Turning Data Into an Engine for Innovation: Why Contextualization and Orchestration Matter More Than Ever in Life Sciences

by Vasu Rangadass, Ph.D. and Alfredo Coviello | posted on August 05, 2025

Walk into any biopharma or diagnostics organization today, and you will find data generated at nearly every step of the process: from sequencers and bioreactors to chromatography systems, laboratory notebooks, LIMS, MES, ERP, and QMS. This data arrives in many forms: from instruments, chemometrics, bioinformatics, scientific calculations, spreadsheets, PDFs, and manual entries. For scientists, engineers, and manufacturing leads, the challenge is not the lack of data, but the complexity of turning scattered records into useful information that supports decisions, ensures compliance, and advances science.

This gap is now widely recognized. According to Gartner’s “2025 Hype Cycle for Artificial Intelligence¹,” 57 percent of organizations say their data is not “AI-ready.” The most common reasons are fragmented sources and missing context. Gartner warns that without a strong foundation for data quality, integration, and contextualization, organizations risk missing both operational targets and strategic goals.

A New Playbook: Orchestrate, Contextualize, and Make Data Work

“At L7, we focus on connecting every point where data is produced and used,” says Vasu Rangadass, Ph.D., Founder and Chief Strategy Officer at L7 Informatics. “This is not just a technology issue; it is a business necessity.”

For most organizations, data contextualization across these point sources of truth is still a daily pain. Teams pull CSV files from instruments, copy values from paper forms, and try to reconcile sample IDs across disconnected systems. L7 takes a different approach. L7’s scientific orchestration capability starts by capturing data at the source, whether from a bioreactor’s digital output, a scientist’s electronic notebook entry, or an automated workflow. The next step is to harmonize and validate this information so that it matches consistent ontologies, formats, and business rules, with automated checks for integrity. Finally, the orchestration engine links each data point to its process, experiment, or business outcome, so that everything from sample genealogy to experimental and process outcomes can be contextualised at the source into knowledge graphs, traced and understood.

This approach changes the daily work of research, development, manufacturing, and compliance teams:

• Instead of exporting and merging files by hand, teams receive unified datasets ready for reporting.
  
• Each result is tied to its experimental conditions, process stage, and operator, providing traceability and context.
  
• AI and analytics tools can then use this harmonized information to detect patterns, surface trends, and generate recommendations.
• GenAI applications can leverage contextualized knowledge graph data to enhance LLM performance.

The ROI: Measurable Gains Across Operations

The value of orchestrated, contextualized data is now being measured across the industry. According to a recent McKinsey publication², organizations using GenAI and automation in life sciences operations are seeing tangible improvements:

• “After implementing a gen AI copilot tool for maintenance support, one biopharma manufacturing team achieved 5 percent reductions in breakdown time, speed losses, and minor stoppages and a 30 percent reduction in execution time.”
  
• “It also experienced a 40 to 50 percent workload reduction for corrective maintenance.”
• “In pilots, companies have achieved reductions of up to 90 percent in manual effort for particular tasks (for example, medical coding or data review).”

L7 customers have observed similar progress. For example, a research institute cut time spent preparing submission documents by over 80 percent using the L7|ESP platform. A biomanufacturing customer improved operational efficiency by 50 percent and made faster, more informed decisions across its production workflows.

Beyond efficiency, organizations are using better data to strengthen experimental insights, process development, improve product quality, and demonstrate compliance. Continuous process verification, robust trend analysis, and predictive insights are becoming standard practice, not just aspirations.

L7|INTELLIGENCE® as the Business Intelligence Layer

L7|INTELLIGENCE is the business intelligence layer within L7|ESP. It works by regularly creating a query-optimized view of all operational records, such as experimental data, batch release data, QC results data, stability analysis data, and analytical data. L7|INTELLIGENCE transforms the raw transactional tables into structured, domain-specific views and data products. For example, a join between batch production data with QC sample results and operator annotations produces a comprehensive report for each batch run, so that Quality can use integrated views of information for regulatory compliance and submissions.

