WHITE PAPER MONETIZE IOT DATA WITH ANALYTICS

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1 WHITE PAPER MONETIZE IOT DATA WITH ANALYTICS

2 WHAT WILL YOU LEARN? n IoT Data Analytics Verticals and Synergies n Using Data Internally and Externally n Automation with a Human Element n The Needs of Various Vertical Markets n Data Monetization Use Cases

3 The Internet of Things (IoT) touches all parts of our lives, all areas of the urban landscape, and all business verticals. The number of connected devices around us is growing every day, and now includes vending machines, cars with constant monitoring of all parameters, smart home devices (which, for example, regulate energy consumption), and even trash bins that inform us when they need to be emptied. Companies from various areas invest in IoT technology to become more competitive, attract new customers, improve business efficiency and lower operational costs. IOT DATA ANALYTICS VERTICALS AND SYNERGIES For CSPs, one aspect of this is investment in applications for managing IoT devices and services in selected industry verticals. Within the vertical-specific applications, data analytics is becoming crucial. A graphical representation of data and processes is always the easiest way to clarify the lifecycle, usage and management of a given service. Even the most basic IoT applications use data analytics. Shipping Company uses vertical application to monitor location of its containers, forklift trucks and transport company analyze time of machine usage, irrigation companies watch sensors data to adjust watering to the plants needs. There is also a trend towards achieving synergy of data originating from multiple verticals. For example, smart car and smart home data are sometimes analyzed together to get additional value. That way, when a user is approaching home, their car sends information to open window blinds or turn on the heating automatically. This trend entails the creation of new standards and organizations, focusing on the data collection, analysis and utilization. USING DATA INTERNALLY AND EXTERNALLY IoT devices and applications generate huge amounts of data related to a given asset s or machine s behavior, location, system parameters, or embedded software status. This information helps companies build additional services, meet customer requirements, build better products, and increase operational efficiency. And all IoT data can be used internally or externally. When used internally, IoT data can help establish how to change or modify existing products to become more competitive (for product managers), how to prepare customized offers for important customers (for the marketing team), and how to discover potential faults before they appear and eliminate errors in the production line or supply chain process (for operations). Externally, IoT data helps companies learn how customers can get the most out of their offer by choosing the service elements best suited to their needs. For individual clients, this might mean providing smart home alarms, heating and security call-outs bundled in a single service that doesn t include irrelevant elements such as international calls. For business clients, a taxi firm might benefit from automated fleet deployment, a logistics company could optimize routes and driving times, a removals service could track and protect fragile items, and all organizations can evaluate which employees pose a service risk. WHITE PAPER 3

4 AUTOMATION WITH A HUMAN ELEMENT The IoT data can be gathered from all layers in the IoT ecosystem, including connectivity, devices, applications and external systems. The data is gathered and processed in real-time by business intelligence tools, supplemented with additional information from external data streams. Real-time actionable analytics tools turn standard data mining into actionable systems as they introduce operational actions on devices (individually or in groups), triggered by pre-defined parameters. Such systems can be used to e.g. automatically disable a device if certain movement patterns are discovered, block financial assets if fraud is detected, or limit spending if costs rise beyond a given level. However, it s important to remember that even sophisticated software can t always interpret raw data correctly, so human input is still needed in order to benefit fully from the business value that data analytics can offer. For example, a logistics company will not get any value from a set of speed data taken from their fleet, without correlating this information with road maps, positioning, routes and assets. Data interpretation helps an organization to address the appropriate business case and find those values, numbers or statistics that are important for the customer. CAN DATA BE MONETIZED? To create a business case for investment in data analysis, three main questions should be addressed. First of all, what data do you need right now? You may need connectivity data to monitor services, their movement, usage, performance and influence on product changes. Secondly, what are the internal and external uses of those data? For example, device movement data can be sold to asset tracking users. Finally, how will analytics add value to your data? You may want to add additional actionable tools to discover home locations, high or low movement patterns, location reached situations and standard movement analytics. These examples show that data can be monetized, used to create additional services, and sold to customers so they can gain value. A service provider has a huge number of data streams, often marked as unnecessary, about statuses, usage, technical info, movement and configurations. The problem is how to use those data so that an enterprise in a specific vertical market can increase revenues, lower costs and be more effective in day to day operations. This is why monetization of IoT data is strongly focused on vertical business customer use cases. Data use varies between verticals, answering different needs in, for example, the retail, automotive and energy sectors. 4 WHITE PAPER

5 Predictive maintenance for logistics companies Predictive analytics for air conditioning manufacturers Harvesting optimizations for agriculture manufacturers Usage-based insurance premiums for insurance companies Real-time analytics for smartparking companies THE NEEDS OF VARIOUS VERTICAL MARKETS Manufacturers of air conditioning system parts can use predictive analytics for their production lines to optimize costs and react when an irregularity arises in the supply chain. Thus, the production process is shorter and the company is prepared for any rise in demand for particular products. n A company selling agricultural machines can use data collected from sensors installed in their tractors for remote diagnostics and fleet monitoring, in order to optimize harvesting. n A smart city parking company can use real-time analytics to help drivers to find free parking places, and to view & use traffic data to make changes to parking locations. More cars can therefore use the parking lots, and revenues can increase. n A logistics company sees how vehicles are driven and whether they exceed the speed limit, mostly for predictive maintenance purposes, to predict when the vehicle should be replaced and keep operational costs under control. n An insurance company can use data from car sensors to match an insurance offer to specific driving behavior and customer types, to propose usage-based insurance, and to change offers all of which helps them to compete with other companies. WHITE PAPER 5

