Internet of Things Landscape Beyond Things

As I am looking at the different facets of the Internet of Things, I realize that there are a million ways to slice and dice it’s different components. It is clearly a complex ecosystem and the amount of buzz generated around IoT is a clear demonstration of that fact. Similarly to my previous post around the IoT protocols stack I wanted to attempt to draft a view of the landscape but this time beyond the things and communication layers. I wanted to have a focus around the application side of things where I believe most of the value lays. So let’s show that. Here is my view of the IoT landscape and I’ll talk about the different bricks right after:

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Following a strong message pitched by Salesforce.com around the fact that behind every device is a customer, this is where I started: The connected customers on the left.

It all starts with connected devices, and you will find in there chip manufacturing, and connection protocols like Bluetooth, wifi, etc.. Not the focus of this blog but definitely important. The following bucket to look at is to the right of the connected devices: The communication buckets where you will find a lot of the protocols I described in my previous post. But there is where the interesting part begins.

DATA END POINTS. I have create a bucket for this as it many players actually only play there. The features found in this bucket are around pub/sub architectures and protocols for high volume communications for millions of devices, but also intelligence. I believe that intelligence at the Data end point level is extremely important as it will avoid the overload of totally useless information coming from the devices. The end point should be capable of creating a first level of triage of the information coming in: What’s expected? what’s normal? what is NOT normal? The end point should be able to generate alerts in near real-time to the customer support bucket (below) or to applications through the development platform (above). The better at assessing data in real time, the less dumb data will be added to the data store (to the right). Another important element of the data end point, is authentication and security that can be handle there. The end point should be able to know (authenticate) the devices and customers behind the devices as well as make sure that communication are secured (encrypted) and no one can send data or commands in a malicious way.

BIG DATA STORE. This is where the raw data will go in. I do believe that if the data end point has done its job right, not everything needs to be stored. Maybe only aggregate data is store like “the device has been on for 24hours” instead of thousands of “I’m alive” data points. That said, there is a balance between pure raw data and not enough data. The value of a big data store is to be able to query and work through that data via learning algorithms, and pattern detection algorithms. Those algorithms love to have tons of data as they become better and better with it. There again, the intelligence is key to determine predictive outcomes. This is the holy grail of the industrial internet by the way: predicting when things are going to fail in order to reduce unplanned maintenance. You will notice that the diagram connects the big data store to applications (above) and customer support (below) as this data is key to provide insights to agents in call centers (of in the field) as well as provide input, bi-directionally, to applications. Maintenance history, upgrades, etc, should feed back from specialized application into the big data store as well.

ASSET MANAGEMENT. There is data generated by the devices, but there is data about the devices. A device needs to come online for the first time and declare itself through a provisioning system before it can send data. The devices have owners, serial numbers, a history of their creation, maybe even all the way to the CAD designs and specifications. It is made of parts that can be assembled, upgraded, etc. It has a cost, to sell of to built. It can be optimized in a way or another. All that information about customers’ assets will be kept in that system. An advanced asset management and asset optimization system is a critical piece of the landscape where a lot of value can be generated.

DEVELOPMENT PLATFORM. On top of the data and assets, can sit a development platform. From simple cloud based OS to full fledge application development platform, you will find a entire ecosystem just in that bucket. Anything that allows others to create applications linked to the data or to connect to the device (via the data end points bus). Those platform have to provide all the brick of security, authentication, licensing and provisioning management, workflows and business processed, etc.

CUSTOM APPLICATIONS. Either through an ecosystem of ISV or by customers themselves, applications can be developed on top of the platform to consume data and manipulate / control devices. Applications can be virtualized for certain industries (smart home, smart cities, industrial internet, healthcare, etc..) each having their own market to go after. I think there is still a lot of work and opportunities in that bucket where most certain 70% of the value chain will reside at some point. Note that those applications will connect back to the customer via the web, through any type of device (mobile, tablet, desktop) including the device itself.

CUSTOMER SERVICE AND SUPPORT. Going back to the bottom part of the diagram, you may know that I am a big fan of the service use case of the IoT. I believe it is one of the big opportunities that this trend provides: to offers outstanding customer service thanks to the data generated by connected devices. From intelligent routing of alerts coming from the data end points in real time, or from the data store asynchronously, to knowledge management, entitlements, RMA, and then multi-channel communication with the customer, a good customer service operation will empower agents by tying device data and related intelligence to the customers themselves.

FIELD SERVICE. Interestingly the field service use case is very big in customer support. It’s not because machines are connected that there is never a need to physically go see them. Repairs, maintenance, refill, physical upgrades, all those still have to be performed by a field technician. I think what has changed is that now that devices are connected, the field service technician can be much more efficient than before. Accessing the entire history of the devices, its usage and failure patterns, knowing everything about the parts needed and planning for preventive maintenance are all very cost effective tasks that will drive significant returns to companies using connected devices. I even think that it will drive more field service needs than before, but it will be done is a more efficient way. Instead of incurring a huge cost of a failure, a company can have a much smaller cost of more efficient technicians.

SALES AND MARKETING PROCESSES. Even if support is definitely a big use case for IoT, the extension into sales and marketing is undeniable. A cartridge is low in your printer: that’s a lead. You don’t drive your car very much: that’s a lead. You go in certain places very often: that’s a lead. Opportunities are all over the place for up-sell, cross-sell, proactive marketing, in-context marketing, etc. I think this will come a bit later as the infrastructure need to be in place for it to be realized fully but it will come and it will be big.

ANALYTICS & INSIGHTS. I could not have a diagram without this piece. Understanding the data through smart visualization is critical to efficient decision making. Seeing trends and patterns and being able to generate reports and dashboards conveniently for every level of the organization are constant asked from any company I talk to. Bringing together, Device data, support data and sales data into single dashboard has tremendous power that many company will take advantage of.

I hope that this view of the IoT landscape brings some clarity to some of you. I would love to get feedback about it to make it better. Why I built this initially was to understand where we wanted to play as a company and I think it has been helpful to segment the market and the players we have been talking to.

Enjoy and share.