The Internet of Things (IoT) is due to grow massively. According to Gartner, more than 20.8 billion “Things” will be connected to the Internet by 2020, all producing data and sending it somewhere. For consumers, the ability to control devices remotely can have practical purposes. Reducing heating bills or power consumption can have substantial savings attached, while other devices get Internet access attached to them whether it is strictly necessary or not.
For businesses, data from Things potentially can support new services, which can be monetised. Gartner predicts that the market will be worth an estimated $235 billion worldwide by 2020, gathering pace as the number of smart devices goes up, too. Separately, IDC predicts that more than 50 percent of Global 2000 companies will earn more profits from selling information-based services by 2020 than is achieved from physical product sales alone. IoT-connected devices will be essential to this shift. However, this data will not turn into cash on its own.
Data from devices has to be stored, organised and analysed to create value. This can be useful in context with a service. For instance, the utility company behind a smart thermostat can use data from multiple devices and analyse it to spot trends in usage patterns. However, for the majority of enterprises, this data from devices will not have enough context on its own.
Putting IoT data alongside other business information
An example of this might be logistics and supply chain operations. IoT data can show where vehicles are, over time, and sensors can be used to judge performance. This information can be analysed to improve preventative maintenance. By predicting when component failures are likely to occur, a logistics company can reduce costs from lost business or poor customer service.
However, this supply chain information may not get used outside that specific team. When other departments aren’t aware that the data exists – or aren’t able to blend it with their own sources of data easily - this misses an opportunity to improve business performance in general. Instead, IoT data on supply chain operations can be used to help manage overall operations. This relies on networking data sources together to provide context to the whole business, not just for the department.
To take this initial example further, the business management team may want to use data on profitable routes to establish what kinds of customers are most likely to buy and where they are based. For a supply chain business, this might lead to focusing sales efforts on businesses in a particular region or within a specific market. By taking information from devices and networking it together with other sources, better decisions can be made.
IoT data in supply chain operations
One important consideration here is how data gets used both inside and outside a company. For a supply chain team, the majority of work will involve collaboration with other suppliers, partners and customers to achieve a goal. Using sensors to detect stock levels and locations of vehicles transporting that stock – and then to manage the interactions between customers, partners and suppliers – should provide a critical set of data to the business. However, this information can also be used in multiple ways:
- For companies that operate logistics, this information is essential for management teams to see how well operations are running and how profitable the business is. Combining this data with sales and invoicing can provide a view of operations against profitability that can be updated in near real time.
- For those organisations with large supply chains, this information can be shared with partners for them to see their own performance in context. This model involves working with suppliers and vendors to see what applicable service levels are in place, how well they are being met and the potential impact on business. This can help turn procurement into a more strategic arm within the business.
- For companies that supply to others, this kind of information can then be used to manage stock levels or their own relationships with suppliers.
Based on these initial use cases, it’s then possible to use device data in more strategic ways. For manufacturers, strategic sourcing is a key element in managing operations against risks incurred by global economic events or disasters, as well as managing product cost levels.
Many manufacturers use bills of material data to create different recipes or production lists for products that can then be swapped, based on availability of raw ingredients or specific components, for example. This approach requires decisions on stock levels and when switches in production should take place, but they may be based on incomplete or out-of-date information. By including IoT data, the impact across other business units can be modeled more accurately.
The value of IoT data in the network of analytics
Making this kind of data available to other organisations requires its own set of decisions. Should IoT data be made available to other partners, or just the results of the analytics that are taking place? How should this access be secured? And how should this kind of data be monetised over time?
This kind of embedded analytics project involves several strategic decisions for the company as a whole, not just the IT team. Should it be a product in its own right that can be made available to customers for an additional fee? Should it be positioned as a tool for additional value that justifies a higher price? Or, is this an internal aid that gets used to improve service levels and collaboration across the company and its supply chain?
Whatever decision is made will determine some of the ways in which data is then shared, and how it is made available for others to work with. This may be as simple as providing regular status reports, to online visualisations that can be used for collaboration.
Looking ahead, IoT data will become essential for businesses that are involved in connecting customers to products and services. These devices will share data that can then be used to improve those services. However, IoT data should not be considered on its own. Instead, it will be one piece of a larger whole that helps companies analyse their overall positions and make decisions about their strategies in the future.
This data will become part of a global network of analytics that connects not just people and data, but also companies and public institutions. This networked approach to data involves putting accurate data into the right context for each of those potential users, and giving them the tools to connect to each other and enrich their insights and those of others, wherever they happen to be and whatever decision they have to make.
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