By Andrew Anderson, VP of Innovation & Informatics Strategy
Digital transformation has been a key focus area across industries in recent years. Certainly, a representative example area of success is in consumer insights—consumer data collection plays a major role in allowing companies to analyze key information about their customers and draw insights to inform a variety of strategic decisions. However, digitalization in labs, analytical data in particular, has not progressed as quickly as other potential digital applications. This is due in part to the complexity of scientific processes and resultant datasets, but also due to the lack of consistency across different scientific/experimental approaches.
While many companies in industries like retail, healthcare, and telecommunications rely on digital data collection to improve communication with customers, life sciences organizations similarly rely on digitalized data to improve communication and collaboration, both across the organization and with external partners. Due to the significant value of the data and its huge potential to influence decision-making and future experimentation, it is important that the data is stored in an accessible and on-demand manner.
With the volume of scientific data increasing dramatically, scalable processing, analysis, and storage technology is also a critical requirement. Organizations are embracing cloud-based technologies to support the data volume and accessibility requirements to ultimately reach overall R&D goals.
As digitalization makes data available to software applications for processing, and the cloud makes it more accessible, here are three reasons why digitalization and data on the cloud go hand-in-hand.
- The cloud enables on-demand access to data
The vast amount of data collected often sits in disparate-but-functional silos. By storing the data on the cloud, scientists are able to access, connect, and store the information in one place. Access to data in real-time presents opportunities for more efficient, data-driven innovation; scientists are able to work with the most up-to-date datasets and apply insights to their current projects. This model is exhibited well by our work with Pfizer to improve access to analytical data for the organization’s scientists using cloud-based systems.
Additionally, by having data stored on the cloud, scientists are able to not only prevent the risk of data losses (and avoid repeated experiments when possible), but also spend less time gathering and reformatting data which allows for enhanced alignment with their organization’s R&D goals.
Moreover, the COVID-19 pandemic has exacerbated the urgent need for on-demand access to data, as many scientists have limited access to data that resides in the laboratory systems. Data on the cloud supports continuous and uninterrupted data accessibility, making overall lab work more efficient and fostering a collaborative environment.
- The cloud increases collaboration
With digitalization, labs are able to capture and store data in a digital form, enabling computational systems to consistently interpret and intelligently analyze all information available. Due to the benefits of on-demand access to data on the cloud, scientists have more time for collaboration and therefore experience heightened levels of collaboration efficiency. Additionally, organizations collaborating with external partners benefit from more expertise, resulting in a decrease in repetitive work (avoiding multiple organizations working on the same piece of a project) and streamlined data management.
An entire organization having access to the same data on the cloud prevents major disruptions in workflows. When an issue arises, the entire organization can be made aware, divisions within the organization can work to solve the issue, and it’s less likely that there will be a case of miscommunication leading divisions to work with outdated/inaccurate data for a period of time.
- The cloud provides a reliable source for comprehensive datasets
The ability to store large volumes of data on the cloud makes it possible for scientists to take a comprehensive look at large datasets easily. It’s important to remember that the re-use of data requires context and therefore requires special attention. Data stored on the cloud can be accessible to scientists looking to connect data, metadata, and other relevant information to see the full context.
Since many scientists are skeptical and require a “full data picture” before drawing conclusions, it’s also important for them to have access to all relevant datasets to draw insights from and use for decision-making purposes. That’s where the cloud comes in—cloud environments are capable of storing large volumes of data, whereas other storage methods may limit the extent of the data picture. A comprehensive look at full datasets allows scientists to glean insight that can lead to more productive experiments in the future. Additionally, the comprehensive look minimizes redundancy within an organization as scientists can avoid repeating work already done by others inside or outside the organization.
Digitalization is in full swing, so organizations must place an increased focus on the destination of their analytical data. While digitalization is critical in making data available to software applications for handling, processing, and analysis, the cloud is key in making the data accessible across organizations, both internally and externally. Thus, digitalization and data stored on the cloud go hand-in-hand as organizations gain on-demand access to digitalized data through the combined approach.
To learn more about the benefits of on-demand access to digitalized data, take a look at our recent eBook, “Ensuring Insights from Data: A view beyond ELNs, LIMS, and SDMS”.