Artificial Intelligence (AI) and Machine Learning (ML) have fully cemented themselves as cornerstone technologies in the building of the future pharmaceutical workspace. Success stories are now readily available, including speeding up research & development (R&D), faster turnaround times and release of drug products, and identifying previously unidentifiable trends. New solutions are coming at a rapid pace, and the life sciences industry is making significant investments to try to build competitive advantages. As your organization continues to plan for the technological revolution, learn how you can establish a data foundation for AI in laboratories. 

An Increasing Amount of Data 

AI and ML require a large amount of reliable, accurate, and clean data to be successful tools for an organization to have a positive impact on predictability, such as catching results before they fall out of specification. Organizations are trending towards creating more data than in years past, which is why AI and ML have become the leading technologies in life sciences.  

It was quickly recognized that all this data has tremendous value, and organizations started digitalizing as much as possible. The problem with this, however, is it often creates a disjointed portfolio of software and databases woven together through processes and manual collation. This results in the difficult problem of extracting true value from the large amount of data collected due to a potential lack of accuracy and reliability.   

Having the Right Resources in Place 

To start a foundation for AI and ML, it’s crucial to have an established team focused on the data strategy and empowering them with the resources needed to implement this strategy will be vital. This team should span the organization and be representative with specializations in the enterprise-level tools you will use to collate and assess your data.  

Most organizations have some form of this, however, here are some additional considerations:  

• Does my organization have an empowered data strategy team?  
• Is my organization’s data strategy clear in how it applies to my responsibilities?  
• Does my methodology/process include clear actions and responsibilities for the data strategy team’s involvement?  
• Is there a clear roadmap for how I will get my systems and data aligned?  

Data Strategy 

Data in the laboratory is often highly segmented because of the numerous specialized tools and software required to meet a wide variety of needs. Consistency and streamlined data are key in turning that segmented data into actionable knowledge that AI and ML can use to provide accurate returns. Achieving this involves input from your data strategy team in new implementations and larger enhancement work.  

Ensuring prioritization of these items in your methodology can create immediate new opportunities as well as future-proof your architecture and systems. Already having prioritized enterprise systems and process harmonization will provide a huge advantage, as their data structures are likely dependable. Organizations that are not yet at that stage should carefully define data standards and enforcement of them.  

It’s essential to have all data follow the same data strategy, which may result in the need to repair existing data where strategies may not be aligned. It is recommended to begin evaluating already existing data hubs in your labs, such as a Laboratory Information Management System (LIMS) and Quality Management System (QMS). These will often make the largest impact and open new opportunities for you to leverage the data within. Aligning your existing data with your new data strategy will ensure that it can be leveraged in perpetuity.   

Leveraging the Data 

Once data is reliable, consistent, clean, and accurate, it’s then time to compile the data in useful and meaningful ways. One way to do so is with the increased ability to analyze data within your individual larger data hubs, such as a LIMS. This results in existing tools and metrics becoming more effective, accurate, and actionable.  

Another way is by leveraging the wider view of data lakes, which have been and still are the primary tool for accomplishing this, particularly in the often-disjointed laboratory space. Understanding your reporting and AI use cases will help you select and prioritize data sets that need to be transferred to your data lake. Data lakes can be extremely powerful with the right tools, providing you insights across previously disjointed areas.   

Looking Ahead 

As AI and ML become more prominent in the laboratory, organizations must remember that data is the foundation for these technologies. Predictive and generative outputs are only as good as the data being evaluated. Clarkston can help establish a data strategy and fundamentals to ensure your AI and ML data is actionable and valuable. Contact our Lab Informatics experts today to learn more. 

Contributions from Rick Curtis