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Article
IoT Impact--Part Two: Understanding IoT Data and How IoT Changes Your Business Model

Recommendations for dealing with Internet of Things data, the role of data brokers, and data ownership. How IoT can change a firm's core business model.


Full Article Below -
Untitled Document

The IoT Impact—Article Series
  1. How Will IoT Change Your Product Offerings?
  2. Understanding IoT Data and How IoT Changes Your Business Model (this article)
  3. The Role of Partnerships and Ecosystems

(This article is excerpted from the report: The IoT Impact - Finding Your Company’s Role in the New Smart Connected World available for download here.)


In Part One of this series, we looked at the 10 questions posed by Michael Porter and Jim Heppelmann in their HBR article How Smart, Connected Products Are Transforming Competition and we examined questions about how to think about what Internet of Things (IoT) phenomena means for your product strategy and decisions. Here in Part Two we discuss data and business model questions.

2) Data Questions

IoT generates a tremendous amount of data—much more than people generate manually with their keyboards and cameras. And the volume of IoT data being generated will continue to increase at an exponential pace. How can companies extract the maximum value from that data? How should they think about it?

Taking a Data Census


Example Uses of Smart Fleet Data

GPS data may be used for geofencing1 (ensure vehicles stay on route), for continuously updating ETA (estimated time of arrival) and generating late load alerts, and for use by dispatchers to identify vehicles headed towards the vicinity of a new to-be-assigned service request. Temperature data can be used to generate refrigeration unit malfunction alerts and predict shelf life of delivered produce. Accelerating and braking data can be used to improve driver habits, resulting in better fuel mileage and improved safety. Electronic manifest data can be used to inform customers of the status of their shipment. When fed into analytic systems, much more value can be squeezed from this data, such as identifying safest or quickest routes, best time of departure, and more.

First, it is helpful to get a solid understanding of what data is already being generated. Data exists at many different levels in the technology stack. Consider a fleet of ‘smart trucks.’ At the lowest level, there is data being continually generated about the level of oxygen in the exhaust gas, vacuum pressure in the intake manifold, barometric pressure (to adjust fuel mixtures and engine timing), coolant temperature, crankshaft position, engine speed, oil pressure—the list goes on and on. Many of these data are not communicated outside of the powertrain, but simply used in real-time feedback loops for things like controlling the fuel mixture or providing anti-lock braking. Today, this may not be considered to be IoT data, because it is never put out onto the internet,2 but there is often a lot of untapped value in exposing some derivation of that data to the vehicle manufacturer, service providers, fleet operators, and others.

At the next level up, there is a huge amount of data that is exposed. In our smart fleet example, some of it is exposed only through a local interface, such as EDR3 data, but increasingly some of those are available over remote wireless links. There are also GPS location data and temperature data for refrigerated loads. Some trucks will carry an electronic manifest stored in an active RFID tag affixed to the trailer or container for use by carriers, shippers, customs officers, and other third parties involved in the transport of the container.

Leveraging Calculated, Inferred, and Federated Information

Often the most useful data is not the direct sensor data, but information that is calculated or inferred from combinations of that data by an algorithm. An example is a real-time locating system. The raw reader data may provide strange results: a specific item seen in the back room and at various points while being brought to the store floor suddenly disappears from view without a trace. In fact, we can infer that the item is simply blocked from being read. Algorithms to decipher the meaning of low-level data are developed by experience in environments over time to create better and better higher-level inferences and intelligence. Data may also be federated from multiple sources to provide a broader view. For example, in an office building, data from the elevators, lighting systems, heating and A/C, security/surveillance, and other systems may be combined to provide a higher level of intelligence about the building. This may also include data from external sources such as weather, traffic, what time a big game is letting out, or other external events, to decide when to pre-heat, pre-cool, or take other actions in the building.

