“I have been passionate about data my whole life, all types of data,” says Yves Muyssen, COO and Team Lead for Data Management at BioLizard. “Be it geospatial data, consumer data or lifesciences data, the power of data and information in combination with its complexity has always been intriguing to me.”
In short, Yves Muyssen has always loved and worked with data. Nowadays at BioLizard, he is leveraging his knowledge and experience to help clients solve their data management challenges and make sure that their most valuable asset – data – is well cared for.
Led by a lifelong love for data
By training, Yves is a bio-engineer and computer scientist, and in his words, he has always been interested in how to “use computing power to bring structure in the chaos of data around us.” Following his education and scientific research at Ghent University, Yves gained extensive experience in a variety of international tech companies in the smart mobility, digital marketing, health tech, and big data industries. It was while working at Tele Atlas and TomTom, “one of the first companies with a mission to create a ‘Digital Map of the world’, long before Google Maps existed,” Yves explains, that he started to dig into the power of effective data management. “Data was and is the biggest asset of the company and there I learned my first steps of what it takes to manage data so it can bring value for the end users.”
Yves became a lizard last year. Since then, Yves has dove deeper and deeper into the topic of data management. In February 2023, Yves passed his exam on the DAMA Data Management Framework, certifying him as a Master of data management!
As both COO and lead the Data Management team, Yves finds that having a broad view on all the capabilities within BioLizard allows him to define cross-functional teams that help customers solve their specific data management use cases. “I love the combination of building and leading teams, with the technical challenges of data management.”
Assembling the right people to create unique solutions for 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, explains, “We heavily rely on experts from different teams.”
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 realize 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 data management team: (left to right) Volodimir Olexiouk, Erik Vandeputte, Galina Polishchuk, and Yves Muyssen.
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 preclinical data management, which was created in collaboration with Yves:
- Read this blog article to understand how preclinical data management can help save your life sciences organization time, money, and headaches
- Read this blog article for a step-by-step guide for building a successful preclinical data management plan
- Read this blog article to learn about how to stay on top of your preclinical data management game once you have implemented a great preclinical data management plan
If you prefer to watch rather than read, you can also take a look at our recent webinar all about preclinical data management, co-presented by Yves Muyssen and our team lead for Bioinformatics, Alexander Koch.
Are you ready to collaborate with BioLizard to ensure that your data is leveraged to the highest possible level?
Collaborate with us.