6 ANALYTICS AS A SERVICE In all of the above examples, analytics can be also sold in a service model called analytics as a service. More and more services are being sold in such a way, including electronic health record and smart monitoring solutions. A service provider is a privileged company in respect to IoT, as it has technology, access to data, hardware and software, and experience in delivering sophisticated solutions. A CSP also has growth potential and in many cases is ideally placed to cooperate with vertical enterprises that lack such attributes. ANALYTICS FOR SMART TAXIS USE CASE For example, a telco operator with access to connectivity and application data can provide analytics as a service for a smart taxi company. Imagine such a company that would like to analyze: n Historical and current data about where taxis most often start journeys, and common long, short or fast routes. This information is transferred to cars so that they can be in the right place to minimize costs arising from inactivity. n Data about how a car is running, journey length, speed and acceleration, allowing the company to predict when a given car will have to go for maintenance, thus preventing mechanical issues and making additional cost savings. n Data about driver behavior and routes and on this basis calculate the appropriate insurance to save money for the taxi company and generate business for a smart insurance enterprise. n Location data so the company is notified if a taxi leaves the city borders, goes outside the country, or travels beyond the normal area. In such circumstances, additional monitoring procedures can be initiated, helping to keep the taxi driver safe. Trajectories analysis for route optimization Driver behavior analysis for insurance premiums calculation Anomaly detection for maintenance and other cost forecast Car tracking for increased employees safety 6 WHITE PAPER

7 DATA MONETIZATION USE CASES Monetization use cases can generally be divided into groups of business problems they are intended to solve. The first is where data analysis will be used to improve operational efficiency, which might include QoS monitoring, operational usage and performance analytics. Quality data monitoring is especially important in healthcare, where a device malfunction can put a patient s life at risk. Performance analytics is crucial in manufacturing, where it can help to solve efficiency problems. A second use case group includes customer focused operations, such as product management and creating marketing offers. Such data use can be seen in almost any vertical where a given organization wishes to use marketing to address customers appropriately. The final group comprises new business models, which is not in itself strictly defined but is connected with multiple verticals, specific vertical processes or as yet untapped or unknown areas in which the IoT might develop. An example use case in this group might be device movement control, where a service provider can monetize data by selling a service adding value to logistics companies. The logistics firm s headquarters could profit from being notified when a truck is returning - for example, when it is 100km away, so the base can prepare for faster unloading and loading. The same company could also avoid fraud, including potential SIM card theft, by monitoring SIM replacements or device switching to prevent a car SIM being used in another device to make long distance calls. THE STEPS TO DATA MONETIZATION So how can an organization begin monetizing IoT data? First of all, a powerful, real-time actionable analytics tool should be exposed not only to internal users, but also to partners, resellers and customers. External use increases opportunities for additional revenues. Such a tool should also deliver real-time notifications on anomalies, fraud detection, and actions on device lifecycle and quota management. It should gather data from all layers and ecosystem interfaces, and be able to store this information in huge volumes. Secondly, the service provider must understand and define how such an analytics system provides business value to a particular vertical, use case or business requirement. For example, a healthcare company that delivers medical supplies may want to monitor temperature constantly, but won t be so concerned about speed or fuel consumption. A well-digging company, on the other hand, will gain most benefit from data about travel time, how long excavation work takes, and location data while information on data and SMS use won t be so important. A vertical should profit from a simple and well-suited solution. Thirdly, a service provider has to be open on the architectural and functional level, so that APIs can access data, external data flows can be imported, compared, collated and assembled for external use. WHERE IS THE MONEY? Finally, it is mainly in the analytics as a service model that real profit can be seen. A vertical company primarily needs their service provider to be a partner to help them understand the data they produce, build better products, resolve operational and business issues, and develop and compete in the multi-faceted and technologically advanced IoT world. WHITE PAPER 7

8 CONTACT US Visit for the contact information of our offices in the following countries: Albania Austria Argentina Belgium Brazil Canada Chile China Colombia Finland France Germany Italy Japan Malaysia Mexico Luxembourg Panama Peru Poland Russia Saudi Arabia Singapore Spain Sweden Switzerland Thailand UK Ukraine United Arab Emirates USA ABOUT COMARCH Since 1993, Comarch s specialist telco solutions business unit has worked with some of the biggest telecoms companies in the world to transform their business operations. Our industry-recognised telco OSS and BSS solutions help telecoms companies streamline their business processes and simplify their systems to increase business efficiency and revenue, as well as to improve the customer experience and help telcos bring innovative services to market Comarch s telco solutions customers include Telefónica, Deutsche Telekom, Vodafone, KPN and Orange. Copyright Comarch All Rights Reserved telecoms.comarch.com