Why It Is Vital to Help IT and Scientists Collaborate

February 23, 2024

Contributed Commentary by Laura Swift, IDBS

February 23, 2024 | In the race to unlock the power of artificial intelligence (AI) for business, it is tempting to focus on the need to strategically leverage data—indeed, this is a message that I reinforce constantly when onboarding new clients. But there’s a step that must happen first. It sounds obvious but is often overlooked: to leverage data well, companies first have to better leverage their teams. 

With the swift growth in AI, the way that data is captured is of the utmost importance. The requirement for scientists to capture data electronically is not new. But for data to be useful for AI, it must be well contextualized and easily accessible across systems and processes. Those added hurdles mean more technical expertise and collaboration is needed. 

To better their data, companies that are ahead of the curve are first figuring out how to best use their people. More organizations are placing individuals with extensive IT backgrounds much closer to their researchers. Where researchers were previously treated as customers of an IT service group, the trend now is to form a single cross-functional team with a unified goal of digital transformation: recognizing data as the most valuable tool for decision making, and prioritizing data management accordingly. This approach has the potential to significantly speed digital transformation. 

A few years ago, most of the teams I spoke with in my role as a data management consultant for a BioPharma software provider faced a common staffing challenge: business and IT teams had competing priorities, with a real impact on project timelines. A business unit, for example, might want to digitally integrate four new assays; the IT team might need to deal with a migration away from a legacy system.  

Previously, IT was prioritizing the assay projects around the migration: in their mind, the big priority for the year. Assay work might get scheduled for later in the year with a set block of time. If the assays took longer than scheduled, additional hours would need to fit around other IT priorities. This approach might get the software migration done on time and on budget, but the assay work would inevitably be delayed. This is a missed opportunity for the business because it means going without assay data that could serve other digital transformation initiatives, including the foundation for future AI work. 

To work around this, business units outsourced what they could: software providers, for example, can create assay templates. But some work, like connecting instruments and data sources, requires internal IT support. To add to the confusion, it was not always clear which team should pay for the IT time invested into the assay projects. 

Now, however, the generative AI boom is putting digital transformation on executives’ radars. AI and machine learning (ML) have long been on the horizon within certain teams. But now, they feel both more urgent and tantalizingly within reach. Perhaps because of renewed executive-level vision and support, teams are now re-organizing to tackle their data challenges more effectively.  

In the new approach—which we have recently seen play out in similar ways across multiple organizations—leaders start by assembling a strategic team. They pull together a cross-functional working group of bench scientists, executives, data scientists and IT experts to create a roadmap for digital transformation. 

Then, they shake up their org charts by putting IT expertise closer to research. This can look like embedding a business analyst with a technical background on the business team and giving that person a dedicated budget for IT staff time. They can pull in IT resources as needed, essentially creating a functional IT team designed to serve the business. 

This means that instead of competing with IT projects for staff time, the business has time allocated throughout the year from the get-go. It is clear which budgets should pay for which projects; it is also clear that there is time available. As a result, business priorities may take the same number of hours, but they are delivered more quickly. If they take less time than expected, hours can be allocated to the next business need, instead of reallocated back to IT projects. 

In addition to speeding up top priority deliverables, the organizations I work with are reporting other wins as well. First, ticket resolution time speeds up: dedicated IT hours mean that problems can be resolved the same day, with clear communication. In the past, researchers often resorted to paper as a backup while waiting for a response; now, this happens less frequently, improving both researcher morale and long-term data quality.  

Cross functional teams are also bridging knowledge gaps. Scientists are more likely to ask for data support when they know they have a dedicated resource; when they do, an embedded team member can take the time to understand pain points and suggest additional solutions or automations that scientists may not have known to ask for. 

Better leveraging people sets the foundation for the kind of rigorous data management needed to unlock big AI wins, hopefully soon. It expedites the implementation of critical data capture solutions for structurization, automation and availability. The organizations that prioritize these restructurings will be best poised for the digital transformation ahead. But they will also see incremental wins along the way.  

We are seeing these restructurings play out across the industry. This kind of org chart has always been a no-brainer; now it is a no-brainer with political will and momentum.  

Laura Swift is Customer Success Manager at IDBS. Laura has spent the last 16 years in the life science industry focusing on software solutions for scientists. Currently, Laura focuses on delivering desired outcomes to customers who are driving to bring life-saving drugs to market faster. She can be reached at lswift@idbs.com