Universities need operating models and IT systems created from their researchers’ point of view
Joanne Bishenden, education expert, and Kathryn Lewis, IT strategy and sourcing expert at PA Consulting, discuss how universities must adapt their research operating models to accomodate for the wealth of data.
Access to data is greater than it has ever been, and academics are harnessing data that are unprecedented in size and scope for their research. The opportunities – and challenges – that presents for researchers are many and well known, from formulating the right research questions to deciding which data to gather. What’s sometimes overlooked is that universities must also adapt their research operating models to accommodate this wealth of data.
Researchers don’t always have the capabilities they need to manage and curate data effectively, says Joanne Bishenden, higher education expert at PA Consulting. “A lot of research operating models are focused on the process of supporting the research lifecycle,” she explains. “That is important, but problems arise when the process determines how data are managed, rather than the capabilities you need to manage data in the future. Then you're going to get yourself stuck in a corner quite quickly, when you can't respond to something that will inevitably evolve quicker than your processes can.”
Universities should instead focus on providing the right capabilities for data management throughout the research lifecycle, says Bishenden. In the early stages, that means ensuring procurement systems are set up to buy data. She says many universities’ systems are only designed to buy goods and services. “Data aren’t either of those; it’s a solution. Those practical elements of getting access to data prevents some universities from even bidding for or proposing research in the first place.”
Ethical approvals present another hurdle. Universities must be able to respond to new legislation, or even changes to the data itself, that emerge over long-term studies. Bishenden says: “Just think how quickly data evolve, especially in bigger studies over the course of years: do the permissions researchers have at the start of the programme apply to the end of the programme – or to the data that they create or manipulate during the course of the research? Is there agility built into the research operating model that allows for the changing of data throughout the process?”
When developing these capabilities, universities need to strengthen their central governance to ensure research operating models can adapt to changes when they arise, says Kathryn Lewis, IT strategy and sourcing expert at PA. If they don’t, research that relies on that data could stall. “This isn't a quick fix; a number of changes need to take place at the strategic level, the governance level, and the day-to-day operation level,” she says.
As part of this multi-level approach, universities must develop their IT services to accommodate ever-growing volume of data used in research, Lewis says. Using cloud services to augment universities’ high-performance computing networks, for example, and “provides flexibility for some academics that may find the current university services unsuitable for their specific need”.
This means universities don’t have to pay to run every service they need in a data centre, and buying in cloud services centrally can generate opportunity as well as “massive savings”, Lewis notes. Now, too often academics purchase pay-as-you-go plans for services that could be bought more competitively on a larger scale. That is why IT departments must also embark on an “education campaign” to ensure researchers know that accessing centralised services can help them get the best value out of their research grant.
Lewis says that it’s critical that any processes universities adopt are “PI-centric”. “You have to look at it from the researcher’s point of view, not just necessarily the cost saving points of view, and make an appropriate case for change,” she explains.
“Academics may not want more bureaucracy, so universities need to think about creating a focus point that doesn’t get in the way, but that is thinking about these issues,” Lewis says. More than anything else, academics want a good environment for their research. Universities that can’t support their researchers with the capabilities they need to manage their data effectively will lose out to those that can. “Devotion to the research often trumps loyalty to the overall university processes,” says Lewis.
Ultimately, having effective, agile research operating models in place puts universities at a competitive advantage, she says. “First, they'll be more compelling in their proposals and grant applications. From there will come funding, more access to more research, you'll attract the better academics, and it becomes a virtuous circle.”