Data is Vital to Life Sciences


Data can be seen as the raw fuel of the life science industry, especially for research and development undertaken by pharmaceutical companies in pursuit of effective and safe, new therapies and treatments. Most life science organizations have come to regard data, and its associated metadata, as a vital strategic asset that must be managed through appropriate investment in technology and people. This is analogous to how oil exploration has fuelled the generation of oil products (Figure 1).


The massive scale of data now available both within and outside life science organizations presents daunting challenges. These are much more tractable through making data and associated metadata Findable, Accessible, Interoperable and Reusable (FAIR) by machine workflows, supervised by scientists [Wilkinson et al 2016, Wise at al 2019] and the Pistoia Alliance FAIR Toolkit.


Experts in User Experience (UX) can contribute to this dynamic interplay between machines and scientists at critical touch points, through the systematic deployment of design principles and UX methodologies. This important relationship has the potential to facilitate when and how data is harnessed productively throughout its life cycle to deliver digital transformation and higher productivity for the enterprise.


Figure 1: Illustration of the data management life cycle which is analogous to how oil exploration fuels the generation of oil products through refinement.

Attribution: Oil elements designed by macrovector / Freepik (


Why should UX’ers contribute to data-driven projects? 

Understand the user-data dynamics


At its most basic level, UX is about understanding the goals and needs of users. Once understood, solutions are designed to achieve those goals. The complexity and vastness of data in the life sciences adds a far greater imperative for UX practitioners to fully recognize the variety of needs and requirements of all users of the system and its data. This means recognizing not only the needs of an end user but also data scientists and stakeholders within the data lifecycle. As the data is a crucial part of any potential solution it’s imperative that UX practitioners have a broader understanding of how the data will be generated and consumed in the long run. 

Facilitate usage of new technologies

We are in the age of the digital transformation, the process of using digital technologies to create new, or modify existing business processes, culture, and customer experiences to meet changing business and market requirements.  User experience professionals will be key to the success of these new initiatives.


As data silos are opened up or knocked down and data begins to flow bidirectionally between applications, departments, and global sites, UX’ers will need to work with stakeholders to help expose the right data to the right people through user (UI) and programmatic interfaces (API) that are intuitive and which enable the end user to make full use of the data available.


The amount of data flowing will continue to increase, as we move not only to the cloud to enable collaborative data usage but to the “edge”, which computes data closer to the source. Life science researchers in the laboratory can passively receive real time edge data from devices such as IoT sensors capturing temperature, humidity, light, sound, and pressure information, adding to the vast amount of new data bombarding them.


While a digital transformation can change the paradigm of an organization, the most important assets of the organization, its people, will need to have easy and pleasant experiences with the vast array of new data and technologies at their fingertips, or the transformation will fail.  UX professionals will be at the heart of ensuring that digital strategies are set up for success.


Enable understanding of data and technology


When scientists feel they can trust the data they see on screen, it increases their use and adoption of key software and technologies that improve their research. UX designers have a shared responsibility to make sure data that people see and consume can be relied upon to make sound decisions. Interfaces that present data (e.g. visualization tools and dashboards) must guard against errors, reduce the risk of misreading data and be intuitive to use.


For designers at companies that have adopted the FAIR Toolkit, it’s important to work with data stewards and other stakeholders to ensure that data is being presented accurately when mocking up user interfaces.  


Support user adoption through onboarding


Huge data projects can often fail with low adoption rates. Although the system that has been implemented may fulfill all the scientific requirements, it may be too cumbersome to use and initial onboarding not well designed. The UX designer skill set is particularly helpful when designing onboarding processes and materials. The knowledge gained through understanding user needs is transferable and can be employed when designing a good onboarding process. Effort and time allocated to onboarding design will not be wasted and ensure users adopt the system in the first place.  


Bridge between data provider and consumer


UX Design can contribute to bridging the gap between the needs of those who generate data and its consumers.


Equipped with their own UX arsenal for analysis (i.e.  journey maps, stakeholder analysis and/or data modelling tools) or presentation (prototyping, UI design, Interaction design), UX designers enable a holistic approach to data science projects and can help resolve the tension between the goals of either parties. 


More crucially, since different users with different roles have specific needs and expect and/or require different sets of data (or metadata), it is one of the main goals of UX designers to translate  and present the data  and their associated metadata so that users will make sense of the data landscape in each step of their journey.


Creative visualisation to generate insights


It is very hard to make sense of a big set of data on a single interface. One of the most challenging parts of being a UX Designer for big data projects is to find efficient ways to represent the data. Choosing the right visualisation tools for the right user flows can enable scientists to be able to scan through large datasets of information faster, and also to find new patterns in the visualized dataset. Graph Explorer or network visualisation of data can be very efficient ways to support research works, especially if UX designers find ways to enable users to be able to interact with these graph visualisations. Different views can lead to different discoveries.


UX designers skill set is primed for big data

We know that data is core to the life sciences and UX designers are a key part of the team delivering on this data. Using UX methodologies we are able to uncover user needs and insights. UX designers are able to create better workflows and touch points for their end users. However, UX designers need to understand the implications of their design on the data life cycle and for all those stakeholders involved in the data life cycle. There are pitfalls and limitations to dealing with data which UX designers need to learn from. The Pistoia Alliance FAIR Toolkit and UXLS Toolkit are excellent resources to get started on your data-driven UX journey.



We thank Sven Neumeyer (NIBR) for his invaluable review and the rest of the UXLS project team.


Image attribution

Header image: “Manhattan Bridge” by alvaroreguly (CC BY 2.0)

Oil image: Oil elements designed by macrovector / Freepik (