| Lab Automation: Which Processes Pay, and Which Ones Do Not  Lab automation pays when a task is high volume, low judgement and methodologically stable. When any condition is missing, exceptions can erase the savings. That’s why candidate selection matters more than technology choice. Labs often prioritize tasks by how annoying they are—a poor predictor of automation’s return.[Read More]
Sourcing the Right Analytics Talent for Modern Drug Discovery: Data Science, Bioinformatics, or Cheminformatics?  Drug discovery has evolved from an empirical, wet-lab discipline into a data-driven enterprise powered by AI, genomics, CRISPR, and multi-omics. As life sciences harness massive datasets to accelerate discovery, a key challenge remains: finding analytics talent with the specialized expertise needed to turn data into breakthroughs.[Read More]
Developing a Robust Lab Data Migration Strategy: A Technical How-To Guide for 2026  As FDA’s QMSR became mandatory in 2026, biopharma organizations face growing pressure to modernize and securely migrate laboratory data. This article outlines a regulatory-first framework for validated, AI-ready data migration that protects data integrity, minimizes downtime, supports evolving LDT oversight, and reduces compliance and breach risks.[Read More]
Last call - From Legacy Samples to Paperless Labs  Still relying on paper, spreadsheets, or fragmented systems? Discover how connected digital lab workflows help track every sample, automate data capture, and improve traceability from measurement to reporting. See a real-world example of how paperless processes can boost efficiency and support compliance.[Read More]
Visualizing Compliance: Insights for Canadian Pharma/Biotech  This white paper explores common data visualization techniques for biotechnology and pharmaceutical laboratory settings, with examples of how different data sets can be communicated visually. Designed for stakeholders in Canada, it provides practical ideas for presenting complex laboratory information clearly and enabling faster understanding at a glance.[Read More]
How Does a LIMS Work, and What Makes an Implementation Fail?  A LIMS is easy to buy but difficult to implement. Success depends on decisions made early—often before a contract is signed. A LIMS makes the sample the unit of record, linking every transfer, method, instrument reading and approval in a traceable history. How well it works ultimately depends on four key decisions made before purchase.[Read More]
| 
