Building more sustainable products to improve life for future generations

• 平台即服务 (PaaS) - 数据管理平台

• 产品研发

• 系统集成

Novozymes, a company focused on harnessing the power of enzymes and microbes to solve global challenges, was facing issues with its data management. The company's strain engineering teams were dealing with an increasing volume of data, from hundreds to thousands of samples, which required a more modern digital infrastructure for better connectivity and scalability. The company had many point solutions and regularly brought on new or custom software to address specific scientist needs. Over time, data about plasmids, strains, and assays were spread across multiple systems. Data scientists spent up to 90% of their time trying to reformat data to glean insights. Routine reports were hard to create and even harder to automate. Additionally, scientists had to hunt through Excel sheets, emails, and databases for past results, taking scientists away from science.

Novozymes is an industrial biotech company based in Bagsværd, Denmark. The company is focused on harnessing the power of enzymes and microbes to solve some of the world’s most pressing challenges. From increasing fuel sustainability to making crops more resilient, Novozymes is building products that will improve the lives of future generations. With more than 700 products and 30% of sales coming from new solutions each year, the scale and speed of Novozymes’ scientific innovation is truly unprecedented. The company employs over 6,000 people and is committed to accelerating its pace of innovation to bring more life-changing products to market, faster.

Novozymes decided to centralize its R&D teams on Benchling through a pilot program. When fully rolled out, this program will allow 1,400 scientists to accelerate the discovery and development of life-changing products. Benchling's modern API and data warehouse interfaces allow Novozymes to integrate Benchling to its existing data lake. Benchling will serve as Novozymes’ central source of truth, following FAIR principles. Laboratory scientists can input and access data using Benchling’s intuitive ELN, and data scientists can easily run machine learning models. Benchling for Lab Automation connects liquid handlers and custom applications, so the team can run assays on resulting strains in bulk and automatically link results to gain insights. Benchling’s unified platform and robust data model makes it easy to connect every step of the strain engineering process from Design and Build (strain ID, genotype, etc.) back to the original DNA. That way scientists can save time on work, such as tracking genetic modifications and identifying unique combinations of DNA parts that could improve strain performance.

• Standardized data capture gives scientists consistent, accurate R&D data at scale and enables aggregation for machine learning models
• Scientists now have visibility and actionable insights at their fingertips, saving them time tracking and finding DNA parts for everything from day-to-day experimentation to preparation of IP filings
• Scientists save time with Benchling as their central source of truth — they no longer need to toggle between multiple systems to do their jobs