7 minute read 23 Apr 2018
Brewers talking tablet wooden casks

How to monetize IoT data to create value

By

EY Global

Multidisciplinary professional services organization

7 minute read 23 Apr 2018

Show resources

Every organization now needs to understand data’s potential as a business asset.

In the Transformative Age, we are seeing the reinvention of everything from business models to the very concept of a business asset. Historically, an organization’s assets were largely physical, but in the future the most valuable ones could be digital.

What was once indivisible can, in the digital era, be easily multiplied and put in the hands of many entities simultaneously — opening the possibility for exponential value creation.

Yet today, there is no real methodology for data valuation. From an accounting perspective, data often has no value; as an asset it is rarely even taxed. This will not be the case for long. So organizations must understand the eight layers of potential value creation from data.

Layer 1: Operational excellence

This is the most common layer in the market, and it focuses on the viability of the business model. Cheaper, more powerful and longer-lasting IoT devices can extend the useful life of physical assets through applications such as predictive and preventive maintenance. They can also automate processes (such as stock level optimization and fraud management), which may improve overall equipment effectiveness. This can deliver substantial cost savings, but new revenue potential is low.

Layer 2: Product enhancement

In this layer, IoT devices can help improve a physical product in at least three ways: during production, in response to how it interacts with the environment or even dynamically. For example, hardware may be built to a uniform standard, but its performance characteristics could be increased dynamically if the customer pays a higher tariff. A vehicle engine, for instance, could be capable of 500bhp, but only produce half of that on a basic tariff. For an extra payment, or in an emergency situation, extra power could be “unlocked” remotely without any change to the hardware. Data acquired from the product during its life cycle could also be used for new generations of product improvements.

Today there is no real methodology for data valuation: in accounting, data has no value; it is not even taxed. This will not be the case for long.

Layer 3: Regulatory compliance

All businesses in regulated sectors, with safety procedures or maintenance requirements, currently have to hire technicians to perform the required work. But with IoT devices that can control specific hardware parameters, this can be done remotely.

To reach this layer, IoT data needs to be completely credible and indisputable. One way this could happen is through the integration with blockchain, which has the required capabilities and could also help reduce the cost of overall control activities. However it happens, without this element of credibility, no governmental institutions will allow IoT technology to replace certified technicians. This is especially true for institutions responsible for the safety of people.

However, this convergence could create “the internet of trustworthy things.” Interactions between connected things, underpinned by “smart contracts,” could establish a foundation for “decentralized autonomous organizations” (DAO). And this would begin the era of the true machine economy.

Layer 4: Data-based economy

Digital disruption has already created new categories of assets with no physical representation. Physical assets and products cannot be “cloned” — when sold, an organization transfers ownership of the asset to a buyer and cannot use it any longer. However, selling digital assets — i.e., data — does not mean losing access to it. And the selling organization can still use the asset to create income, and in other ways of their own choosing.

For example, one well-known delivery company optimized its vehicle fleet operations using data from sensors — reducing maintenance time and costs, fuel consumption and accidents. They then built their business around that data, publishing information about the attractiveness of business areas, based on the patterns of parcels delivered.

Layer 5: IoT value chain integration

Value chain integration is particularly relevant for organizations in business-to-business (B2B) industries. It enables direct interaction with end consumers by extending cooperation to a business-to-business-to-consumer (B2B2C) model.

IoT devices embedded within or integrated with physical products enable organizations to extend their value chain beyond direct buyers or suppliers in the chain. For example, a brewery could fit IoT sensors in a beer barrel to monitor certain parameters during transit and check that the delivery company is handling it correctly (B2B). 

Layer 6: Value net integration

The combination of mobility with data analytics can enable a more customer-centric approach. Organizations that occupy the same customers’ value network can exchange data about them and their behavior, with benefits for everyone, including the customer.

Organizations that control these integrated value nets will claim the predominant position in the business ecosystem — with the potential to monetize it, whether through internal cost reductions, or new revenue from value net partners or stakeholders.

Layer 7: Digitized asset economy

Low-cost infrastructure assets can move from being treated as indirect costs to the business, to revenue generators in themselves, by way of improving customer experience. IoT devices integrated with blockchain capabilities, in particular cryptocurrency wallets, open up possibilities for micropayments such as charging for Wi-Fi in cafes or paying for services in a smart city. Also, revenue from asset utilization can fund maintenance and failure management tasks handled by local stakeholders. These stakeholders could keep assets running for much lower costs than the asset owner.

Layer 8: IoT stack commercialization

In the evolution to a data-based economy, the endgame is to control the flow of data. We can already see the strategic importance of this control as some major and well-known players are gathering gigantic volumes of data to be the subject of multi-perspective analytics. In this way, by securing their access to actively acquired data, they are securing their position as a potential leader of virtually any market in which they wish to compete.

However, rapidly expanding IoT infrastructure allows potentially any organization to take a similar strategic position by controlling a substantial part of connectivity or end devices (IoT). This translates into control over a huge amount of passively acquired data, and therefore control over major data flows.

Flow control guarantees that all necessary data required for services is consistently available, and it gives the organization the predominant position in the value net ecosystem. By sharing one or more layer of the IoT technology stack (including sensors and actuators, communication, data lake, data analytics and human-machine interface), organizations can build highly competitive positions. And with effective commercialization of such sharing services, they can reduce the cost of developing and maintaining their IoT infrastructure.

Conclusion

Very few organizations have established methods for data asset management. It is not enough for data to be used only for the purposes of monitoring and maintaining physical assets. That data needs to be monetized. This, in part, is a process of generating new revenues from data that currently serves another purpose.

The key is to understand the growing potential of data-based business, and, layer by layer, orchestrate around this new paradigm in which an asset can simultaneously be owned, utilized and sold many times over.

The first step is to realize that data can generate value, even though it is not seen as an asset from an accounting perspective. Organizations must assess what data can be used only internally (for production optimization or efficiency improvement) and what has external potential (to extend life cycles and add value).

Because in the near future, data will be key to growth — and survival.

Summary

It is not enough for data to be used only for the purposes of monitoring and maintaining physical assets. It is crucial to realize that data can generate value, even though it is not seen as an asset from an accounting perspective. Organizations must assess what data can be used only internally (for production optimization or efficiency improvement) and what has external potential (to extend life cycles and add value).

About this article

By

EY Global

Multidisciplinary professional services organization