Data Brokers

Some smart connected products may reside in a ‘closed’ environment, in which the products, communications, and cloud software all come from the same company. We expect the majority of products and data will need to exist in an open environment with many devices from different manufacturers all making their data available to many different applications and other devices. Open environments often require an intermediary private data broker;4 a system that collects, protects, and provides access to heterogeneous data for whichever systems, trading partners, and third parties have the need and right to use it. Public data brokers may implement crowdsourcing, such as Waze collecting GPS location data from millions of users while they drive, analyzing it to infer and provide real-time traffic conditions.

Data Ownership

IoT data ownership can get complicated. Who owns the data generated by a smart truck?5 Data ownership considerations should be discussed and included in contractual agreements between the parties. This may include multi-party agreements, anonymized and aggregated uses of data (for example for benchmarking performance), and security/privacy guarantees.

3) Business Model & Scope Questions

Smart connected products have the potential to radically shake up business models. For manufacturers, there is a natural progression from selling things, to selling things + services (i.e. maintenance and repair), to selling outcomes (product-as-a-service model). Building intelligence and connectivity into products is a key enabler of these shifts, but ultimately business will also need to develop new types of payment and subscription pricing models, support structures, service level agreements, and service organizations and partnerships. As well, it impacts product design. The whole relationship with the customer is profoundly impacted:


Product-as-a-Service Examples

In the new outcome economy, customers can buy the desired outcome rather than buying the thing producing the outcome. Examples include all three major aircraft engine manufacturers selling power-by-the hour instead of selling engines; Philips customers can pay-per-lux with their light-as-a-service instead of buying light bulbs; Schaeffler Industrial Services offers bearings-as-a-service for large complex machines like wind turbines or cruise ships with a multi-year, pay-per-rotation contract; Evergreen Lease from Interface who installs, cleans, replaces, and recycles their carpet squares so the carpet is perpetually new; Contract Air service from Atlas Copco provides compressed air at specified pressure, dew point, and purity—the customer pays by the cubic meter; Philips Healthcare offers pay-per-use programs for their whole technology portfolio. Additional value-add services are wrapped around many of these programs.


  • Ownership of the capital assets shifts from the customers to the manufacturers.
  • Product manufacturers’ interests are more aligned with the customers. In particular, manufacturers are incented to make increasingly reliable products requiring less maintenance and fewer parts replacements since those costs are now solely born by them.
  • Customers are protected from technology obsolescence.
  • New pricing models are needed to align the manufacturers’ compensation with the value gained by the customer throughout the full life of the service.
  • Providers of the product-as-a-service become more deeply embedded into their customers’ environments, with a more fundamental understanding of their customers’ needs, both through dialog with the customer, but also via more embedded monitoring to gain insights into actual usage.
  • Insights can be used to optimize outcomes, such as lowering fuel consumption, increasing safety, reducing labor costs, reducing environmental footprint, and more. These optimization services can become a source of incremental income for the service provider.
  • Product-as-a-service offerings typically generate higher margins than does selling physical products alone.6 Margins are increased by continually reducing the cost-to-serve and providing a broad set of higher-value-added services on top of the base service.

Companies need to be careful to not get stuck in existing models, even if they seem to be working now. New players emerge and existing players change what they do. Some equipment providers are becoming software or platform providers. We see battles being fought over who will become the central controlling platform for various environments—the office building, the home, the farm, the mine, the smart city.

Using IoT to Transform a Service Business


Disrupting the Truck Leasing Business

Like many products, truck technology is evolving ever faster each year. That means that within a two to four year period, a new generation of technology is available that can provide better fuel mileage, better safety features, and other advantages. With Fleet Advantage’s shorter-period lease, customers can continually take advantage of the latest and greatest vehicle technology. Trucks are cycled through multiple owners much earlier in their life than with the traditional leasing model. Tier two and tier three lessees all gain access to relatively newer models with similar benefits as well.

