Turning Unstructured MongoDB Data into Usable Insights
A fast-growing SaaS company relied on MongoDB to store millions of dynamic survey form submissions. Users designed their own forms, which meant no consistent schema, just deeply nested, inconsistent JSON data. Over time, this freedom became a bottleneck for analytics, reporting, and product scaling.
The Problem:
The unstructured nature of the form submissions created multiple data issues:
No standardized field names or types (formData blobs were inconsistent and unpredictable)
Redundant metadata stored with every submission bloated the database
Aggregating cross-form analytics (e.g., response time benchmarks) was nearly impossible
Exporting to BI tools like Power BI or Looker required fragile, high-maintenance ETL pipelines
Mismatched field types led to data quality issues and delays in insights
The Solution:
DataPeak’s data management engine was deployed to bring structure to chaos:
Dynamic schema extraction: Parsed and normalized metadata from custom form designs
Relational mapping: Transformed JSON blobs into a flattened, analysis-ready model
Automated ETL: Built pipelines that validated, enriched, and loaded form data into PostgreSQL
Self-serve analytics enablement: Enabled direct access for BI tools, no more manual wrangling
The Benefits of Solving This Problem:
🚀 37% reduction in storage volume
⏱ 60% faster ETL pipeline execution
📊 1 unified data layer across all forms
🧠 Instant insights for product and marketing teams
📈 Scalable analytics with confidence and consistency
By turning messy, inconsistent form data into a clean, scalable reporting foundation, the company unlocked the full potential of its survey platform. Business users can now get the insights they need, faster, more reliable, and without constant intervention from data teams. It’s a transformation that proves structure isn’t the enemy of flexibility, it’s what makes flexibility sustainable at scale.
