What is Data Stewardship? does it matter in R&D? In Life sciences?

What is Data Stewardship?

Data Stewardship is the responsible management and oversight of an organisation’s data assets to ensure they are accurate, accessible, and secure. It involves working with the entire data stakeholders to get the most value out of the data, setting processes for data collection, storage, usage, and sharing. The aim is to maintain the integrity, accessibility and quality of data in preparation for its use and reuse, while also complying with regulatory requirements and fostering ethical practices. It is not restricted to the scientific world, many industries use data stewardship to make the most of their data. Several similar definitions be found on the web, on various websites not connected to scientific world (nor affiliated to me).

In a scientific context, data stewardship is crucial for ensuring that data used in research is reliable, reproducible, and can be shared effectively within the scientific community, contributing to advancements in knowledge and innovation. Think FAIR data principles but also data flows.

Data stewardship is not a new fancy tool. Data stewards have been around for a while, possibly under other labels such as data custodian, data guardian, information curator or data manager or even without a label, not as a fully recognised function.

Is Data Governance the same as Data Stewardship?

The simple answer is no. But they work closely together.

There are other similarities and other differences and that may vary from one organisation to the other. But they have to work hand-in-hand to provide the most value to an organisation.

What about Data Science?

Data Science is another actor in R&D data landscape. Data Stewards need to include data scientists in their overall review of the organisational data landscape. They are one of the users of the data produced by scientists at the bench. They are also using other data sources such public or commercial datasets. Accessibility, format, interoperability are issues commonly encountered by Data Scientists. Data stewards will work with data scientists to understand their needs.

What can Data Stewardship help with?

Data Stewardship can help R&D organisation to achieve their goals by engaging with all the stakeholders, understands their goals and data needs at the high level but also in the details. This includes working with data governance to put process in place to capture quality data, including context and metadata. Another aspect is working across teams to develop internal ontologies or adopting public ones, and data catalogues to make sure the meaning of the data is captured consistently.

Does Data Stewardship matters in R&D?

The discussion on this LinkedIn post show how much this subject is important. In R&D, very often, groups are working in semi isolation: their own remits, goals, procedures and nomenclature. The first victim of this is the data. It is important to have a “glue” to ensure a smooth flow of the data to maximise the value of the data. Data stewards with their position across teams have a key role to play in this. Applying the FAIR data principle at the organisation level but also at the individual team level is one of the function where data stewards can make a difference.

SALDS offers help and advice for your data stewardship needs thanks to our understanding of the R&D data from the bench all the way to the business level. Contact us to discuss your needs.

#datafirst

SALDS- R&D Digital Data Strategies Consulting