Creating a data governance plan and building data architecture that fits your needs is essential for getting the most out of your data, and ensuring that your projects are truly data-driven.
At BioLizard, we know from experience that instituting a solid data strategy will allow you to focus on what you are really good at, instead of spending your time finding, organizing, curating, and annotating your data.
A great data governance plan combined with customized data architecture that conforms to best practices makes your data work for you, instead of demanding work from you, and is an important prerequisite for applying advanced analytics like AI and machine learning. In order for advanced analytics like AI to be successful, high data quality and well-curated (meta)data is absolutely essential – and BioLizard has proven expertise in getting data AI-ready.
How will you know that you have a great R&D data management plan? When you barely have to think about data management anymore.
When BioLizard partners with organizations to build their perfect R&D data management plan, we always plan for future efficiency and success in its maintenance to make sure that it stays hassle-free. To accomplish this, we train internal data stewards within the company to do the ‘aftercare’ of complying with the agreements set out in a data management roadmap. This ensures that as your organization grows and projects progress, it will be possible to seamlessly scale and integrate new data without constantly needing to rework your plan for data management.
Oncology drug development often begins in patients, allowing early safety and efficacy insights. Yet many cancer drugs still fail in the clinic. We validate the target but ignore its context within the tumor microenvironment. This article explores why spatial biology may improve clinical success.
BioLizard has been named “Bioinformatics Innovation of the Year” in the 2025 BioTech Breakthrough Awards. The honor spotlights Bio|Verse® Navigator, a visual analytics workspace that unifies complex R&D data so scientists can decide faster, with confidence.
Bioinformatics pipelines break for the smallest reasons: package updates, shifting dependencies, or “it only works on my machine.” This post explains why experienced software engineers and DevOps practices (Git, CI/CD, IaC) are essential to keep workflows reproducible, stable, and scalable.
