How Super Technologies Turned 600 Quality Checks Into Data Confidence
Super Technologies: Transforming Data Quality at Scale
“Since launch, the overall organisation grew significantly while the number of questions about 'what does this table do', 'who owns this figure', or 'what is this column used for' dropped significantly.”
Customer Overview
- CUSTOMER: Super Technologies
- INDUSTRY: Sports betting and gaming technology
- SIZE: 5,000+ employees
- REGIONS: Brazil, Belgium, Greece, Poland, Romania, Serbia
- SOLUTION: DataHub Cloud
- USE CASE: Data democratisation, Self Service, data quality, conversational discovery
- DATA STACK: DataHub Cloud, Snowflake, Tableau, Airflow, Kafka
- GOALS: Make data findable, trustworthy, and understandable across the full organisation, not just data professionals
The Topline
- Challenge: As the data and technology arm behind one of Europe's fastest-growing sports betting platforms, Super Technologies needed data quality, discovery, and ownership to scale reliably across 800+ professionals.
- Solution: Deployed DataHub Cloud natively on Snowflake across the full organisation, combining 600+ custom SQL assertions running directly against Snowflake tables, automated metadata checks, lineage mapping, and a conversational discovery layer.
- Impact: Caught a live P/L incident before it reached the business, reduced KPI lookup time from an orders-of-magnitude effort to seconds.
The Challenge
What does it take to make data work at the speed of a live sports product?
Super Technologies is the technology and data arm powering Superbet, one of Europe’s fastest-growing sports betting and gaming businesses. With more than 800 professionals across engineering, product development, analytics, and data science, data is embedded in the product itself at Super Technologies. When data quality fails, it does not slow down a report. It compromises the product.
The Solution
How Super Technologies built data quality that scales.
The company deployed DataHub Cloud as part of a phased rollout. The initial rollout tightened company-wide foundations necessary for a broader launch.
Key Components of DataHub Cloud Deployment
- Glossary and ownership
The DataHub Glossary stores and defines KPIs across the organisation with ownership and lineage connected. - Data Lineage
DataHub’s capability maps the movement of data across pipelines and systems. - Data Observability
The Data Platform team wrote more than 600 custom SQL assertions combined with metadata checks for data freshness and volume. - Ask DataHub
This mechanism enables anyone to ask where a figure comes from, who owns it, how fresh it is, or any quality issues.
The Impact
From specialist function to company-wide capability
- A live P/L incident caught and resolved before the business felt it.
- KPI lookup time reduced from minutes to seconds.
- Hundreds of employees now answer data questions without the data team.
- Engineering time shifted from maintenance to product building.
What’s Next
Super Technologies is moving towards building a data quality dimensional model and KPI data mart fed directly from DataHub Cloud metadata, turning from anecdotal data health reporting to systematic measurement.
Super Technologies: before and after DataHub Cloud
| Before DataHub Cloud | With DataHub Cloud | |
|---|---|---|
| Finding KPI definitions and owners | Significant effort | Seconds via DataHub Chrome extension |
| Data quality checks | Homegrown checks | 600+ custom SQL assertions |
| Pipeline failure detection | Limited observability | Immediate detection |
| Inbound questions to the data team | Rising volume | Decreased volume with Ask DataHub |
Download the IDC Business Value study to see how DataHub Cloud customers achieve 91% faster data searches and higher engineering productivity.