A growing complexity that is only getting greater with the growth of new IoT device and system providers.
Toby Cottam - Managing Director - Dyna-mo Instruments.
Without question one of the top medium to long term challenges for remote monitoring is the management of data, as the number of IoT device providers expands, with many seeing SaaS as an opportunity to generate passive income.
The scale of the challenge, when looking at a single monitoring project, is, in many cases, not that significant. One IoT provider may be able to capture all of the required parameters with their proprietary devices, or by integrating third party devices via digital or analogue nodes. The scale, however, dramatically increases when you consider the tens, if not hundreds, of monitoring projects a service provider will be supporting at any given point in time.
The possible complexities and required parameters for each project requiring new devices from additional providers to be adopted, and many of these devices are supported by their own proprietary comms, data servers and visualisation platforms.
The service providers choice then is to, limit device selection to only those that can be integrated with the primary IoT system adopted, or, consider all of the available devices and accept the relating additional data & platform costs, the disparate data storage and the management of more than one data visualisation platform.
How many data visualisation platforms does a service provider ideally want to work with both internally and to share with clients? Well I'd be astonished if the preference would be anything other than to work with ONE data storage location and ONE data visualisation platform.
It is possible to consolidate the captured data to a single data storage location and to feed this data to a single visualisation platform, BUT, not without duplication of cost and increased configuration complexity.
So what's the solution?
My honest answer is that at present there is no simple solution. Fundamentally, the commercial desire with IoT device providers to generate passive income from SaaS drives the development of device or system specific data visualisation platforms and therefore the associated platform and data costs. Similarly, the desire to enhance the presentation of device data leads to more and more complex post processing and algorithm development which, in order to minimise the power consumption and data transmission from remote IoT devices, is often run on the providers servers rather than edge processing on the devices themselves.
If we compare the challenge to similar circumstances in other markets there are some examples to be found where a cost effective and efficient solution has been developed.
Many other markets have found a way to bring together data, soft tools, visualisations platforms and apps accessible through a single portal, minimising duplication of cost and the necessity to continuously move data to a central store, and I have to think that this is possibly the best solution. But, to achieve this it would require the collaboration of a significant number of IoT device providers with a centre platform provider for this to expand to a point where it offers true value to service providers and mitigates a large proportion of the data management and visualisation complexity.
Software platforms that have delivered a similar integrated infrastructure are Quickbooks, HubSpot and IFTTT, enabling the connection of various software tools to a central platform, saving significant duplication of effort and data storage.
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In conclusion
In the short term I see the challenge only increasing as the falling cost of IoT device development and the growth in new providers will inevitably lead to more data storage locations and more data visualisation platforms that are primarily focussed on supporting proprietary devices.
There are data visualisation platforms for remote monitoring that provide for connection to multiple data stores, but the configuration of these connections is less than intuitive when compared to the alternative software examples I've given that serve other markets and applications, and, these monitoring data visualisation platforms also replace the proprietary data visualisation rather than pulling together the various platforms to ensure the value in each is realised.
There are many challenges facing the monitoring and instrumentation industry with the rapid advancement of IoT devices and monitoring techniques, and I don't see this particular challenge going away anytime soon. Without a fresh approach that attempts to bring together the many fantastic software tools that exist rather than replacing them, the pull for device provides in realising a passive revenue source through data management and visualisation platforms is too great.
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