Turning Unstructured MongoDB Data into Usable Insights

Written By Veronica Skinner
 

A business management software company hit a major data roadblock. Their flexible survey platform, which let users create completely custom forms, had an unintended side effect: millions of inconsistent, deeply nested JSON documents in MongoDB.

As the volume grew, reporting slowed to a crawl, storage costs spiked, and data pipelines cracked under pressure. Teams across the company felt the pain.

To turn things around, the company brought in DataPeak. Using smart schema detection, automatic cleanup, and reliable ETL pipelines, they reworked the entire data flow from the ground up. What used to be a tangled, brittle mess became fast, structured, and easy to work with. This gave the teams the clarity and speed they needed.

The results?

 

Read the full story of how they did it and see what DataPeak could do for your team.

  • 37% reduction in storage costs

  • 60% faster ETL execution

  • 1 unified data layer across all forms

  • Instant reporting & scalable analytics

Disclaimer: Results described in this case study are specific to the featured client’s experience. Actual outcomes may vary based on your business context and implementation.

Veronica Skinner
Previous

Enhancing CRM Accuracy & Lead Conversion Through Real-Time Data Enrichment
Next

Optimizing Material Procurement Based on Order Demand