451 Research authored an Impact Report recently which looked at IBM’s future for the Internet of Things (IoT). CEO Ginni Rometty has “disclosed 80,000 developers and 500 partners to create an IoT ecosystem leveraging the company’s Watson technology for natural-language processing.” It’s an interesting read looking at IBM’s commitment and ambitions in the IoT space – established IoT headquarters in Munich, billions in R&D, 750 related patents in IoT and analytics, The Cloud Foundation and commitment to spend $100m on startups and partners to accelerate its IoT ambitions.
The 451 Impact Report says that:
“IBM’s primary competition as a total solution provider to IoT is from the incumbent OT vendors such as Siemens, Schneider Electric and Rockwell Automation, which have legacy market share in the industrial applications that are the initial target of its Munich efforts.
It talks about competition from “full-stack IT powerhouses and integration shops like HP, Cisco and EMC, and hyper-cloud operators such as Microsoft and AWS” and that “IBM will find itself competing for developer mindshare against primarily Cisco and Intel, both of which have opened IoT development and innovation centers around the world.”
IoT Use Cases Could be Any Total Solution?
The one phrase that really caught my attention in this report is ‘primary competition as a total solution provider to IoT’. It got me thinking – what is a total solution provider to IoT? Let’s consider some IoT use cases – you can see a full list of great examples at Libelium.
Smart Cities – This includes things such as smart parking (the monitoring of parking spaces available in the city), traffic congestion (monitoring of vehicles and pedestrian levels to optimize driving and walking routes), structural health (monitoring of vibrations and material conditions in buildings, bridges and historical monuments), noise urban maps (sound monitoring in bar areas and centric zones in real time), waste management (detection of rubbish levels in containers to optimize the trash collection routes) or smart roads (intelligent highways with warning messages and diversions according to climate conditions and unexpected events like accidents or traffic jams).
Retail – This includes ‘things’ such as supply chain control (monitoring of storage conditions along the supply chain and product tracking for traceability purposes), NFC Payment (payment processing based in location or activity duration for public transport, gyms, theme parks, etc.), Intelligent Shopping Applications (getting advice in the point of sale according to customer habits, preferences, presence of allergic components for them or expiring dates) or Smart Product Management (control of rotation of products in shelves and warehouses to automate restocking processes).
Industrial Control – This includes M2M Applications (machine auto-diagnosis and assets control), Indoor Air Quality (monitoring of toxic gas and oxygen levels inside chemical plants to ensure workers and goods safety) or Temperature Monitoring (control of temperature inside industrial and medical fridges with sensitive merchandise) to name a few.
eHealth – Things such as Fall Detection (assistance for elderly or disabled people living independent), Medical Fridges (control of conditions inside freezers storing vaccines, medicines and organic elements or Sportsmen Care (vital signs monitoring in high performance centers and fields).
We could continue to go on, but I think the point is illustrated that there are a lot of things, but how do you quantify the total solution?
What is the Total Solution?
Is it the ‘thing’ that receives the data i.e. the trash container, the medical fridges? Is it the network that delivers that data (after all there is no IoT without the network), is it the back-end systems that can process that data in real-time and act on it? Is it the analytics that gives intelligence on how to improve the city or product management?
IoT is many solutions combined, but the two things that are prevalent throughout the front to back end systems is data and the network. Each one of these ‘things’ creates (to sound cliché) mountains of data. That data then needs to be sent to different systems. Chief Evangelist for Apache Cassandra said in a Wired article:
It is helpful to think about the data created by a device in three stages. Stage one is the initial creation, which takes place on the device, and then sent over the Internet. Stage two is how the central system collects and organizes that data. Stage three is the ongoing use of that data for the future.
For smart devices and sensors, each event can and will create data. This information can then be sent over the network back to the central application. At this point, one must decide which standard the data will be created in and how it will be sent over the network.
You then have the storage and analytics of that data which requires the ability to move that data or process it in potentially different languages to other systems. There are two things to pay close attention to:
- Data is key. What IoT really depends on is the structure of data, how it changes, which bits are important and which are less so. And what do customers care about? They care about the “now” and experiences that are as close to instant as possible.
- Since everyone is using the same network (i.e. the Internet) to exchange data, the company that overcomes the Internet’s many obstacles wins (obstacles include no control of available bandwidth, no control over intermittent network drop outs, , no control over network configuration and operation restrictions, no control of client capability).
An IoT Total Solution Needs a Reactive Data Layer
I would argue that a complete IoT solution is nothing without a Reactive Data Layer. A Reactive Data Layer (RDL) normalizes data from all systems and cloud applications to provide a single, living breathing data model that abstracts the transmission of that data between the RDL and consumers (the things) or producers. In the modern world, applications do not adhere to a typical client-server model – instead reactive applications are both consuming and producing data across multiple endpoints, at the same time.
That’s why to be innovative in IoT organizations need to add a Reactive Data Layer to their core technology stack. Just as a business intelligence layer provides single reference point for customer or reporting information, a Reactive Data Layer provides a single view of data from all systems and cloud applications, creating a living, breathing data model.
For companies like IBM looking to offer a total IoT solution, employing a Reactive Data Layer is a requirement. Learn more about a Reactive Data Layer.
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