Money-making opportunities in manufacturing: Translating big data into ongoing revenue streams
Product development
Exploiting IoT data to develop new products is a preeminent way to increase revenues since “better visibility into customer usage in the field provides valuable information for product designers,” Wills observed. Once, manufacturers relied on inefficient surveys to understand which product features were useful and what concerns customers had that would affect future iterations. According to Long, today’s IoT connectivity provides actual usage data for unambiguous understanding of “how to monetize these behaviors” by creating new features targeting customer usage. This application reduces research and development costs since organizations know exactly which features resonate with consumers and which don’t. It also results in increased customer satisfaction, which may lead to further patronage. For example, washing machine manufacturers know “exactly when you washed your clothes, what setting you used, and how long it took,” Walker said. If the manufacturer sees that a setting is never used, in the next product cycle it can be taken off. Or, if customers are frequently using a double rinse, maybe the rinse cycle will need to be improved.
Advertising
Long characterizes advertising revenue as having “the lowest return” compared to those for strategic partnerships and related services. Some ad revenue streams are somewhat futuristic, said Long, who offered the example of advertisements based on a person’s Bluetooth, Wi-Fi, and purchase history in the retail space, enabling vendors to “set up LED screens that do advertisements so when you walk by a store they show you something on the screen to draw you into the store.” Other geolocation examples in the IoT world enhance the user experience with remarkable detail. Long described a scenario in which it is possible to track people’s positions well enough within their home that as they walk around their home, whatever they are watching on TV can follow them to their closest display screen. The result is targeted advertising based on behavioral data: “This person’s using this device and they have it tuned into this screen from this particular channel or this thing they’re watching,” Long said. The most profitable possibilities stem from using smart home data to promote products based on user behavior. In Amazon shopping venues, where there is no checkout, “what if now your refrigerator can read the same sensor and know what’s in the refrigerator and suggest recipes?” Long wondered. Now, the refrigerator manufacturer might have an interest in giving a company a look at everything in this refrigerator, and that company may buy screen time to advertise recipes and additional food, he added.
Data management
The data management rigors of the manufacturing industry, both before and after the advent of IoT, are considerable. In this regard, IoT’s effect has been both helpful and challenging. “Up until recently, the connectivity wasn’t available to get the data, and the communication protocols were not aligned in such a way to make it easy for the machines to talk to each other and send that information to analytics,” Walker said. Although IoT solved these connection and (most) protocol issues, it unleashed a massive influx of data to tame for the preceding revenue streams. This fact is aggravated by the reality that frequently, a manufacturer’s respective facilities employ different equipment (functioning as data sources), and manufacturers must also account for data sources in various supply chains. Contextualizing this data with historic consumer or affiliate data from partnerships only compounds the difficulty. “Having a big analytics platform that can look at all those data sources and not just one piece of equipment, can help identify problems that you otherwise couldn’t have identified,” Walker said.
Technologies accounting for real-time, disparate IoT data can aid these data management constraints. Wills pointed to “distributed data orchestration capabilities that enable edge devices to filter, store, and transfer the data that decision makers need, when they need it, to save resources and optimize power use” as a source of possible assistance. Organizations can alleviate integration and aggregation issues by leveraging solutions featuring common data models and code bases. This allows them to “get that data model put in, and start working with those algorithms,” and then later when they want to improve the quality of the product, they can, Walker said. Multi-hub master data management solutions have respective repositories for customer, product, and supply chain data with common data models and quality measures.
Moving forward
The challenges with deploying IoT data to facilitate a service economy in the manufacturing industry don’t end with data management. Equally daunting is the growth of regulatory requirements pertaining to privacy and consumer data. However, the manufacturing industry will continue to serve as a stellar example of how the combination of prudent knowledge management and big data technologies can increase the financial viability of a particular vertical—if not all of them. In several ways that count, manufacturing is as much about providing services to customers as it is about provisioning products. As adoption rates for IoT continue to increase, what’s to stop other verticals from similarly exploiting ongoing relationships with customers due to the non-stop generation of big data?