More recently in the life sciences industry, SDMS has been used for archiving the raw data generated by laboratory instruments to provide seamless data retrieval, more efficient instrument data reporting, and more structured data integration and interfacing between SDMS and another system (e.g., other external laboratory software applications such as LIMS, which we will cover briefly below). As organizations move toward implementing SDMS for their operations, there are a few considerations to keep in mind. 

What is SDMS? 

A Scientific Data Management System (SDMS) provides life science organizations with effective raw data management that’s critical to quality and compliance interests. SDMS often acts as an Electronic Document Management System (EDMS) to capture, catalog, archive, and retrieve large sets of unstructured data. 

SDMS vs. LIMS 

So, what’s the difference between a Laboratory Information Management System (LIMS) and SDMS? The lines can get very blurry depending on whom you ask. LIMS, in a more traditional sense, are customarily implemented in the life sciences industry to accommodate structured, mostly standardized laboratory operational data, whereas SDMS are implemented to accommodate unstructured, mostly heterogeneous instrument data.  

SDMS enable the laboratory to view all instrument data routinely and ensure that data is securely stored and managed in a central location. As with other life sciences GxP applications, users are managed based on user groups, roles, and permissions, so anyone with proper credentials would be able to drill down from a result in an interfaced Lab System to the originating instrument report, all stored and viewable in the SDMS. 

Interfacing LIMS with SDMS 

As mentioned above, one of the benefits of the SDMS is the ability for the managed data to be interfaced with other external laboratory software applications, such as LIMS, Electronic Laboratory Notebooks (ELN), Chromatography Data System (CDS), and others. 

Interfacing SDMS with your LIMS could bring a wealth of benefits, such as: 

• A significant reduction in manual data entry and verification, which increases productivity and efficiency. 
• The ability to eliminate data integrity deviations from manual transactions.
• Improved confidence in data quality, accuracy, and consistency of use. 
• Standardized data for analysis and usage in other applications. 
• Long-term, single archival solution for efficient management of data. 

If the benefits of interfacing LIMS to SDMS are of interest, it may be worthwhile to consider performing a thorough understanding of your current and future software applications and data storage needs in quality and compliance areas. An assessment of your current system compliance risks and gaps could lead you to identify where a SDMS may assist. Similarly, as it relates to system risks and gaps, there may be similar observances needed for your data, especially as it relates to instruments, where SDMS may assist.  

Additionally, prior to implementing an SDMS, organizations should also consider:

• Establishing an inventory of the instruments you have or plan to acquire in the future. 
• Rollout strategy in phases based on the priority of instruments in inventory or being acquired. 
• Allowing for adequate time to map rollout sequencing based on numerous factors if it’s a phased approach (examples being instruments with highest utilization, spread out complexity, quicker process for like for like, etc.). 
• A non-aggressive project schedule and timeline. 
• Planning appropriately against any LIMS constraints and other external systems and dependencies. 
• Starting with a small scope, which could potentially be based on the size of the anticipated project team. 

How do we get started with implementing SDMS? 

If your organization has decided to implement SDMS, here are six efforts to consider before embarking on that journey:  

1. Collect Instrument Inventory Information. There is work required to collect as much information about every instrument at every lab in the organization. After data is collected, the instrument inventory data should be combined in an Instrument Inventory Master List for prioritizing instruments, vendor selection, planning infrastructure, data harmonization, and ingestion.
2. Plan Strategy and Prioritize Instruments. It’s highly recommended to begin the effort with a few instruments to determine the best strategy for implementing SDMS on a larger scale. We typically call this the pilot phase to gauge proof of concept and delivery. This work enables the team to prove the end-to-end solution from development to production for upper management buy-in before moving forward. These efforts also enable project management to gain impactful insights on the process of the data platform to enable planning of full rollout. Furthermore, planning the IT support model enables the agile implementation of instruments as they become ready for production.
3. Plan Interface with LIMS and other external applications. A typical bi-directional interface would involve querying LIMS for a list of pending samples for a certain test. After collecting the relevant samples, the interface would deliver the job to the instrument. After analysis, the data results are collected by the interface and reported back to LIMS. There are several questions that come to mind when planning the interface with LIMS and other external applications. If interfacing with LIMS, what test methods will be included? Will the interface be uni-directional or bi-directional? What middleware will be utilized? How will data be mapped between systems? What data harmonization rules will be used? 
4. Evaluate and Select SDMS Vendor. Strategic partners are a must to implement SDMS for Instrument Interface. You should be very diligent in the evaluation of selecting a vendor, as this can be critical to the overall success/failure of the project.
5. Plan Appropriate System Architecture and Infrastructure. Very similar to LIMS implementations, it’s typically recommended to configure and deploy three instances (DEV, QA, PROD) of the SDMS Application environment. This involves effort from IT Infrastructure teams to install all necessary SDMS software and platforms, agents, datahubs/servers, IT security protocols, and accounts. This work being planned and implemented appropriately is crucial to project timelines and schedules.
6. Determine Instrument Connectivity Strategies. During the collection of instrument data, it will be determined that some of your organization’s instruments are not currently connected to your internal network. This is where a strategy will be needed to plan how any standalone instruments will be connected to the network.  

Questions that need answers: Will the instruments be networked? Will electrical outlets, network jacks, switches, and ports require installation and configuration? Would any instruments need an Internet of Things (IoT) device to enable connectivity? How will you deal with non-networked instruments that are connected to a PC? Does the PC need to be upgraded to enable connectivity? If upgraded, what level of re-validation is needed? 

Implementing SDMS 

As with all life sciences application projects, validation, testing, change management, and training will also be key deliverables for the commitment to an SDMS. You’ll also need to ensure that these efforts align with your organization’s overall goals and objectives. For further guidance on SDMS implementations, connect with one of our experts today.