So long, 2024. Hello… breakout year for Digital CMC?
In today’s market, digital transformation isn’t just a buzzword—it’s a competitive necessity. As artificial intelligence and data-driven methodologies reshape drug development, organizations that successfully embrace digital innovation are already unlocking a range of operational benefits.
As exciting as that is to see, though, it’s all too easy to think of it as a triumph of technology over traditional methods and processes.Â
To some degree, it is, of course: By now, nearly every square inch of the drug development landscape has been christened with some form of enabling software. But before we let innovation do all the talking, it’s just as important to remember that implementing any of these tools is merely the first step.Â
Look closer at the drug developers that are successfully sustaining transformation, and you’ll quickly see the trend that really matters. They’re not just deploying a stack of innovative technologies, though that’s certainly part of the process: they’re cultivating a business culture where digital thinking and data-centric workflows become second nature.
So how did they do it? Glad you asked. When it comes to embedding Digital CMC best practices, our QbDVision team has front-row seats to what works, what doesn’t, and what really makes transformation stick. Let’s take a look at a few examples.
“But the pilot went so well!”: Why successful deployments don’t turn into durable change
When it comes to changing how a business operates, any experienced leader can tell you there’s one constant: Success comes down to culture. Whether that business is shifting from documents to data frameworks, or from horse to ICEs, the durability of any changes is 100% dependent on employees’ comfort with that change and leaders’ comfort with navigating it at organizational scale.Â
The exact technology involved is almost irrelevant. When we’re talking about transformation, technology only enables. Culture sustains.
That’s why one of the most persistent barriers to digital transformation remains surprisingly basic: simple, human resistance to change, the timeless refrain of “this is how we’ve always done it.” Every industry is susceptible to this kind of inertia, but we’ve got it particularly bad in drug development—a sector where all too many stakeholders see “change” and “compliance risk” as synonymous.Â
To overcome this fundamental challenge, transformation leaders need to go a level deeper: They have to ensure that every team member clearly understands not just what needs to change, but why. Employees need to see the direct connection between digital transformation initiatives and tangible benefits—both for the organization and their daily work lives. Without this foundational shared understanding, even the most sophisticated digital tools risk becoming expensive shelf-ware—or the latest season of “Pilot Purgatory.”
So how can smart leaders build that understanding? Here are 5 valuable places to start.
Whether a business is shifting from documents to data frameworks, or from horse to ICEs, the durability of any changes is 100% dependent on employees’ comfort with that change and leaders’ comfort with navigating it at organizational scale. The exact technology involved is almost irrelevant. When we’re talking about transformation, technology enables and culture sustains.
1. Fundamentals: Make sure everyone understands the difference between creating knowledge & managing it
At its core, real digital transformation is about learning to live and breathe data. And for many drug development professionals, especially scientists and engineers, that often brings out versions of the same response: “But we already live and breathe data.”
Well sure. But as they say: “yes… but.”
True, many drug development professionals work with experimental data daily. To get everyone on the same transformation page, the crucial gap to bridge is between working with experimental datasets and managing data as an organizational asset that drives innovation and efficiency. And that means clarifying the distinction between several key concepts:
- Raw experimental data and its immediate applications within a CMC program
- Enterprise data that enables automation, prediction, and intelligence for a drug development organization
- Knowledge management processes required to turn #1 into #2.
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For drug developers, failing to establish these distinctions is one of the most common stumbling blocks in digital transformation. All too often, CMC contributors work diligently under the assumption that generating a dataset, tabulating it, and storing it on SharePoint is managing organizational knowledge. But while scientists and engineers may excel at those tasks, successful transformation depends on helping those experts understand that real knowledge management means something fundamentally different.
To help build that understanding, focus on the key activities and principles that distinguish these ideas. For CMC contributors, familiar data production processes typically involve:
- Conducting experiments and documenting their results
- Creating datasets for specific projects our milestones
- Assessing implications for specific process steps or risk analysis
- And most crucially, working with their immediate team or functional unit to generate data specific to their assigned task(s)
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Knowledge management, by contrast, focuses on:
- Contextualizing data within broader development processes
- Identifying patterns and relationships across multiple studies
- Creating searchable, accessible repositories of institutional learning
- Enabling data reuse and cross-project insights
- Working as an organization to maximize the utility and value of the business’s data resources
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And there’s the secret: data production is often an individual or unit-specific activity, while knowledge management requires organizational thinking. It means considering and valuing how today’s data might inform tomorrow’s decisions—not just for one’s own team, but in different departments or projects.Â
Flipping this switch for CMC contributors is often the first step in helping them accept why traditional data exchange and storage methods (looking at you, Adobe, Outlook, and Sharepoint) may not support the organization’s broader digital transformation goals. It also helps employees appreciate why their expertise with data—critical as it remains—is just one step in unlocking the full value of the data they create.
Modern platforms like QbDVision can help bridge this gap by automatically capturing the context and relationships that transform raw data into organizational knowledge. But that’s not the only benefit. A platform like QbDVision can also help you put another key piece of the transformation puzzle in place: organizational data standards.
