Running your IoT company with 3 layers of Operational Analytics

By Pilgrim - October 15, 2019

Introduction

All connected-product companies need analytics to “run” their products and deliver value to the customer efficiently. To help define a structured plan of action, here we present a simple framework to think clearly about IoT analytics, focusing specifically on the operational questions and actions required to deliver an efficient business with happy customers.Operational Management for the 3 layers of IoT (1)

 

For any connected proposition we can identify three distinct “layers”. Each layer can be thought about independently, as certain data, activity, analytics and value flows within that layer. Each layer also depends for its existence on the layers below it. Analytics at all three layers are vital for every connected product.  Working-up from the bottom:

Device Layer

The device layer involves the hardware of the device, and the software running on it.Operational Management for the 3 layers of IoT - device

Operational device questions include:

  • What have we deployed, where is it, who has it?
  • What software is it running, what hardware version?
  • Is it secure?
  • Is it working properly (what’s the device uptime)? If not, why not?

Operational device actions include:

  • Triage for devices requiring intervention (rebooting, manual help, upgrading)
  • Performing a software upgrade across a device estate

All connected devices will have bugs and vulnerabilities, so will require a software upgrade from time to time. Upgrading is a complex process that is fraught with peril, but analytics can be used to identify devices which need to be upgraded (don’t conduct a dangerous activity on devices which don’t need it) and when (don’t upgrade a device when someone’s trying to use it), and can then track devices through the upgrade process, identifying drop-outs and their causes.

Communication Layer

Operational Management for the 3 layers of IoT - commsThe communication layer connects the device to the internet. It might be as conceptually simple as an Ethernet port, or as complex as a mesh or drive-by radio network. Again, there is lots of software involved, and potentially multiple business entities, and therefore lots that can go wrong or fall between the cracks.

Operational communication questions include:

  • Is the device reliably connected (what’s uptime of the connection?)
  • How much is it costing to connect?

Operational communication actions include:

  • (Re-/De-)Provisioning (activating a SIM, enabling roaming, switching providers)
  • Producing bills

Application Layer

Operational Management for the 3 layers of IoT - appIf both the Device and Communication layers are working then from the Cloud we can “see” a device and know that it is working and connected - at a technical level. Analytics at these two layers is “necessary but not sufficient” though, because the end customer is usually not buying a device or connectivity, they’re buying some kind of benefit.

There is a very strong tendency for connected products to become services over time:

  1. An EV charging company might start off deploying charging points (products) but pretty soon it will end-up delivering service (the ability to charge your car).
  2. A lighting or heating company might start of deploying lights (products) but end-up delivering service (light).
  3. A printer company might start selling printers, but then start delivering print as a service (responsibility for topping-up the ink and paper lying with the vendor, not the customer).
  4. A connected car company might start selling cars, but pretty soon end-up selling miles (with all aspects of servicing etc. taken care of).
  5. A paper towel company might start by selling towels, but pretty soon it’ll be selling the service of “the towels in your washrooms will never run out”.
  6. A pallet company might start selling pallets, but pretty soon it’ll be selling the availability of a pallet to carry goods, in the right place and the right time.
  7. A vending machine company isn’t really selling machines, it’s selling a service to sell consumables effectively (by ensuring those consumables are available)

By taking responsibility for the running of the product as well as its manufacture, vendors can deliver a much better customer experience at a lower cost (because they understand much better how to run their product than any customer ever will). An additional attraction is that it can now be sold as gradual opex costs over time, instead of a big capex outlay at the start, making the purchasing decision easier.

So just because we know our device is working technically doesn’t mean that it is working for the customer. Our EV charging points might all be technically working perfectly, but in a particular car park all 10 of them are currently in-use, then the 11th customer will experience a failure of service delivery. Our printers might all be technically working perfectly, but if they’re going to run out of paper during today’s print run then the customer will be furious. Our pallets might be fine, but if they’re in the wrong place today then the customer can’t ship their goods. And so on.

Therefore analytics at the application layer is about measuring, managing, and perhaps even predicting, the availability and delivery of the actual customer use-case.

Operational application questions include:

  • Is our service available a high percentage of the time, in a high percentage of our sites?
  • Are customers using the service, repeatedly?
  • Are these metrics all trending in the right direction?
  • Does how they are they using it give us any clues as to how to improve our proposition?

Operational application actions include:

  • Deploying more products to a site, moving products from one site to another
  • Fixing a problem with a lower layer (communications, device) which is preventing us achieving a key application metric

Comparing analytics across the layers

Once there is a robust set of analytics running at each layer, further useful business intelligence may be gathered by correlating information across the layers, for example:

  • A company deploying connected vending machines can optimise their return by correlating:
    • which sites sell most goods
    • which sites have highest cellular connectivity fees
  • Customer sites with more devices on them are probably more important than ones with fewer, enabling any device/communications triage activities to be prioritised for VIP sites/customers.

Conclusion

Analytics is how connected product companies deliver value, efficiently, and analytics happens at three layers: device, communications and application. As companies tend towards delivering a service rather than a product, IoT becomes an integral part of the transition by enabling the optimisation of service delivery creating competitive advantage by ensuring excellent service at minimum cost.

 

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