As more and more objects are connected to the internet, real-time data is generated massively. Sensors in smartphones, cars, manufacturing equipments or infrastructures collect data about the product use and its environment. Sent to a cloud server, data are used to predict future events and make better business decisions. How value is created and captured from IoT data? Which strategies can be implemented to leverage them?
Every year in January a new bunch of connected products are launched during the CES in Las Vegas. In 2024, it’s the Rabbit R1 which attracted the most attention. Other regular attractions are consumer connected products (think of the car, the TV, the toothbrushes, …). Over the last few years, pretty much every common product has been equipped with sensors with the promise of new value for their users. However, this is just the visible part of the iceberg of connected devices and the majority of cases and value are related to business-to-business solutions and manufacturing environments.
Understanding IoT data
The Internet of Things (IoT) generates a vast and diverse pool of data, offering a goldmine of insights for businesses savvy enough to tap into its potential. IoT data comes in volume and velocity and blends structured and unstructured format. Data are collected from a myriad of sources:
- Sensors and Actuators: Found in everything from home thermostats to industrial machinery, these devices provide real-time data on temperature, pressure, humidity, motion, and more. For example, agricultural drones use sensors to assess crop health and optimize farming practices.
- Wearable Devices: Fitness trackers and smartwatches monitor health metrics such as heart rate, steps taken, and sleep quality, offering insights into consumer health trends and individual wellness.
- Smart Home Devices: Products like smart locks, lights, and assistants like Amazon Echo or Google Home, manage household tasks and preferences, revealing patterns in consumer behavior and preferences.
- Connected Vehicles: Beyond basic telemetry, connected cars offer a wealth of data on usage patterns, vehicle health, and driver behavior, aiding in everything from predictive maintenance to personalized insurance plans. A single connected vehicle can generate up to 2 terabytes of data per day, encompassing everything from engine temperature and speed to GPS positioning and driver behavior analytics.
- Industrial IoT (IIoT) Devices: In the manufacturing sector, IIoT devices track the performance and condition of equipment, facilitating predictive maintenance, optimizing production processes, and ensuring worker safety. Smart meters, for example, can report energy usage in real-time, providing utilities with instant data for optimizing grid operations and customer billing.
The role of IoT data in gaining competitive advantage
These data produced at scale, in real-time and centralised offer massive value creation opportunities for the companies who can access them.
Consuming external IoT data for making better business decisions. The widespread adoption of connected equipment in manufacturing environments opens a new continent of data, generating new opportunities to monetize or aggregate data. Accenture estimated the value generated by IoT data marketplaces will be 3 000 B$ in 2030. Clients of such companies buy IoT data to improve their operations. Retailers, for example, are intense consumers of geolocation data to understand better the flows of their clients and prospects.
Using internal IoT data for operational efficiency and cost reduction. When a manufacturing operation is equipped with sensors, data are analysed to identify patterns and predict failures. With better predictions, maintenance operations can be optimised and hence costs are reduced. Similarly, if failures can be predicted, preventive actions can be performed which limit the impact of the failure. Oil companies such as Total Energie have experienced massive cost savings by equipping their refineries with sensors and analysing historical and environmental data.
Enhance customer experiences through personalized services and products. The rapid growth of connected objects makes that type of offer possible for numerous products. Suppatvetch and his colleagues describe 4 archetypes in recent research. Add-on is when a provider offers services that are added to a physical product to support its function. Usage-based when customers subscribe to a plan based on their use and needs. Solution-Oriented when providers offer integrated solutions. Sharing when customers pay for using or accessing a product for a limited amount of time.
Develop new business models. Power-by-the-hour is the poster child of servitisation. 50 years ago Rolls Royce offered a complete engine and accessory replacement service on a fixed-cost-per-flying-hour basis. Many product manufacturing companies moved from product manufacturers to solution providers or ecosystem orchestrators. In these business model changes, data is the combustible on which the model runs.
Improve product design. When objects are connected, more information about their usage is available for the product manufacturer who can adjust better the design of future products. Car makers have benefitted a lot from analysing the data of the connected cars of their clients and have adjusted the car designs accordingly.
Grow revenue through data reselling. Data generators, companies who have direct access to data, like telecommunication companies and banks, have three choices to monetize their data. First they can offer analytics services leveraging their proprietary data. Mastercard generates 25% of its revenue from leveraging its data for services that include marketing analytics. Second, they can sell their data to an aggregator. Vodafone generates significant revenue by trading their users’ data to aggregators. Third, they can sell their data to an analytics service provider. Thasos sources data from telecommunication companies to provide real-time customized indicators for hedge funds to help them make their investment decisions.
Challenges in leveraging IoT data
Opportunities are numerous and value creation pools are attractive. However, value seems harder to capture than expected and major challenges explain this gap between the promises and reality.
Integrating IoT data with business intelligence and analytics tools. Integrating IoT data with existing business intelligence (BI) and analytics frameworks can be daunting due to the sheer volume, velocity, and variety of the data generated. Traditional BI tools may struggle to process and analyze IoT data in real-time, limiting the ability to derive actionable insights swiftly.
Implementing advanced technologies (AI, machine learning, big data analytics) for deeper insights. Adopting AI, machine learning, and big data analytics is crucial for extracting meaningful insights from IoT data. However, implementing these technologies requires significant investment in infrastructure, tools, and expertise, posing a barrier for many organizations.
Ensuring data security and privacy compliance. IoT devices are often vulnerable to security breaches, and the vast amount of personal data they collect poses privacy concerns. Ensuring data security and compliance with regulations such as GDPR and CCPA is a significant challenge for businesses as are the regular attacks by hackers, such as the one felt by Schneider Electric.
Building an IoT ecosystem through partnerships and collaborations. Developing a comprehensive IoT ecosystem involves integrating diverse devices and systems from various vendors. This requires effective partnerships and collaborations, which can be challenging due to compatibility issues, competitive interests, and the complexity of coordinating across different platforms.
Access a skilled workforce to interpret and utilize IoT data effectively. There is a growing demand for professionals skilled in data science, IoT technology, and analytics. However, the talent pool is limited, and competition for skilled workers is fierce, making it difficult for companies to acquire the expertise needed to leverage IoT data fully.
Engage in a culture change. Leveraging IoT data for competitive advantage often requires a shift in organizational culture towards data-driven decision-making. This cultural change can be challenging, as it involves altering established workflows, decision hierarchies, and mindsets. For example, when a company moves from product to service, perceived quality is more important than offered quality and the value proposition is more important than the technical performance. Most importantly when a company moves from product to service, the user is more important than the client.
As objects and devices around us become increasingly interconnected, they generate a wealth of data that, when harnessed correctly, can provide significant competitive advantages. However, realizing the full potential of IoT data is not without its challenges and the journey to leverage IoT data is complex and requires a strategic approach, but the rewards are substantial. As businesses continue to explore and invest in IoT technologies, we will undoubtedly see new and innovative ways to capture and create value from the vast oceans of data generated.