This time last year, scientists had only just sequenced the novel coronavirus genome. One year and billions of data points later, commercially approved vaccines are in distribution.

The blistering pace of this global research and development effort demonstrates how the lab environment has changed in recent years. This is especially true for contract labs, beholden as they are to the needs and expectations of clients who themselves are in a race to turn good ideas into commercial success stories. Speed is important. Speed controlled and shaped by data-driven business intelligence is mission-critical. Whether working on the pandemic response or not, this is a universal truth in today’s pharma manufacturing landscape, and labs are feeling the pressure.

Data-driven business intelligence doesn’t just happen. And it certainly doesn’t just happen when labs are building the plane while flying it, accumulating huge volumes of data without the means—or the time—to monitor its quality, let alone put it to use. That’s why the concept of “digital transformation” has received so much airtime. Core to the trending concept of Pharma 4.0, it’s touted as a remedy for the pain felt by organizations that are overburdened by big data and underserved by standalone IT solutions.

The term “digital transformation” can be misleading, though. It suggests that we’re talking about a move from the clipboard to the computer screen, which is only one component of a much bigger shift. True, some labs still operate paper-based management systems. For them, the first step is digitization in its most literal sense. But even labs that are fully supported by automated digital systems may well have a long way to go before they’re able to put their data to work for them, using it to drive better scientific and business outcomes.

That is the promised land of full digital transformation in the contract lab, and thanks to a new generation of intelligent data solutions, it’s more attainable than ever before.

Digital transformation inside the contract lab
The lab is a data-intensive environment
Before unlocking its full potential, labs must contend with the lifeblood that makes a digital ecosystem work: data. Collecting it, structuring it, and assessing its quality can consume a huge amount of effort and expense, especially in contract labs burdened by the complexities and shifting dynamics of multiple clients.

To help ease this burden, many labs are replacing manual data collection systems with interconnected and automated lab equipment, collectively known as the Industrial Internet of Things (IIoT). By shifting the burden of data collection from operators to instruments, these labs improve the integrity of their data while removing potential process bottlenecks—but these improvements come with a cost. The easier it is to indiscriminately gather data by hoovering it up from automated instrument sensors, the more data you’ll have to deal with, which only replaces one kind of burden with another.

What I’m describing is the distinction between automation and true digitization. Automation is the fork carrying a morsel of food to your mouth; digitization is how your body turns those calories into fuel to keep you moving forward. In other words, it doesn’t matter how much data you collect until you have the right tools to metabolize it into action.

Point solutions aren’t keeping up
Both global contract pharma companies and smaller-scale, digitally native startups are likely to reach an inflection point at which they realize that the tools which got them this far will not get them any further. Their legacy data management practices and systems, adequate for yesterday’s needs, struggle against the pressures of new competition, new client dynamics, new cyber security threats, and new data challenges.

For larger companies, that point often comes when a series of mergers and acquisitions results in a global network of labs whose incompatible digital systems prevent operational harmony. This leads to silos within the organization, which slows productivity at the bench and hampers decision-making at the management level.

Smaller companies reach this point when client demand outgrows capacity, and when competitive pressure and increased regulatory scrutiny bear down with intensifying force. Their legacy systems, never designed for this new scale of operations, reveal their limitations; eventually, the cost of maintaining the status quo is far greater than the cost of adopting a better solution, and they initiate the process of transformation.

From standalone operations to a harmonized enterprise
In a harmonized enterprise, an end-to-end digital ecosystem collects data from across the business and makes it available and readable to those who need it—without requiring data modelling expertise or IT support. As a result, good ideas and valuable insights flourish into action instead of meeting a dead end inside of operational silos.

