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Internet of Laboratory Things – Coming Soon to a Lab Near You
The Internet of Laboratory Things – a disruptive set of technologies that is changing the way innovators are managing their labs.
When it comes to disruptive technologies, few have the capacity to change our world as profoundly as the Internet of Things (IoT). One example that’s been in the news lately and has the potential to come on line in the next few years is the concept of the self-driving car. These cars contain hundreds of different embedded sensors that collect data from their immediate environment in order to assure that you get to where you want to go safely and efficiently.
In the IoT, a “thing” is a physical device or machine with the ability to gather information or data from the world around it through some kind of sensor and transfer that data over a network, either locally or globally. Once assigned an IP address, these “smart” devices or things can transfer the data they gather and communicate with other devices without requiring human-to-human or human-to-computer interaction, thereby becoming part of the IoT. The list of smart devices already in use in our world is long and growing rapidly – fitness trackers, thermostats, lights, refrigerators, heart monitoring implants, and much more. Gartner estimates that by 2020, the IoT will include an astonishing 26 billion smart devices.
Advancements in micro-electronics, sensor technologies, wireless networks, software technologies and many other areas have helped to tear down silo walls between operational technology and information technology (IT), allowing the enormous amounts of data (big data) from the IoT to be analyzed for insights that are driving improvements in safety and efficiency across many different industries – agriculture, healthcare, automotive, energy, oil and gas, and more. Truly, we are in the midst of a revolution in connectivity. People are now talking about smart homes, smart grids, smart cities – but what about smart analytical laboratories? What are the opportunities and challenges involved in leveraging the IoT in modern analytical laboratories to improve operational safety, efficiency, accuracy and cost-effectiveness?
Potential Benefits of Leveraging IoT in the Laboratory
The analytical laboratory is in fact one of the best environments to utilize IoT compatible technologies. McKinsey & Company has identified 6 major benefits that flow from IoT:
- Tracking behavior for real-time marketing;
- Enhanced situational awareness;
- Sensor-driven decision analytics;
- Process optimization;
- Optimized resource consumption; and
- Instantaneous control and response in complex autonomous systems.
With the exception of “Tracking behavior for real-time marketing,” each of these points is applicable to the analytical laboratory environment.
Challenges of Leveraging IoT in the Laboratory
Of course, there are also many challenges to successful implementation of the Internet of Laboratory Things (IoLT) including cost, lack of integration, security, and industry knowledge hindering the journey to adoption.
Within the laboratory, IoT can also help predict some common resource usage such as water or heat. Companies can achieve cost saving and be environmental friendly by looking at these usages.
However, costs associated with establishing an technical environment to handle incoming data alone can be staggering. Even though “storage is cheap”, there is capital expenses typically associated with establishing the server farm including the data center floor space, the HVAC costs, the personnel costs associated with management, etc. Early adopters in other industries tended to be those with massive data centers. Many laboratories do not fit into that category.
Luckily, there are now technology partners that can offer this in a cloud environment, shifting the costs into a pay-as-you-go, operational expense. This is particularly inviting to the laboratory looking to kick the tires – performing a “proof of value” experience as a first step to the journey.
Integration of devices can be a challenge in the laboratory. Devices have far-ranging capabilities: from serial connected scales to siloed database applications and API-enabled applications. Bringing these together can be a challenge, but not a complete obstacle. Labs tend to be a bit ahead of manufacturing with respect to device integration / interaction capabilities. Lab measurement devices and their communication protocols have been in place for many years. However, in many cases, these instruments are islands of data – not fully integrated, and requiring manual transfer or rekeying of results. As devices continue to become more intelligent, the hurdles are lessened and the future is becoming more bright for integration.
Much like the platform for storing and managing IoLT data, integration has also moved to the cloud, which makes it more affordable for laboratories to integrate device data with systems that enrich the resulting data lake.
Privacy and security have become considerable issues to resolve as data moves out of siloed systems in the lab and into the cloud. As this happens, it is critical to ensure that “trust zones” are established and that appropriate governance is applied to protect confidential information. Adding security has traditionally implied performance degradation. To enforce who can access what, data is constantly decrypted and access rules are evaluated. Cloud providers have become increasingly security conscious over the past few years. As cloud environments have become more than just “someone else’s server”, providers have taken great steps to ensure protection from intrusion. Security is moving further down the stack – in some cases, directly into the silicon – to provide protection like never before. Further, much like there are tools that can answer questions you never thought to ask, tools exist today to monitor portions of the operation and its access that you never thought to monitor and provide dashboards and potential intrusion alerts.
Perhaps the greatest challenge to enabling the Internet of Laboratory Things is the applied industry knowledge in this technical area. Scientific knowledge is a key part of the puzzle. However, so is knowledge of the systems and devices providing the data. The next key piece of knowledge is how to integrate them together and overlay the technology to provide the benefits desired.
About the Author
|Dale Curtis Jr. is the President of Astrix Technology Group. For over 18 years, Mr. Curtis has built an impressive track record of leadership and success in high technology/scientific enterprise software sales, business development and service delivery. He has proven talent for driving innovative operational and marketing strategies, building successful teams, and rapidly developing new markets for start-up companies as well as multimillion-dollar global technology enterprises. Mr. Curtis holds a B.S. in Chemical Engineering from the University of Virginia and an M.B.A. from the Drexel University LeBow College of Business.|