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Navigating data-intensive innovation: The transition from research data management to research data governance

Factors leading to research data governance challenges across institutional functions.

The exponential surge in research data, coupled with the advent of cutting-edge computational tools, is ushering in an era of unprecedented opportunities for research performing organisations. However, with many still in the process of adopting basic research data management practices and support mechanisms, the current state of play is concerning. For example, it is becoming increasingly difficult for institutions to know what research data is being created by their researchers, where this is held, as well as whether this is actively being managed. Similarly, it is difficult for researchers themselves to identify and follow good practices in a rapidly shifting landscape.

Some research performing organisations are moving to tackle this set of emerging challenges by developing data governance mechanisms. Research data governance refers to the principles and protocols that guarantee the integrity, consistency, security and ethical management of research data throughout its existence across the organisation and beyond. This critically differs from research data management, as the focus shifts from individual projects or datasets to the way the organisation as a whole thinks and operates when it comes to research data.

Typical research data governance challenges across institutional functions.

In this article, I explore three key areas that research performing organisations should consider in their transition towards a comprehensive research data governance strategy, as well as highlighting a few roadblocks.

1. Craft well-defined policies and guidelines

Establishing clear and comprehensive policies and guidelines for research data is essential. For example, these should address aspects such as data ownership, access, sharing, storage, security and ethical considerations. Any policies and guidelines should set out a strategic organisational approach as well as including sufficient detail to be actionable: generic, high-level information is helpful to novices, but the organisation should also have and provide agreed-upon practices that address common data-related challenges. What to do with data when a researcher leaves the organisation is one of the most common difficulties, as it touches on all the above-mentioned aspects as well as on the capabilities of the digital infrastructure available.

When it comes to policies, research data management planning is perhaps the starting point, including because this is increasingly required by funders and therefore an obvious responsibility of research performing organisations. It is necessary to communicate to researchers that data management is not a perfunctory activity done to please a funder, but a key responsibility to consider proactively throughout the life of a project.

Importantly, well-defined policies and guidelines within the scope of research data governance are not only meant to mitigate risk – they have the potential to enable a broad range of positive impacts, such as:

  • Increasing levels of awareness of good practices and research integrity
  • Increasing uptake and usage of software and support services around research data
  • Showcasing leadership in data-intensive discovery and innovation
  • Increasing levels of trust from external partners, including government, the private sector and the general public
  • Streamlining data collection, storage and sharing processes
  • Ensuring compliance with ethical guidelines and legal regulations
  • Facilitating data discoverability and accessibility to promote collaboration and reuse

2. Clearly assign roles and responsibilities

It is tempting to think of research data as an operational topic, meaning something that only concerns researchers and those directly supporting them. This might partly be the case when it comes to research data management, but a robust approach to research data governance includes virtually all university stakeholders.

Senior leaders are responsible for ensuring that research data is part of the organisational narrative and strategic objectives. This is essential, as budgets, resources and policies are shaped around institutional priorities: as a result, it will be extremely difficult to secure funding for digital infrastructure unless this is seen as a core component of organisational culture.

A host of professional services can also be put in place to support the management and governance of research data:

  • A dedicated research data management team can help by providing guidance, support and training on all aspects of data management and use. Ideally, an extent of disciplinary tailoring is helpful, for example in the form of data stewards and/or data champions.
  • Librarians and information professionals contribute to research data management and governance by curating research data and by assisting researchers in locating and accessing data. They also play a significant role in advocating for open data sharing, open access and long-term preservation.
  • Ethics and compliance officers are responsible for ensuring that any research activity adheres to ethical guidelines and regulatory requirements. A security compliance team or function may be part of this, focusing on effective information security and data protection.

Alongside these professional services, the role of the information technology (IT) department is crucial. This underpins all efforts related to digital information, from the management of individual devices (e.g. laptops, desktops, workstations) all the way through to data transfer between different buildings and collaborative partners and organisation-wide backup solutions.

Finally, researchers and principal investigators are of course directly responsible for the way data is collected (or generated), used, stored, documented and shared, in line with institutional and funder and regulatory requirements. Building and rewarding a culture of good research data management is an important mechanism to ensure that the efforts of professional services are not disjointed from the practical reality of researcher behaviours.

To keep an eye on progress, research performing organisations may put in place committees or other governance structures focusing on data management, compliance and oversight, data security, infrastructure and more. This typically varies based on institutional size, but the overarching principle is valid in all cases: research data governance requires checks and balances to be impactful and appropriate to the local context.

