With the progress of new scientific techniques and high-throughput modalities, biotech R&D organizations are generating larger volumes of data with increased complexity. Collecting, standardizing, and analyzing this data across teams, projects and processes is crucial to accelerating research timelines and informing key decisions. However, legacy software hasn’t adapted to this new environment, resulting in rigid data silos and inconsistent data models. As a result, scientists and R&D organizations struggle to extract insights from their data, and key decisions are often based on incomplete information.
To better understand the nature of these challenges and identify the core questions that R&D organizations are struggling to answer with their current systems we consolidated five themes in using rigid systems, as well as suggestions for how you can address them in your own R&D organization.
Challenge: To get the complete picture of a sample, a wide range of interrelated entities that produce specific types of data need to be captured and interlinked. Legacy software wasn’t built to model the complexity of biological entities.
Solution? Use tools that are built with biology in mind to capture and interlink relevant types of data.
Challenge: Spreadsheets, emails and legacy R&D software don’t provide an easily accessible experiential history. Data is often lost, inaccurate, or duplicated.
Solution? Connect assay data with data on processes and consumable usage. Scientist and decisions makers will be able to understand the steps that led to a result, making their processes transparent and repeatable.
Challenge: With legacy systems data is often housed in silos making it difficult to communicate and collaborate with team members. Scientists have to manually track down sample data or repeat experiments.
Challenge: Life science R&D processes are inherently complex and iterative. It’s difficult for organizations to identify bottlenecks and optimize processes without data on the processes themselves.
Solution? Leverage your data management system to unify inventory tracking with project management and gather data on process performance (ex. Process runtime, reagents used) and results. This will give clear insights into project processes and ensure rapid iteration.
Challenge: Life sciences R&D forecasting is largely dependent on the company’s own historical data. Incomplete, siloed data storage threatens forecasting accuracy.
Solution? Connect project data with reagent usage data. With insight both into project performance and reagent usage, you’ll be able to innovate more efficiently in the future.
While life science R&D is developing incredible solutions to global problems this groundbreaking work is often being done with outdated tools that weren’t designed with the complexity of life sciences in mind. If you’re struggling with these challenges, you may already know that the tools you’re using aren’t innovative enough to keep up.