Using data management to generate value

BioLizard helps organizations build tailored R&D data management plans, combining strong governance and custom data architecture. By making data AI-ready and hassle-free, teams can focus on research while ensuring high-quality, scalable, and FAIR-compliant data for future analytics.

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.

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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.

How spatial biology improves clinical trial success in oncology

How spatial biology improves clinical trial success in oncology

In oncology, the drug development path is unique: Phase 0 and Phase I trials are typically conducted in patients rather than healthy volunteers, allowing for early assessment of efficacy and patient selection alongside safety. Yet, even with this early clinical insight, many cancer drugs show promise in the lab but fail to transition effectively into the clinic. This often happens because, while we verify that a drug’s target is present, we frequently overlook its context, specifically its location, the surrounding microenvironment, and its interaction with neighboring cells. By revisiting real-world examples of discontinued trials, this post explains why understanding the “where” is just as critical as the “what”, and how spatial biology is positioning itself as a valuable avenue for validating clinical potential.

Why bioinformatics workflows require experienced software engineers

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.