A Laboratory Information Management System (LIMS) relies heavily on the system master data to deliver functionality to end users and reflect the needs of the business processes. As such, there are a few LIMS master data best practices to keep in mind when considering the complexity of the data at hand. 

LIMS master data covers many different object types across different process areas, such as Lot Management, Stability, and Environmental Monitoring. A key decision during a LIMS implementation is identifying the level of complexity needed in the master data. While there are varying levels of complexity, ultimately the decision for the level of complexity is directly dependent on the business needs. 

Levels of LIMS Master Data Complexity

Master data for test methods is an excellent example to showcase the different levels of complexity that could be deployed, such as: 

1. Final results only: This is the least complex type, where only the final and reportable results that will be stored in the LIMS – excluding any instrument, standard and reagent, or intermediate data – are recorded. This is the easiest and quickest way to implement and support test methods in a LIMS, but it offers the least benefit to the business, typically leveraged only in fully virtual environments or when the LIMS is primarily being used for generating regulatory reports. 
2. Middle ground: A moderately complex test method can be used to store many types of data. A QC Batch or worklists can be leveraged to record testing metadata for a test method (e.g. instrument use, standards and reagents needs, system suitability tests), and multiple intermediate results can be combined with simple calculations to assist the analyst in performing the test method. While creating and supporting this type of test method is a more complex effort, it doesn’t require a system expert to configure or customize functionality, adding value for the business. 
3. Guided execution: Highly complex guided execution employs logic to guide an analyst through the test method step-by-step. In this type of master data, programming can be used to combine data from other tests on the same sample (e.g. concentration values that are used in calculations), to expose data entry parameters based on other values within the test, or to resample or retest events based on results. Due to the complexity of design and the need for customization, this type of test method requires the most time to implement, the heaviest level of validation, and has the highest long-term support burden; however, it also reduces the risk for human error during test execution. 

As the test method master data increases in complexity, there is a trade-off between the value returned to the business and the costs associated with creating and maintaining the master data.   

LIMS Master Data Best Practices

Below, we outline some of the questions that can help guide your decision for managing the complexity of LIMS master data and ensure you’re following LIMS master data best practices: 

1. Maintenance: Who will maintain this master data and who would be able to make updates when needed? What is the LIMS support model? Will you use an external support team or internal SMEs?  If the master data is beyond the skillset of maintaining in house, the company would need to consider a contract with a AMS provider to maintain master data. When considering the complexity of master data, the maintenance is just as, if not more, important than the initial set up. 
2. Interfaces: Do interfaces need to be considered? Does the LIMS need to reflect the physical activity 1:1? Are you replacing other tools that exist to support the process (e.g., spreadsheets)? Interfaces add a level of complexity to the system. This may be a simple instrument or an SAP interface, and businesses need to consider and understand the effect on how the systems interface to the master data build. 
3. Resources: Do you have the resources available for testing and validating? What level of access do you have to business resources for design and testing? As the complexity increases, the need for business involvement grows. 
4. Technology: What is the level of technical complexity that your business resources are familiar with? As technology grows and advances, learning that new technologies is paramount to maintaining the LIMS. It may be necessary to promote additional training, certifications, or other learning opportunities to maintain complexity in-house. 
5. Business Process: What is the volume of master data that must be created? How frequently are the SOPs (standard operating procedures) that govern the process updated? As we think about complexity, we must think about the time and resources it will take to maintain master data. If there is a simple build, there will be minimal maintenance; however, adding complexity increases the time to make any changes that would be necessary as processes change. With frequent changes on a complex system, it would be difficult to sustain. 

Determining the complexity of your master data depends on several business decisions that must be considered in order to determine the best fit for you. The complexity can vary from a simple, results-only implementation with little to no added functionality to the most complex, guided execution approach. There are benefits and risks to each approach but with careful consideration you can be sure you are making the right choice. 

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