The cloud deployment model has proven its value for CDS workloads from sample management to complex analytics.

The cloud deployment model has proven its value for CDS workloads from sample management to complex analytics. But many labs have been slow to jump on the cloud bandwagon, for multiple reasons. They operate in heavily regulated environments. There are data integrity issues to consider. Moving CDS to a cloud model could cause temporary disruptions in workflows.

All of these challenges can be overcome. The one issue that is more difficult to address is an integral part of human nature: inertia.

In many cases, lab managers are intrigued by the advantages of CDS in the cloud but become overwhelmed by the complexities of getting there. This article provides a brief recap of the benefits of moving CDS to the cloud and the key considerations in getting started, with pointers to more detailed information.

The Benefits: Look at Cloud from All Sides Now

When assessing the advantages of the cloud for CDS, be sure to consider the benefits for all constituents. For example:

  • For lab technicians, a cloud deployment means faster, easier access to data and tools from anywhere. The cloud can also help with automation or simplification of core CDS-related processes. Volumes of data can be managed through a simple Web portal.
  • For IT, a cloud deployment eliminates the need to purchase and maintain the infrastructure to run CDS. It ensures that the organization pays only for the resources it consumes. Fewer staff are required to deliver on and manage the lab’s computing needs, saving money and reducing the need to hire additional IT employees.
  • For lab leaders, cloud adoption can reduce costs and increase team morale by enabling self-service access to resources. It provides easier access to sophisticated analytics, simplified data migration, and more. Cloud also increases collaboration, enabling a cross-pollination of knowledge and expertise.
  • From a business perspective, the cloud can accelerate the lab’s digital transformation initiatives. It offers a unique opportunity to leapfrog ahead and deliver new operational efficiencies.

Making the Move: 5 Key Considerations

When you’ve made the decision that cloud is a better option for your CDS workloads, here are the preliminary steps in transforming the promise into reality:

1. Define your objectives precisely. Start by pinpointing your goals in moving CDS to a cloud model. What are the desired outcomes for lab technicians, for IT, and for the lab’s business? And how will you measure success? What metrics will you use to track cost savings and productivity gains, and how will you measure intangibles such as the impact on job satisfaction and collaboration among lab technicians?

2. Understand your options. Cloud technology is evolving rapidly, expanding the range of choices for deploying and managing CDS workloads. Make sure you understand both the basic cloud service models such as Software-as-a-Service (SaaS) and Infrastructure-as-a-Service (IaaS), and the primary delivery models, including public cloud, private cloud, community cloud, and hybrid cloud. For more information, read our white paper, “Cloud Adoption for Lab Informatics,” or these articles “CDS and Cloud Computing” and “Cloud Architecture” in this eBook “Is Chromatography Ready for the Cloud, Part 1”.

3. Carefully examine the regulatory impact. In a regulated laboratory, you need to know what impact GxP regulations will have on the cloud and vice versa. And you need to know this up front rather than wait for an inspector to start writing citations for violations!

The main US, EU, and OECD GLP and GMP regulations (5-8) do not explicitly mention cloud computing, as they were written before the cloud was widely available. Therefore, regulations must be interpreted for the cloud. Look to modern regulations, such as EU GMP Annex 11 for computerized systems, and consider the potential impact of all GxP regulations, including good laboratory practices (GLPs), good clinical practices (GCPs), and good manufacturing practices (GMPs) when planning your move to the cloud model. For more information read this eBook “Is Chromatography Read for the Cloud, Part 1” on applicable GxP regulations.

4. Do your data integrity homework upfront. Moving CDS to a cloud model can create new data integrity and security challenges, so be sure you are prepared to address them before you make the move. Migrating data to and from the public cloud, for example, puts data outside the lab’s direct control, and the “shared responsibility” security model of the public cloud means protecting the lab’s data and applications is still primarily the lab’s responsibility, not the cloud service provider’s.

However, the cloud can also offer new solutions to old data integrity challenges. For example, multi-cloud services enable labs to test data integrity across huge data sets and user populations to identify potential issues or even the risk of a security breach. In addition, many advanced data integrity and data loss prevention technologies have proven highly effective at thwarting advanced attacks that traditional security technologies do not even detect. For additional information, visit the web page “Guidance for OpenLab Cloud Deployments.”

5. Partner with IT for a collaborative transition. Ultimately, IT will do the heavy lifting in moving CDS to the cloud model. This is not a task lab managers should simply “throw over the fence.” By working closely with IT on every aspect of the transition, you can accelerate and amplify the results for all stakeholders.

Move ahead with confidence.

The transition to a cloud-based model for CDS can improve many lab processes without compromising data integrity. However, there is a lot to learn before the journey can begin. Leverage the resources below to increase your understanding of the both the why and the how of moving to the cloud model, and the advantages of OpenLab CDS cloud options.

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