We cover the key takeaways from our recent webinar exploring how dispersed scientific data can easily be accessed, enriched, and harmonized for analytics with the Tetra Scientific Data CloudTM. Featured case studies include high-throughput screening, collaboration with contract research organizations (CROs), batch release, and instrument utilization.
The challenge: Manual data handling is grossly inefficient for biopharma labs
Scientific data is largely inaccessible in biopharma organizations—locked in data silos and proprietary formats. Efforts to extract and organize this data remain heavily manual. For example, scientists still transfer files off instruments with thumb drives and process complex data sets in Excel spreadsheets. Around half their time is spent on manual data handling.
“We need the freedom to do science.”
— Scientist from a global biopharma
This time-consuming and error-prone process leaves the data at an analytical dead end, unfit for higher-value activities like artificial intelligence (AI) and machine learning (ML). In short, the status quo wastes massive resources to extract only a fraction of the data’s value.
The solution: Automatically centralize and harmonize data in the cloud
TetraScience developed the industry's only scientific data cloud to automatically collect, centralize, enrich, and engineer data from the lab. It frees up considerable time for scientists while providing the foundation for advanced analytical tools and AI/ML. The approach dramatically outperforms manual workflows in terms of speed and data quality.
The Tetra Scientific Data Cloud goes beyond mere archiving by harmonizing the data and enriching it with metadata and scientific context. The result is a cloud-based, centralized store of searchable data that is prepared for analysis and comparison. This helps companies quickly move up the scientific data pyramid, creating value from their data and turning it into actionable insights.
The result: Faster and better scientific outcomes
From discovery to manufacturing, the Tetra Scientific Data Cloud has accelerated and improved scientific outcomes in biopharma. Here are four use cases that show the impact.
1. High-throughput screening
- 1 hour per run saved for processing data
- 4X more compounds screened per week
"The transition to automated, cloud-based workflows has built efficiencies across multiple levels of Prelude’s Discovery organization and eliminated scalability barriers."
– William Gowen-MacDonald, Senior Research Scientist, Prelude Therapeutics
2. Collaboration with CROs
- 76 hours per week eliminated for preparing data
- 10 days cut down to 10 minutes for data prep and analysis
“Data cleansing is time-consuming and expensive. Some scientists spent a majority of their time manually curating and transforming data.”
– Customer from a top 20 biopharma
3. Batch release
- 8 hours weekly saved in reporting
- 2+ hours/week/scientist eliminated for file transfer
- $30,000 per HPLC saved for consumables
"We want to learn from existing data to 'predict' failures before they occur."
– Customer from a top 15 global biopharma
4. Instrument utilization
- Increased lab productivity and efficiency
- Reduced costs through better CapEx decisions and planned downtime
"With insights into day-to-day usage of our fleet of scientific instruments, we are able to increase our throughput without increasing the lab footprint."
– Customer from an American biotech company
The takeaway: Transformative insights
The Tetra Scientific Data Cloud replaces slow, unreliable processes and disparate data with highly automated workflows. It leverages a scalable, cloud-based infrastructure to produce liquid, compliant, harmonized, and actionable data. The upshot is higher-value data, delivered much faster.
Scientists and data scientists can redirect time spent on manual tasks to more valuable work. Data freed from isolation—harmonized and centralized in the cloud—enables the usage of AI and ML for transformative insights throughout drug development.
Go deeper and watch the entire webinar on demand.