Combining the right expertise to tackle data management challenges

The Data Management team is the newest team at BioLizard, but although it is still small, it is mighty. Yves Muyssen , COO of BioLizard and Team Lead for data management, says, “Today the team consists of me and Galina Polishchuk but we heavily rely on experts from different teams," such as Volodimir Olexiouk of the Data Analytics & AI team and Erik Vandeputte of the Software & IT Infrastructure team.

This collaborative approach is entrenched deeply in the DNA of BioLizard. Volodimir Olexiouk , Team Lead in Data Analytics & AI at BioLizard and regular collaborator on data management projects, explains, ?“For each and every project, the right lizards are assembled in order to provide the best solution for our clients. We interact in order to combine the right expertise for each task in order to deliver optimal results to our clients.”

Although Data Management is a very large and varied topic, it is quite common for it to come as an afterthought in the life sciences. As a result, Yves explains, “Data management projects typically come to us as a follow-up to a previous project that was focused on a specific use case, be it a predictive model or bioinformatics question. During those projects our customers realise that there is a lot of effort needed ‘under the hood’ to make sure that these use cases can bring their value to the end users.”

The goal of the Data Management team, thus, is to structure and organise everything ‘under the hood’. One overarching goal in data management is to follow the FAIR guiding principles for scientific data management and stewardship - in other words, to ensure that all organisational data is Findable, Accessible, Interoperable, and Reusable. However, it’s not always easy for organisations to make their data FAIR on their own. “This is where we come in,” says Yves.?

Data management is a very broad topic, and is only becoming increasingly important in life sciences. Historically, R&D teams tended to do just fine with pen-and-paper solutions,? because in past decades there were fewer data generated per experiment. But nowadays, on average researchers generate 10000x more data per experiment than they did a decade ago, but spend 30%–40% of their time searching for, aggregating, and cleansing data, due to the exponential increase in dataset size.

In the experience of the Data Management team, these days effective data management is essential for generating the best and most accurate scientific insights, because a less biased approach can be implemented for data collection and analysis, and no data will be left behind or forgotten. Effective data management is also often a prerequisite for implementing state of the art technologies that can give your organisation a competitive edge, such as AI or machine learning.

Oftentimes, the leadership of life sciences companies fully recognises the value that data can bring, but can run into challenges with implementing data management strategies. To build a FAIR data management plan, address limitations in data quality, and bring ML models to production, data savvy talent is necessary and not always at hand. So, this is exactly where BioLizard’s expertise in data governance, data architecture, and? ML-Ops comes in.?

If you would like to learn more about this topic, be sure to check out our blog series on data management - and to follow us on LinkedIn to read the rest of our #MeetTheLizards campaign!

Are you interested in putting effective data management strategies into practice at your life sciences organisation? Reach out to us today!