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Laboratory Technology

The Challenges of "Digital Collaboration"

As one looks to the future of industrial innovation practices, two challenges—externalization and harmonization of incompatible infrastructure—are apparent. 

Andrew Anderson

New Governance Models for Instrument-Derived Laboratory Data Should be Considered

As one looks to the future of industrial innovation practices, two challenges are apparent. The first is externalization—the increasing reliance on an ecosystem of external partners from early discovery through commercialization. The second is the challenge of harmonizing incompatible infrastructures that result from mergers and acquisitions or competitor consolidation.

A variety of new technologies and infrastructure governance models have recently emerged that may be helpful in addressing these challenges. In this article, we will summarize these new technologies and describe their application in modern informatics risk- and burden- mitigation strategies.


Many publications have cited the increasing reliance upon externalization to develop new commercial products. While the short-term financial benefits are readily observable, there are some factors that present challenges to maintain an effective and efficient product innovation life cycle. Of particular interest for industrial laboratory scientists is this trend’s impact on collaboration.

Historically, large cross-functional teams were organized to develop a new commercial product. The interdependency of each function within a team requires close collaboration. The benefits of “bumping into brilliance” justified collocation. Scientists at Bell Labs, Los Alamos National Lab, and many others conducted Nobel prize-caliber research within their facilities.

However, the fixed financial costs associated with maintaining core R&D facilities present some commercial challenges. Therefore, executives at many firms have attempted to externalize specific core competencies—including a variety of laboratory functions.

Brian Fahie and Evan Guggenheim from Biogen1 presented an article categorizing innovation tasks with specific application to CRO outsourcing. Their model outlines the ability to establish a strategic framework whereby outsourcing can be used effectively while maintaining an appropriate level of internal capabilities.

Whatever distribution of internal and external assets an industrial firm selects, effective collaboration remains the greatest challenge to new product commercialization.


Similar collaboration challenges are presented to scientists when firms merge. Consider how core laboratory facilities are built. Instrumentation is selected and implemented for use based on the merits of the results these systems generate for scientists. While there are many competitive options for decision makers to consider, scientists will often standardize on a specific vendor’s instrumentation platform. Moreover, decision makers will often conduct extensive due diligence on equipment purchases, but such evaluations are based on a firm’s present set of selection criteria. As laboratory assets and supporting IT systems are built up over time, the considerations for extensibility are often under prioritized.

In the event of a corporate merger, firms are presented with the challenge of integrating systems that were not built to be integrated. Therefore, laboratory systems that need to communicate require the development of data extensions to consolidated decision support interfaces. Moreover, significant efforts to establish a minimum set of instrument management, control, and data repositories are undertaken after the merger. These efforts often result in a reduction in the overall data fidelity and quality compared to these systems prior to integration campaigns.

Office productivity and collaboration tools

To address both of these trends, many firms have implemented (or are currently implementing) general productivity and collaboration tools. Consider the rise in use of high-quality video conferencing, cloud-based document management, web conferencing, and many other general use tools.

These technologies indeed address some collaboration challenges when firms merge or externalize certain laboratory functions. The types of water-cooler conversations that often yield unanticipated inspiration can be replicated with appropriately deployed digital collaboration assets. Firms must instill appropriate use governance models to ensure successful technology adoption. In addition to developing a standard set of installation and operation criteria, successful “digital collaboration” implementations benefit greatly from an experience design approach.2 Prior to technology deployment, IT staff must understand the ideal user experience these systems must support. Leveraging a well-documented set of user experience criteria translated into robust system requirements can increase the probability of implementation success and, ultimately, improved collaboration.

Similarly, the facile use and exchange of documents in externalized (and geographically disparate) environments are afforded by modern document management systems—most large firms have implemented a variety of global document systems such as Microsoft Share- Point, Documentum by EMC, and IBM Connections.

Specialized language and data interrogation

As stated above, generalized collaboration assets have been implemented with reasonable success. Scientific collaboration, however, often requires systems to support highly specialized language. Consider the methods for communicating a chemical structure. For the past 50 years, firms have implemented specialized systems to appropriately manage chemical structures in a digital fashion. However, general communication and collaboration systems are significantly challenged to effectively manage chemical structures without integration with specialized chemical structure add-ons.

For instrument-generated data, a significant collaboration challenge is emerging. Current collaboration interfaces offer limited data presentation, exchange, and analysis capabilities. These systems require scientists to abstract high-fidelity instrument data to numerical values, textual annotations, and pictures. This abstraction can often limit the collaboration options available to scientists. Collaboration systems offer scientists limited ability to interact with live instrument data. In the past, such intimate data interrogation was afforded by a walk down to the lab. Scientists could easily interact with results Calibrex™ Bottle Top Dispensers directly at the instrument console—or even sitting in their office if their data acquisition systems were provisioned with appropriate network connectivity.

Considering the trends described above, such interactions are not possible:

  • In an externalized environment, scientists do not have persistent or convenient access to an external partner’s instrument acquisition console.
  • Right after a merger, a scientist may have access to another site’s set of instrument acquisition consoles after significant network access efforts have been undertaken. The console platform, however, may be an unfamiliar or undesirable interface and, if so, scientists would require a personal escort of sorts to conduct data interrogation. This assumes, of course, that an appropriate level of staff familiar with a foreign interface exists after merger-related activities are completed.


In order to address some of the scientific collaboration challenges presented by externalization and consolidation, thought leaders in informatics are considering new governance models for instrument-derived laboratory data. Specifically, systems must be developed to afford flexibility in the following areas:

  • Instrument asset flexibility
    IT systems must assume that the scientific instrument data formats will change periodically. This can be due to a shifting external partner ecosystem or a corporate merger. Therefore, IT systems should provide a wide variety of commercial data format support or invest in proprietary data conversion capabilities as needed. These data conversion routines should be future-proofed to ensure facile integration of new platforms when the need to do so arises.
  • Scientific data assemblies
    Consider material analyses that require interrogation of data from multiple techniques from different instruments, collected at dif ferent times in different places, inside or outside the company. This is especially relevant when leveraging material generated and tested by external partners, as described by Fahie and Guggenheim.1 To support such interrelatedness, firms must be able to not only sufficiently index individual analytical data files but must also afford “analysis assembly” capabilities directly within IT systems to provide users with a comprehensive “story” for relevant analyses.
  • Live data interrogation at the presentation tier
    Furthermore, IT systems must support the rich, live, and connected interrogation of data that scientific users require. Such types of analyses must be made available in an ad hoc, on-demand fashion. Therefore, appropriate data indexing, assembly (as described above), and ultimately rich-featured user interfaces must be decoupled from the native instrument acquisition console. This gives users the power of intimate data interrogation and analysis without the burden of maintaining system use proficiency across a variety of instrument data processing systems.
  • External partner data exchange
    As a condition of engagement, external partnerships must be extended to support data exchange beyond periodic summary reporting or results submission. Ideally, flexible data connections between external partners’ instrument data sources and internal data repositories should be established. However, facile cloud-based methods can also be considered when securely exchanging data—assuming, of course, that such systems offer the features for live data interrogation described above.


Recent efforts by firms to optimize laboratory assets (via externalization and consolidation) have presented significant collaboration and productivity challenges for scientific staff. While some collaboration tools have been successfully implemented, they often lack specialized scientific capabilities that laboratory staff require. The capabilities and governance models described above can help address successful scientific collaboration when appropriately implemented.