Fleet Advantage further leverages the data they collect from these smart trucks, comb­in­ing it with maintenance and route data from their customers and runs it through big data analytics, drilling down into a cost per mile (normalized for season, terrain, loads, and other factors). This allows fleet managers to make smarter decisions. They can track fuel economy, hours of service, driver behaviors and much more. They can detect when an engine wasn’t tuned up properly or a driver is staying too long in the lower gears, thereby using more fuel than necessary. These generate additional savings on top of the ones gained by having the latest and greatest equipment.

Thus Fleet Advantage is providing much more than just a financial service. They have become a key partner in helping their customers optimize their fleets. By adding multiple elements above and beyond the typical lease-only service, they are able to create incremental value, which they divide equit­ably with their customers, thereby boosting Fleet Advantage’s overall revenue and enabling them to realize higher margins than typical truck leasing firms. Combining a new vision with a deep understanding of the industry, Fleet Advantage has been able to leverage IoT data to upend the leasing model and create a whole new approach and type of service.


Business model transformation from IoT is not just happening to physical product manufacturers. Take the case of Fleet Advantage, a finance company specializing in leasing tractors and trailers for vehicle fleets. They saw an opportunity to change the whole way that leasing is done using the data generated by the smart truck’s onboard computers. Traditionally, trucks are leased in fixed financing arrangements lasting six to eight years, locking them into an agreement that is not responsive when circumstances change. Fleet Advantage shortens the terms to two to four years and, critically, bundles in services that aggregate data generated by the onboard computers in the fleet into a format that allows the fleet manager to make more sense of it. They also bundle benchmarking services based on that IoT data they collect and provide experienced trucking industry veterans to help customers interpret and take action on that data. For more see the side bar “Disrupting the Truck Leasing Business.

Who Are You?
Re-examining Your Core Value Propositions

As a company evolves and dives deeper into these new business models, the very definition of what they are evolves. Bigbelly, the solar powered trash compactor company mentioned earlier, originally aimed at reducing trash pickups. This had knock-on benefits of reducing the number of garbage trucks on the road (thereby reducing traffic congestion and noise), decluttering public spaces (far fewer trash containers needed), reducing rodent infestations (containers are fully enclosed and animals can’t get in), and dramatically reducing wind-blown trash. But then they found some of their municipality customers asking if the platform could be used to provide public WiFi services. This did not come from their existing customer base, the department of public works, but from the CIO’s office or other innovators at city hall. Bigbelly has started thinking about their device as more than a smart trash can. It is self-powered communications platforms scattered throughout public spaces, located near where the most people are. Working closely with their most innovative customers, they are exploring other uses, such as adding sensors to detect air quality. One lesson here is to take an expansive view of who your customer is and what your value proposition might be. Sometimes a broader view and different perspectives help identify the additional value you can provide.

In Part Three (the final installment) of this series, we will look at the critical role of partnerships and an ecosystem for IoT, as well as guiding principles of successful IoT transformations.

__________________________________________

1 Geofencing can be used to detect when a vehicle deviates from the planned route in order to detect a theft in progress, as well as other varieties of driver malfeasance. -- Return to article text above

2 High volumes of internal-only data used for real-time controlling functions are found in many, if not most, IoT systems. -- Return to article text above

3 Event Data Recorder (EDR) records crash-related data such as speed, vehicle proximity, brake application, clutch application, airbag deployment, and cruise control status. In addition, they may provide monitoring of the driver’s hours of service, fuel economy, idle time, average speeds, and maintenance-related information. -- Return to article text above

4 A data broker service can be both open and private at the same time. It is open in the sense that it is extensible and integrates with heterogeneous systems and devices. Yet it is private in the sense that the broker is an invitation-only environment, often under the control of a single enterprise. You can’t just sign up on the internet. -- Return to article text above

5 The data generated by a smart truck could be owned by the truck manufacturer, the fleet owner, the carrier, the shipper of the goods, the providers of monitoring services (such as produce temperature monitoring), the driver’s employer, or different combinations of these. -- Return to article text above

6 However, they entail radical changes to pricing, business model, and organizational competencies. These must be executed well to realize the higher margins. -- Return to article text above


To view other articles from this issue of the brief, click here.


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