3. Invest in robust digital infrastructure

Digital infrastructure complements the social infrastructure described above in delivering strong research data governance. Once again, it’s not all about mitigating legal or ethical risks and ensuring protection from unauthorised access, data breaches or potential loss. Robust and well governed research data management infrastructure is an enabler of research quality and impact, because it helps produce more accurate and reliable (e.g. reproducible or replicable) research outcomes that can be preserved and accessed in the long term. Furthermore, infrastructure serves as the foundation for data storage and retrieval, gaining even greater importance as research data volumes continue to expand.

Today, scalable and efficient solutions are fundamental, with cloud computing increasingly emerging as a popular choice to address the evolving demands of researchers, particularly when dealing with big data and employing techniques such as machine learning. In these cases, research performing organisations need to familiarise themselves with the three V’s of big data: Volume, Variety and Velocity.

Through a growing focus on the FAIR principles (findability, accessibility, interoperability, and reusability), research stakeholders are also pushing for enhanced data standardisation and interoperability. A well-designed organisational data management infrastructure promotes the use of standardised data formats and metadata schemas, making it easier to share and reuse research data across different projects and disciplines. This enhances collaboration and fosters a culture of open science within the university.

A central consideration in infrastructure development is cost-effectiveness. By investing strategically, long-term savings can be achieved through reduced operational expenses, minimised duplication of effort and optimized resource utilisation. However, obtaining approval for substantial investments in new IT assets or updating existing equipment can be challenging. Therefore, research performing organisations should recognise research data as a strategic priority, empowering senior leaders to approve expenditures that positively impact research data governance and elevate the efforts of researchers and professional services supporting them.

Challenges and roadblocks

The development and implementation of a data governance strategy is typically hindered by several constraints, which tend to vary based on the institutional context and range of disciplines served. The one barrier that most research performing organisations will experience is lack of awareness and understanding of the topic, which often goes hand-in-hand with limited (or no) availability of adequate expertise and skills.

Many researchers and administrators may not be fully aware of the importance and benefits of research data governance and lack a clear understanding of the specific policies, procedures and best practices involved in implementing an effective framework. This can result in resistance or reluctance to support research data governance initiatives. Other sources of resistance may include the fact that research cultures value autonomy and independence, which can make it challenging to promote a standardised data governance framework.

Training provision and awareness raising, including for professional services, are, therefore, key. The table below provides and an overview of the numerous areas that need addressing to foster the operationalisation of a research data governance strategy.

To mitigate the impact of these potential challenges and barriers, research performing organisations must engage stakeholders at all levels, from researchers to administrators, at any level of seniority and across the spectrum of disciplines.

Area Focus
Data management basics Basics of data organisation; documentation; storage; backup; archiving; sharing
Research integrity Data security; privacy; sensitive information; data encryption; access control; anonymisation techniques
Metadata and data standardisation Metadata schemas; interoperable data formats
Data quality and validation Data cleaning; error detection techniques; reproducible publication practices
Data analysis and visualisation Analysis and visualisation tools and software; with a focus on solutions approved by the organisation’s security compliance team
Ethics and compliance Discipline-specific ethical guidelines; national and other applicable regulatory requirements
Open science and data sharing Principles of open science; collaboration and transparency within the university and with collaborators; use of open data repositories; licensing; citation practices
Long-term preservation Data archiving; format migration; ongoing data accessibility; cold storage
Research data governance strategy Organisational approach to research data governance; including stakeholders; policies and guidelines

It is time to take action

Research data governance is a critical component of responsible research conduct in research performing organisations and helps ensure the responsible stewardship of research data throughout its lifecycle. Many research performing organisations have data management systems and practices that do not align with today’s need for robust research data governance. Of course, transitioning to a new and more comprehensive approach may sound daunting – not least because the process is time-consuming, costly and disruptive for the whole organisation.

However, research performing organisations should start prioritising and investing in research data governance frameworks that align with their mission, values and strategic goals. This will not only contribute to higher levels of research integrity and quality, but also help enhance the reputation and impact of their research activities. Now is the time to seize the transformative potential of robust research data governance, unlocking benefits for researchers, participants and society as a whole, and propelling scientific discovery to new heights.

Copyright © 2023 Andrea Chiarelli. Distributed under the terms of the Creative Commons Attribution 4.0 License.