Strategies for a Successful Cloud Implementation
To implement scalable cloud technologies in the lab, decision-makers must consider data complexity, data storage and security, and standardization
Scalable cloud technologies appeal to laboratory businesses aspiring to improve and grow via digital transformation. Specific goals include accelerating and automating the marshalling, standardization, and interpretation of data to supply analytics pipelines, rapidly achieving insights and increasing visibility for all stakeholders.
The key to a successful cloud implementation involves strategies incorporating the required technical elements for marshalling scientific instrument data: transmitting, transforming, analyzing, and cleaning it. Also needed are integrated capabilities for applications, storage, backups, and security, while dealing with the additional factors of governance, deployment, and costs.
Dealing with data complexity
The main factor behind a cloud strategy is data; however, scientific lab data are complex. A cloud strategy covers the design, preparation, characterization, and testing of bio(chemical) substances and materials, including their properties, reactions, processes that form them, and all the methods used. The variety and diversity of available scientific lab equipment mean this data is heterogeneous.
Standardizing data and metadata simplifies processing and interpretation using applications, their (generally RESTful) Application Programming Interfaces APIs, and/or services, including workflow orchestration. Digital representations are needed for each unit operation of classic design-make-test cycles, whilst avoiding added extraneous transposition of information between subsystems.
Storage considerations: access and security
For cloud-based digital data to improve efficiency, add value, or facilitate innovation, it needs to be securely accessible. It must also be transferrable from original data sources and retrievable for authorized individuals. Metadata is crucial to data being findable.
For most scientists, visualizing and interpreting data is vital for their work. However, all the preceding considerations also make data amenable to automated pipelines for artificial intelligence (AI) or machine learning (ML) analytics. Ultimately, data storage and backup are essential for utility beyond the present moment, whether for data review, re-analysis, data mining, or troubleshooting. For example, global pharmaceutical companies’ Chemical Manufacturing and Controls (CMC) groups establish process standards and ensure drug product quality throughout development. For a potential medication to successfully reach the market, regulators require consistency in the identity, quality, safety, stability, and strength of products for clinical trial use, release, and manufacturing.
To accomplish this, CMC data is collected during the development processes from thousands of analytical instruments using a variety of methods. This results in a subsequent challenge of marshalling, assembling, and interpreting large collections of heterogeneous data to evidence product quality.
Normalizing and standardizing data structure
The key is normalizing and standardizing the data structure to be independent of the originating data source architectures. Doing so ensures scientists have the information they need to determine identity and stability, satisfy inquiries by authorities, and ultimately yield insights from AI/ML analytics. For instance, marshalling chromatographic data from all processes used to generate a particular Active Pharmaceutical Ingredient (API), and assembling the chemicals of those operations and identified impurities present from each, can streamline the search for situations where a potentially genotoxic impurity may have been generated.
The final phase is to incorporate the various technologies to meet the organization’s desired outputs. Choices should logically originate from workflow requirements and proceed to support services and systems. So, start with applications having APIs that can marshal the necessary data, ensure that metadata can be extracted, and that it can be processed, interpreted, standardized, and assembled as needed. Then, determine at which cloud abstraction layer implementations would be most practical, whether as infrastructure, platform, or software as a service. Other considerations include scaling and load balancing, networking connectivity, and authentications needed for client systems to access data securely and rapidly, and which cloud providers can support the desired technology stack.
A successful cloud implementation can accelerate insights and outcomes by helping scientists work with data more efficiently and securely. To support effective collaboration in today’s digital lab, organizations must follow these strategies around data. To manage its complexity, storage, and access, and to ensure they’re meeting the information needs of scientists and other stakeholders effectively.