What do the IoT and R&D Labs Have in Common? Not Much. Yet.

What most people think the research lab looks like.

The IoT — Internet of Things — is an omnipresent part of our lives, supporting everyday tasks across home, work, fitness and more. Distributed sensors, wireless networking and cloud computing enable us to control lights, monitor security systems, and manage entertainment and HVAC systems, all from our smart phones. In our work lives, smart building technologies sense how many people are in a conference room and automatically adjust lower the thermostat set point, dim smart windows to block out sunlight, and more. Along the way, these systems are gathering and using critical data that can be analyzed to optimize the systems and provide users with valuable insights.

But while these technologies have crept into almost every facet of our daily lives, they have been slow to make it to the laboratory. In fact, despite the hip and contemporary look of labs in the latest TV crime dramas, real-world labs look more like your college chemistry lab. Cringe. No neon lights and lots of manual data gathering. Yes, there are incredibly sophisticated pieces of equipment (mass spectrometers, PCRs, liquid handlers), but each piece of equipment is a veritable island. This is the next frontier for the IoT — the Lab. Because one of the things the IoT does best is to automate processes and unlock critical data, which will be transformative for science-based industries.

To say that data gathering, sharing and analysis in labs is highly manual is an understatement. Information is typically recorded or downloaded from a piece of equipment for analysis in a spreadsheet, while day-to-day equipment is usually monitored by a staff person walking around and using a probe (thermometer) to measure equipment temperature and then record that single data point in a log book. Sophisticated experiments are documented and tracked either in a lab notebook or electronic version, often with variability that isn’t captured or documented. The processes hardly resemble what we see on CSI, and the exorbitant costs to these approaches are illustrated in the current state of life sciences R&D.

Life sciences R&D spending in the U.S. is estimated to be $56 billion per year. Of that, a full 50% is spent on work that is irreproducible, the industry term for experimental work that can’t be successfully repeated by another person or team following the same documented procedure. That’s $28 billion per year wasted, and much of the issue is a data problem: insufficient data, data trapped on legacy equipment, data corrupted by manual, error-prone processes, data silos that don’t integrate into a bigger view, rudimentary data analysis capabilities, and so on. That is where the IoT-enabled lab is poised to transform science with more data, better data, and accessible data.

Enter the Smart Lab

Smart Lab technologies optimize the IoT stack for the rigorous requirements of science-based environments and change the way we gather, understand and utilize information. The foundational concept of the Smart Lab is connectivity — either deploying devices with intrinsic networking capabilities or adding connected capabilities to existing legacy devices. In all cases, the goal is the seamless gathering of critical information.

The modern lab generates staggering amounts of data, and yet misses other data sets, making collecting and consolidating that information in a uniform, interchangeable fashion one of the first steps to modernizing the front lines of science. By gathering data from existing equipment and eliminating data gaps — places where data isn’t gathered and should be (equipment, process, etc.), Smart Lab technologies complete the data picture.

Another core aspect of the Smart Lab is the implementation of open, standards-based systems that are designed to facilitate the seamless flow of data between data sources and those professionals who need access to it. Ease of access is critical to unlocking data’s value to the organization, and reducing data silos that plague so many labs is key.

For the modern scientific professional, Smart Lab technologies hold promises both big and small. At the macro level, the Smart Lab will accelerate discovery and improve operational efficiency, reducing time to market for critical therapies and improving the ROI on R&D spend. And, on the day-to-day level, it will free teams to invest precious resources where they can have the most impact. Just as data transformed manufacturing practices a few decades ago, laying the groundwork for Six Sigma and other standards, the Smart Lab is the stepping stone to transforming research. After all, if you can monitor your home remotely, why not your lab? And if you can use performance data to optimize your personal fitness, why wouldn’t you want to leverage data to improve research outcomes?

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