Labs Are Drowning in Data. Here’s How to Fix It
Modern biotech, diagnostic and pharma labs generate vast amounts of data daily, from sequencing results to assay readouts. However, manual data entry is still a major challenge, leading to errors, inefficiencies, and data silos. Traditional LIMS and ELN systems were built for record-keeping and compliance but often fail to support custom data models that can integrate experimental, analytical, and instrument-generated data.
To address these issues, Scispot hosted a LinkedIn Live webinar titled Fixing Broken Lab Databases: How Scientists Can Build Smarter Data Models. The session, led by Satya Singh, Co-Founder & CPO of Scispot, explored how labs can design flexible custom data models to improve data integration and automation. Here’s a recap of the key insights.
The Problem with Traditional Lab Databases
The webinar started with an important question: Why do biotech companies struggle to connect their data? The problem stems from fragmented lab databases that require scientists to manually combine experiments, samples, instruments, and plate data.
Some of the biggest challenges include:
- Rigid registries: Labs often start with spreadsheets and move to an ELN or LIMS. But as workflows grow, these systems become too rigid, making it hard to adapt. For example, a company running cell-based assays may struggle to link compound screening results with microscopy images, forcing researchers to rely on external scripts.
- Complicated APIs: Many legacy LIMS systems use nested APIs, which slow down data retrieval. Scientists may need multiple API calls just to pull sample, plate, and results data, delaying high-throughput screening experiments.
- Disconnected instruments: Instruments generate large amounts of data, but without organized data marts, it's difficult to standardize and analyze results. In proteomics, for instance, mass spectrometry results often remain separate from metadata on sample preparation, making comparisons difficult.
The Solution: A Smarter Way to Structure Lab Data
Satya explained that fixing broken lab databases starts with rethinking data structure. Instead of relying on manual data entry and custom integrations, labs should implement flat API structures and intelligent data dictionaries that automatically connect datasets.
Here’s how Scispot is solving these challenges:
- Prebuilt Templates for Data Models: Instead of forcing scientists to build registries from scratch, Scispot provides templates for workflows like qPCR, ELISA, and Next-Generation Sequencing (NGS).
- Automated Data Linkage: With built-in VLOOKUP columns, sample lineage and experiment results are automatically connected, simplifying cross-experiment tracking.
- Flat API Structure: Unlike traditional LIMS and ELN systems, Scispot removes nested API calls, making data retrieval easy. This is especially useful in regulated environments where audit trails must be maintained across datasets.
Satya demonstrated this using a real-world example: running an enzyme activity assay. In traditional LIMS setups, this would require multiple SQL joins and manual API queries. With Scispot's Lab Sheets, all assay data, instrument reads, and metadata are automatically linked—no extra coding needed.
Moving Beyond Spreadsheets and Legacy Systems
A key takeaway was how Scispot streamlines lab automation. Instead of relying on manual data entry, the system can:
- Identify molecular weight, batch IDs, and reaction conditions
- Link assay results to sample metadata
- Spot inconsistencies and suggest corrections instantly
The webinar also showed how Python SDKs help automate sample creation and plating for liquid handlers like Tecan. With just a few lines of code, Scispot retrieves and organizes plating data, eliminating manual tracking.
The Future of Smarter Lab Databases
Biotech labs are evolving fast, and outdated LIMS and ELN systems can’t keep up. Scientists need smarter data models that integrate seamlessly without the complexity of manual data entry and nested API queries.
Scispot is making this possible by enabling:
- Custom Data Models tailored to specific lab workflows
- Automated Data Marts for structured and scalable data storage
- Easy connectivity between samples, assays, and results
Missed the event? You can still learn how Scispot is transforming lab data management by booking a demo today.
Watch the Webinar Recording
If you weren’t able to attend, you can watch the full recording on Youtube:
Let’s build smarter data models together!
.gif)
