Data Connectivity with IoT drives new value for business

You may have heard the question, “Is data the new oil?” It even made its way into Forbes nearly two years ago. Well, is it? In the context of creating new business value from the Internet of Things (IoT), the answer today is both yes and no.

Yes, data contains huge potential value and, of course, it is much more plentiful and accessible than oil (and about to become much, much more plentiful). However, it is simply raw material that needs to be delivered to the right place at the right time and “refined” there (by applications) to create new business value, e.g., additional revenue streams (services as well as products), resource optimization (for both capital and human assets) and environmental benefits (waste reduction, energy efficiency, etc.).

There is little new about “islands of automation.” Data has been “refined” for decades to produce operational (OT) and corporate (IT) value at the tactical level. However, the IoT offers the potential for completely new levels of business value by providing a corporate data-connectivity backbone to deliver the right data to the right place at the right time, enterprise-wide and inter-enterprise.

The IoT can thus be applied to:

  • Liberate valuable data from legacy and new sub-systems (via gateways)
  • Directly or indirectly add new connected edge devices and machines (new Things as data sources)
  • Provide global-scale connectivity at reasonable cost (via the Internet)
  • Support new application deployment and analytics anywhere in the system (e.g., on devices, gateways, enterprise systems, cloud services, mobile)
  • Generate new insights and business and societal value from these distributed and instantly accessible applications and analyses

Data connectivity for under-explored valuable data

Tactical OT and IT systems obviously add value to enterprises and have provided good solutions in areas from process control to SCADA to ERP to corporate payroll since at least the 1970s. These self-contained applications provide a good ROI and solve real operational problems, but they also tend to be domain specific, often utilize proprietary technologies and lock their data into “vertical stovepipes.”

As such, they do a good job but in a limited way. They do not fully exploit the potential of the data they generate since they do not liberate that data for sharing and analysis wherever in the enterprise new insights and value can be generated. For example, they do not support distributed analytics, cross-domain integration or global-scale data access.

And as enterprises deploy literally billions of new connected Things during the next few years, this problem (of underexploited valuable data) will become dramatically worse unless a new data-connectivity approach is taken.

Read the full article at www.sandhill.com

Unleashing Clinical Measurements

Medical devices are an essential element of modern medicine as they provide accurate clinical measurements such as oxygen saturation, blood pressure and temperature, x-ray and ultrasound imaging, as well as automatically administer intravenous medications, and provide support of critical life functions. In spite of the advances toward improving medical devices’ accuracy, robustness and reducing their form factor, very little has been done to pursue interoperability – most medical devices communicate their measurements through proprietary data formats and protocols thus hindering the integration with other medical systems.

This lack of seamless integration has several negative clinical implications. As an example, the use of a single clinical measurement – oxygen saturation – makes it harder than it should to detect morphine narcolepsy false positives. As the oxygen saturation measurement is very sensible to the proper positioning of the probe, several false positive can be raised in patients that are restless, perhaps due to post-operatory pain.

If multiple clinical measurements, such as breath per seconds, oxygen saturation and perhaps heartbeats, were fused together to detect morphine narcolepsy, then the number of false positives could be dramatically reduced. In addition, the actual insurgence of a narcolepsy could be detected earlier.

Ideally, medical devices should make available their measurements using a standard data model and through an open and standard protocol. The good news is that we have been working for the past year with the MDPnP (Medical Device Plug-and-Play) – a consortium of hospitals, research centers and medical device manufacturers – to explore the use of the OMG Data Distribution Service as the standard for sharing clinical data. In addition, we along with a number of the MDPnP participants have been involved with the SmartAmerica Challenge, a White House Presidential Innovation Fellows project designed to showcase Internet of Things frameworks and benefits for a variety of environments including healthcare.

DDS enables seamless, efficient and secure information sharing across any device and at any scale. As a result, once the clinical data is made available over DDS, it can be consumed virtually anywhere, on anything, and by anybody who has the proper access rights. The simple fact of making data available through DDS enables a series of use cases that are very valuable in supporting both doctors as well as patients.

Read: Applying the Data Distribution Service in an IoT Healthcare System

As an example, if we consider the image below, we see how a DDS-based platform, namely PrismTech’s Vortex, is currently being used to allow doctors to access patient data from anywhere as well as non-hospitalized patient to be continuously monitored.

Specifically, in this use case DDS makes it possible for doctors to enter a hospital room and discover the devices that are available. Doctors can then select one or more devices from which they would like to see live measurements. In case the doctors need to discuss the live data with colleagues from another hospital, the clinical data will be shipped in real-time to the remote doctors on whichever devices they have at hand–a mobile, a tablet or a notebook. In addition, data is continuously streamed to a private cloud where on-line as well as off-line analytics are executed on the various medical measurements.

Likewise, mobile personal medical devices can also make their data available seamlessly to doctors as well as to analytics applications. For instance, consider an elderly patient suffering from dyspnea. This patient can be continuously monitored while staying comfortably at home thanks to the use of a mobile oxymeter. This oxymeter would be sending data to the hospital’s private cloud via 3G/4G or WiFi where analytics applications interpret the data and respond as necessary. For example, if it’s detected that the patient needs some oxygen, a notification can go out to the patient while an alert is sent the doctor.

The beauty of DDS is that each of these use cases is seamlessly supported by its core abstraction: ubiquitous data sharing. Additionally, DDS supports for data modeling and QoS facilitates the definition of common data models for medical devices. The combination of standard data models and interoperable protocol are key elements to enable medical devices interoperability.

If you want to learn more about the DDS standard you can refer to the OMG website where you can find the specification or take a look at the educational material available on SlideShare.