A digital data backbone: The key component of a successful instrument data integration

The demand for novel therapeutics is increasing but the use of paper-based or disparate systems for record-keeping cannot serve or keep pace with today’s demanding laboratory environment. Many data management tools and systems create a patchwork ecosystem – adding little to no context to the data, inhibiting organizations from realizing their full potential.

So, it should come as no surprise that the lab informatics market is expected to jump to $13 billion by 2032, up from $4.3 billion in 2021, driven in large part by the need for increased laboratory efficiency through better data management. The results are reduced human error, increased efficiency, time savings and increased revenue.¹ Ultimately, companies want to get their therapies to market faster, and data-driven insights should help speed time to market.

The requirements for data integration are changing

Lab managers have certainly recognized the importance of automation in capturing data from instruments and processes. And while replacing manual transcription improves data integrity and saves time, data integration can play a bigger role in tracking and ensuring data is not only descriptive but also predictive and prescriptive. The objective is to consolidate data from hundreds of instruments, perform real-time analysis and automatically feed insights back into lab processes and operations.

This may sound easier said than done. Integrating instruments can be challenging because labs typically have no control over how data is formatted. Data exchange is the key to integrating instruments with the data management system, but the harder the data is to work with, the more complicated the integration build will be.² Additionally, access to instrument data is critical to streamline workflows and gain the insight needed to drive decision making but the wide array of instruments and applications used in laboratory environments and the lack of standardized interfaces makes this a daunting task.³

IDBS’ Platform Product Leader for Integrations, Craig Williamson, suggests that labs “need a holistic, accessible view of their data and a digital data backbone that can put it to use”. And he submits that this is quite achievable. He highlights that if an instrument reading is identified as an outlier, storing this data point for record-keeping purposes is one thing, but an outlier could have big implications. It could, perhaps, indicate an instrument failure that, unnoticed, could make a dataset unusable. Flagging the outlier early and triggering deeper analysis could avoid costly do-overs.⁴ Each new piece of data that enters a digital data backbone should always be combined with appropriate context so that the precise meaning is unambiguous and aligned with accepted standards for interoperability and reuse.

Current data integration tools and tactics

IDBS partnered with Scitara in February 2021 to provide IDBS Polar users with extended laboratory data connectivity and instrument integration capabilities. IDBS Polar is a platform that securely manages contextualized data through the drug development process in contexts of workflow, integration and insight. Rapidly integrating Polar into your development ecosystem enables automation and curates a process-centric data backbone, which will bring meaning to your data.

Polar workflows and integrations remove risky and inefficient manual processes and deliver high-quality contextualized data that supports decision-making and insights throughout the BioPharmaceutical lifecycle. The combined solution provides integration with instruments, equipment, informatics systems and enterprise platforms, significantly reducing the time and complexity of Polar implementations.

A vision for the future of data integration

Dr Williamson suggests scientists should choose their integration based on configurable parameters. Workflows might involve in-line capture of supporting data and context, the ability to verify and validate data values and business rules, or calculations and normalizations that can run in the background. Integrations should also offer branching workflows capable of acquiring and processing discrete data sets in parallel before consolidation. As process needs and data sources evolve, workflows should be able to evolve and adapt.

He says capturing the full potential of lab instrument integration means that lab leaders must reimagine good data management: success is not simply about data integrity or the mechanics of data transfer. Integrating data from diverse data sources without losing meaning is critical. To find the best solutions, you need to look at all the data being generated in the lab and be creative about how it can help you achieve your goals.

References

1. The Worldwide Laboratory Informatics Industry is Anticipated to Reach $12.6 Billion by 2032: Reduced Human Error and Increased Efficiency is Driving Growth, ResearchANDMarkets, Dec. 13, 2022.
2. How to Overcome the Technical Challenges of Lab Integrations.
3. IDBS, Polar Integrations.
4. Why a Digital Backbone is Necessary and How Companies Can Get There, Craig Williamson, Lab Manager, Oct. 13, 2022.
5. IDBS Integrations.