To make reporting scalable and reliable, L7|INTELLIGENCE uses workflows that move and structure data between the L7|ESP operational database and the reporting layer. For teams using cloud data warehouses like Snowflake, L7|INTELLIGENCE provides direct integration, allowing users to run advanced queries or analytics without manual data exports or duplication.

Because the data is harmonized at source and available in near real-time, customer teams can produce data products and reports for many use cases, such as:

• Process Development: Analyzing experimental results and scaling up processes with automated statistical summaries.
  
• Analytical Method Development: Monitoring method validation and performance over time.
  
• Stability Management: Aggregating and trending stability study outcomes for shelf-life determination and compliance.
  
• Continuous Process Verification (CPV): Detecting trends in critical process parameters and providing early warnings for deviations.
  
• Clinical and Commercial Manufacturing: Combining batch records and equipment data for analysis and investigation.
• Environmental and Method Lifecycle Monitoring: Centralizing monitoring data for quality and clinical operations.

This harmonized layer also acts as the gateway to AI enablement of the business, including the application of large language models (LLMs) and Machine Learning models to support both operational reporting and predictive modeling of chemical and biological processes. Reports generated by L7|INTELLIGENCE help scientists, engineers, and quality teams focus on high-impact questions, such as: 

• How to optimize the chemical reactions? 
• How to increase the cell count in the bioreactor expansion process?
• Which batches show early signs of process drift? 
• How does stability compare across production sites? 
• Where can manual review be replaced with automation?

As Alfredo Coviello, Director of Product at L7 Informatics, explains: “With L7|INTELLIGENCE, our goal is to make high-quality analytics available in near real-time to every business function that needs it. By combining the powerful scientific orchestration and data contextualization into knowledge graphs and integration with Snowflake (or custom data warehouses), we deliver contextualized data and insight at the speed business teams require for rapid decision-making and continuous process improvement.”

The Bottom Line: Orchestration and Contextualization are the Foundation for AI in Life Sciences

AI (and all the business value and ROI) will only be as strong as the data foundation underneath. Without orchestrated and contextualized information and analytics, AI models cannot deliver their full promise.
As Vasu Rangadass puts it: “The organizations that succeed in the next era of life sciences will treat their data as a strategic asset, making sure it is automatically contextualized at the source of data creation, and ready to drive decisions and AI models. It is time to put manual contextualization using aging toolsets behind us so we can get true ROI from all the AI IT expenditures.”

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Sources: 

¹ Gartner: “The 2025 Hype Cycle for Artificial Intelligence Goes Beyond GenAI,” Haritha Khandabattu – July 8, 2025

² McKinsey: “Gen AI: A game changer for biopharma operations,” Boyd Spencer, Parag Patel, and Vivek Arora, with Krithiknath Tirupapuliyur and Raj Rajendran – January 28, 2025

ABOUT THE AUTHORS

Vasu Rangadass, Founder and Strategy Officer

Vasu Rangadass, Ph.D., is the Founder and Strategy Officer at L7 Informatics, Inc., a leader in life sciences workflow and data management. Previously, Dr. Rangadass was the Chief Strategy Officer at NantHealth, following its acquisition of Net.Orange, the company he founded, to provide an enterprise-wide platform to simplify and optimize care delivery processes in health systems. Before Net.Orange, Vasu was the first employee of i2 Technologies (currently Blue Yonder), which later grew to be a global company that revolutionized the supply chain market through innovative approaches based on the principles of Six-Sigma, operations research, and process optimization.

Alfredo Coviello, Director of Product

Alfredo Coviello is a seasoned technology product leader with a strong emphasis on data-driven AI and ML solutions. Bringing extensive experience across various industries, including Life Sciences, since joining L7, he has led a cross-functional team to design, build, and deliver L7|ESP’s leading-edge functionalities. He has a track record of successfully delivering business-critical applications and is passionate about driving the creation of pioneering solutions at the forefront of technological innovation.