Breaking Down Data Silos with Centralized Data Management

Biotech companies typically operate with small teams using file-based mechanisms for managing their critical reagents and entities. They tend to use spreadsheets for their data. This practice can cause significant data management issues over time. Auditing of changes to data is a particular challenge in that context, as is record retention, such as version tracking and data traceability.

Most scientists follow a five-step data-management process during the life cycle of an experiment: plan, execute, analyze, process and report. Through this process, scientists interact with a variety of software tools. Some of these are highly valuable, automated solutions, while many are not. Generally, for every automated process, there is at least one manual process to copy, paste, transform and load data. Each time this is done there is a risk that a mistake is made, which can lead to inaccurate data reporting. To mitigate this risk, laboratories conduct a manual review of this process to ensure data quality.

To address the data management challenge, labs often deploy either an SDMS or LIMS system. However, neither of these provide context sensitivity. Bringing the entire experimental process into a single, integrated scientific workflow provides several significant advantages:

• Experimental work can be prioritized by project and team leaders. This reduces the need for meetings, emails and other ad hoc ways of communicating status. Tracking who prioritized which items and when they did it allows for accurate reporting of key project metrics.
• Integrated workflows enable real-time feedback to the user. On-screen notifications provide the scientist with information about the availability of equipment, sample preparation and instrument data. This streamlining greatly increases individual productivity. When these workflows are fully integrated, stepwise execution of a process can also be enforced without separate documentation or outside monitoring. For example, results cannot be entered for a sample until the sample preparation is properly documented and completed.
• Search results can also be informed by the context of experiments. The review and approval of experimental results being a built-in part of the experimental process makes it trivial to screen out results that have not yet been finalized, for example. But the user can also optionally include these data if they are of interest. In loosely integrated systems these simple kinds of queries can return complex reports that are not only difficult to decipher, but also hinder the user’s ability to navigate across the data sets with ease.
• As we slowly transition back to a more normal workday, and while companies take precautions to prevent further outbreaks, intuitive workflow management tools will be relied upon to enable team leaders to delegate tasks and workloads in an efficient and effective manner.

WEDNESDAY, July 15, 2020

• Discussion of data management challenges in the lab, including specific examples such as outsourcing to CROs
• Review of various software tools used in labs, LIMS, Excel, instrument software etc
• Importance of context sensitivity. Benefits of deploying a single integrated workflow
• Understand the benefit of relating all research information together in a single view to improve collaboration, productivity and decision support
• Learn how to manage essential lab employees remotely