2. Standardization: Make sure everyone’s calling everything the same thing
Stop me if you’ve heard this one before: Your CMC program is burning dozens of hours harmonizing datasets because each unit has their own name for every resource.Â
Now if it’s taking your human experts that long to parse everything together, it’s not hard to imagine how well digital solutions—dependent as they are on high-quality structured data—will fare with the same information. So it’s no surprise that many transformation initiatives and new tool deployments run aground on the shores of heterogenous taxonomies, inconsistent labeling methods, and variable nomenclature. Or none at all.
To prevent or overcome this challenge, standardization is a must—or as our Director of Product Management likes to put it, “What you call things matters.” It’s the only way to ensure that organizational data can flow seamlessly between teams and into solutions that can use it to generate time-saving content, proactive insights, or valuable predictions.Â
To effectively standardize CMC data resources, there are a few key fundamentals to prioritize:
- A cohesive data model that supports cross-team collaboration and knowledge sharing (FAIR is a great example)
- Clear naming conventions that make data easily searchable and accessible
- Comprehensive documentation standards that preserve context and enable future use
- Metadata protocols that capture crucial experimental conditions and methodologies
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It sounds like a lot of dimensions to manage, but platforms like QbDVision can make this fairly simple. Standards-aligned best practices are already embedded directly in the product’s workflows, making adoption as simple as entering data into the platform—and more natural and consistent across the organization.
But let’s say you’re going to use a solution like QbDVision to start producing standardized, structured data resources across your organization. That sounds fantastic—unless you’re one of the MANY white-collar workers who’ve heard the term “high-quality structured data” and directly equate it with AI-powered layoffs.
Let’s talk about how we get them on board too.
Standardization is a must—or as our Director of Product Management likes to put it, “What you call things matters.” It’s the only way to ensure that organizational data can flow seamlessly between teams and into solutions that can use it to generate time-saving content, proactive insights, or valuable predictions.
3. Reducing fear: Make sure you address the AI elephant in the room
Disruptive innovation is notorious for, well, disrupting people’s career tracks—and we’re far from immune from that in drug development.Â
It’s already been a pretty rough stretch for the industry’s workforce. Now, perfectly rational fears about the impact of AI can amplify resistance to adopting new data-centric ways of working. AI-enabled solutions have tremendous potential for the industry, of course, but that’s cold comfort to experts whose human skills may be at risk of replacement by this new technology.
As AI becomes increasingly prevalent in drug development, CMC leaders will need to be ready to get ahead of the very reasonable concerns their workforce may have about these technologies—including deep doubts about the “real reason” a workforce is being asked to accept them. Even highly skilled professionals may feel anxious about their future role in an increasingly automated environment.
Smart leaders will tackle these concerns head-on, showing how digital tools enhance rather than replace human capabilities. When taking this step, it’s important to showcase specific examples of how automation and AI free up valuable time for higher-order thinking, strategic planning, and creative problem-solving —activities where human expertise remains irreplaceable.
4. Incentives: Make sure everyone knows what’s in it for them
Business transformation may seem like it’s all about embedding digital technologies and data-centric best practices, but it’s still a very human-dependent process. And humans need a reason to do things—one that directly benefits them, and not just “the organization.”
To create a self-sustaining culture of digital excellence, meaningful adoption incentives are key. Even the most versatile and adaptable professionals will want a clear WIIFM—”what’s in it for me”—to change the way they work. In fact, “A Players” often require the biggest incentives. They’re already doing great doing it how they’ve always done it, so they frequently need very good reasons to mess with their own success.Â
So what could those look like? They might include:
- Career advancement opportunities tied to digital proficiency
- Recognition programs that highlight successful digital initiatives
- Publishing opportunities for innovative digital applications
- Special project assignments that leverage new digital capabilities
- Podium opportunities focused on digital transformation
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Whatever carrots you decide to offer, make sure they’re clearly and explicitly tied to the success of your transformation initiatives. The rewards themselves can take many forms: the most important thing is that your workforce feels like they have something to personally gain from going along with big shifts in the organization.
5. Leadership: Make sure everyone knows they don’t need to figure this out themselves
This final foundation of a strong digital culture should be pretty clear by now: To achieve durable transformation, employees need to know there’s a strong hand at the wheel of this new evolution.
The key distinction here: “leadership” doesn’t (just) mean hiring a new C-suite role to represent your digital initiatives, though that can be a valuable move. But equally and perhaps even more important, successful transformation takes frontline digital champions who can provide peer support and mentorship to their colleagues.
These champions can be “digitally native” younger employees, seasoned mid-levels who’ve experienced the evolution of today’s technology, or simply curious employees with a natural interest in transformation. Whatever their background, they can play an invaluable role in helping coworkers navigate new tools and processes while demonstrating the practical benefits of digital adoption.Â
Identify and empower these champions early, give them resources and authority, and more than anything—see #4—recognize and reward their contributions to the transformation effort.
Building culture takes time. So there’s no better time to start than now.
As anyone who’s led a foundering digital pilot can tell you, transformation is a race that belongs to the smart, steady, and thoughtful. Shifting an organizational culture can be a time-consuming feat, but following best practices like these can give you a big head start.
Just remember: technology makes it feasible, but culture makes it happen. By focusing on building a strong digital culture alongside technical implementation, your organization can ensure its digital transformation efforts deliver lasting value, a strong market position, and continued success in an increasingly digital future.
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