analytical possibilities, and the team continues to explore advanced features.
"We went from having data requests fill our Slack channels to having teams naturally turn to data before making decisions.”
- Abdelmounim Boufous, Senior Data Analyst
For Stake, Lightdash democratized data access while maintaining quality and governance, creating the foundation for truly data-driven decision making across their growing organization.
Let's talk about how Lightdash can help your team move faster, cleaner, and smarter. Just book a demo with a member of our team to get started.
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