Standalone data tools can be rigged together to approximate a harmonized system, but they show their cracks at scale when huge volumes of data are pushed and squeezed between systems that were never designed to work together. Data loses quality and integrity in the process, and the threat of a cyber attack grows with every makeshift connection between disparate systems. When labs embrace a single, standardized platform designed to communicate in a common language with other enterprise systems, data quality improves, and so does the infrastructure designed to keep it secure. This gives company leaders a fulsome view of their operations, from the R&D lab right through manufacturing, packaging, distribution, and consumer reporting. It’s a game-changing shift. If you’ve ever stared at a “Magic Eye” pattern, you know what I mean: the moment a detailed, dimensional image emerges from all of that visual clutter, your perspective changes completely. 

To realize this level of data harmony and security, many labs are investing in cloud-based solutions. Certain technology partners take the ease of cloud-hosted platform solutions even further by offering SaaS-based data management solutions, validated for the pharmaceutical lab. This frees IT departments from the burden of hosting, maintaining and validating on-premise platforms, and it affords access to all the modern tools and security features available in the cloud.

However they get there, companies who are digitized to the core, with harmonized data flowing between functions and up through the decision-making suite, can count on numerous competitive advantages in the contract manufacturing space, including:  

• Better data integrity: Today’s modern enterprise-wide data management systems are designed with world-class security and data integrity features.
• Stronger competitive positioning: With a data system designed for quality and risk management, these labs are always ready for a successful customer audit or a business development opportunity.
• Accelerated regulatory outcomes: Demonstrating compliance and risk management is much easier when data is consolidated, standardized, and readily accessible.

Once they get to this stage in their digital transformation experience, contract labs—and the companies that they serve—know how much data they have, where it is, and what it means. At that point, they’re ready for the next frontier in their lab’s digital transformation.

Predictive analytics: turning lab data into business intelligence
In the same way that a smart GPS is so much more than a map on a screen—it’s a real-time recommendations engine, helping you find the best route from here to there—the ultimate application of a harmonized data management system isn’t just about creating a digital environment. It’s about how companies use that digital environment to predict obstacles, solve problems, and consider alternative ways forward.

To do that, they need an advanced analytics engine capable of transforming LIMS data into meaningful insights so that lab managers, analysts, researchers, and others can run queries, investigate “what if” scenarios, and find synergies between ancillary systems inside the company.

The best of these data analytics platforms feature statistical and machine learning capabilities. Companies can train the system to analyze historic trends and behaviors in order to predict possible outcomes. With these predictive insights backing them, it’s as though corporate decision-makers graduate from squinting through a periscope to interacting with a high-definition, 360-degree interactive view of the world around them.

What does this change for contract labs? Everything.

• Proactively reduce or eliminate downtime: By analyzing historical trends related to sample volumes, operator behavior, and other factors, an analytics platform can accurately predict instrument failure or out-of-spec conditions and recommend preventative action.
• Simplify and accelerate research: With an analytics platform helping to identify R&D goals and uncover synergies between a contract lab and an external client or partner, innovation isn’t painful or burdensome—it simply happens.
• Optimize scheduling and maximize capacity: When a contract lab uses data analytics to understand and predict demand, it means they’re always ready to meet clients’ shifting needs. Lab managers can rely on their analytics engine to plan optimal workloads, automate scheduling, proactively manage consumables and reagents, and much more.

For those still relying on manual, paper-based lab operations, these rewards may seem a long way off. But as I said, digital transformation is a spectrum, not a moment, and every company must start where they are and proceed from there. What’s certain is that those first steps are absolutely vital to long-term business health, especially for CDMOs who face tremendous pressure to move faster despite increasing complexity. Speed, agility, and above all, smart decision-making: these are the qualities that today’s lab environments need to thrive, and they’re the rewards promised by a digitally intelligent ecosystem. 

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Prior to joining LabVantage in 2009, Bob Voelkner was Americas Sales Manager for the LIMS division of Applied Biosystems. Previously, he spent seven years in a number of sales leadership positions at Thermo Fisher Scientific’s Informatics and Services division. He started his career at Beckman Coulter, Inc. as a senior software developer working on LIMS and chromatography data systems, progressing to Director of Global Sales and Marketing of its Laboratory Automation Operations division.