Over the years, we’ve been involved in a number of LIMS/ELN selection projects. In this new series of blog posts, we’ll take a look at some of the lessons learned from those projects.
Requirements Are Required
While today’s ELN/LIMS solutions are highly configurable, there is no one-size fits all solution. Requirements analysis will help insure that your selection process identifies technologies that can best support your organization. They help you make data-driven decisions, and minimize bias in the selection process.
Every business is unique. On the surface this is counter-intuitive. For example, one might expect that all small-molecule companies would follow the same process to arrive at the same destination. But this isn’t the case. Even between small molecule companies, there a differences: rational drug discovery vs fragment-based design, knockdown studies vs knockout models, etc.
By reviewing your process with your teams, requirements come to the surface that might easily have been overlooked. The data generated by each step is documented, making it easier to identify software capable of parsing and loading the data.
Priorities for each requirement are identified. This makes it possible to generate weighted scores when you assess the vendors, and tells you where to focus your resources.
Requirements for the system fall largely into two categories: scientific and technical. Here are a few examples.
Scientific Requirements
Data/Instrument – The system should be able to import instrument data from all of the instruments that you’re currently using. The vendor should have some relationship with instrument vendors to insure that the drivers required to read these data files are supported even as the instrument software continues to change.
Integration – The system should be as self-contained as possible, or should present a minimal number of integration points. Imagine that your system is a collection of links in a chain. Each time one or more links changes, you need to validate the system again.
Data Verification & Signoff – The system should support the ability of scientists to verify the data, and electronically sign the dataset.
Analysis – The system should support the data analysis work that needs to be done
Technical Requirements
Cloud Requirements – Does the system need to be on-premise, or should it be in the cloud?
Backup Requirements – How frequently should the system be backed up? How will the backups be tested?
Disaster Recovery Requirements – In the event of a system failure, what plans should the vendor have in place to insure that you can get up and running again quickly?
Data Transfer Requirements – How much data will be transferred to the system? For example, if you need to transfer large imaging data sets to the system, then you’ll need to look at bandwidth requirements.
Performance Requirements – The system should always feel performant to the user. No lags in responsiveness at certain times of the day when all of the labs are inputting data.
Single Tenancy vs Multi-tenancy – A single-tenancy system means that your instance of the application is the only one running on the hardware. You’re not sharing it with anyone else, and therefore not potentially impeded by other tenants.
Read more of the ELN/LIMS Blog Post Series
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