When looking at pure numbers, according to data analyst Gartner [1] the facts around connected “things” in the relevant markets Greater China, North America and Western Europe are impressive: 8.4 billion worldwide in 2017, a 31 percent increase from 2016, and the total spending on endpoints and services will reach almost $2 trillion in 2017. Internet of Things (IoT) has become increasingly important for the producing industry and is growing at a fast pace. IoT is deployed in areas such as health monitoring, asset tracking, environmental monitoring, predictive maintenance and home automation. Approximately 60 % of the spending is for consumer devices like smart TVs or connected home, however 40 % are spent in the industry i.e. for smart sensors or manufacturing field devices [1]. The increasing importance of cross-industry devices / information is driven by higher-volume, lower cost devices to share data between different applications.

The Internet of Things offers a potential economic impact of $4 trillion to $11 trillion a year in 2025.

Although most people are aware of the importance of IoT for their business, many companies still seem to struggle with “going digital” with their products or services. IPG GROUP consolidate important facts digital managers should keep in mind before they start their IoT initiative.

1 Understand IoT – It’s not just technology

Connectivity is only the starting point which brings together various inputs to a higher technological and commercial picture. Numerous technological and other elements such as analytics, artificial intelligence, connectivity technologies, networks, cloud computing, etc. build the fundament for new applications and finally business models. Digital manager must understand and consider the complete circle from IoT devices to customer benefit during the development and marketing cycle.

2 Start with the customer benefit

IoT is associated with high-tech solutions which is often perceived as too far away from the customer. But similar to any innovation, IoT projects should start with the customer experience of the final product in order to stay focused from the beginning. Your company can contribute to any individual element shown in the graph above (e.g. Big Data analyst, sensor producer, cloud service provider) or cover the complete spectrum from IoT device to user experience. Apart from your portfolio it is strongly recommended to investigate the customer value chain and to challenge existing processes at the beginning. Especially in the producing and transport industry key customers can be accessed for a collaborative approach. Once the user need is extracted you can define your project and investigate internal resources or potential collaboration partners to cover all elements of the above described IoT value creation circle. There are also large amounts of IoT use case on the internet as a reference for your business. For example, IoTOne is a great source of information for case studies including hardware, software, and sensor information.

3 Select an IoT technology you can build on

IoT isn't only for the big players, it is a great opportunity for small and medium enterprises (SMEs). Smaller companies often have higher agility to implement practical initiatives for both internal operations and commercialized solutions. Especially if you are in a niche and you discover a unique use for IoT, consider commercialization. SMEs often cannot cope with lengthy IT project which will pay off after multiple years. Look for practical IoT that will work for your company right from the start. Like with every other IT solution begin with a small project you can overview and develop a roadmap for future extensions. This is independent on whether you want to extend your product portfolio by selling smart products or want to improve your internal operations.

4 Don’t fail to use your data

According to a study conducted by McKinsey [2], most of the data generated by IoT sensors is not used. Even worse, only retrospective actions (repairs, tracking of operational issues) are taken based on the gathered information instead of proactively leveraging on the value. Especially prediction of events and optimization of processes powered by big data analysis brings in multiple opportunities for business models. As an example, a manufacturer of machines not only has the possibility to offer new service plans based on predictive maintenance but can also improve future products based on data. The major challenge is integrating and analyzing data from multiple and often heterogenous sources, especially with all reservations regarding cyber / data security. However, many branches could or can heavily benefit from multiple source data. Predictive models based on artificial intelligence analytics of legacy and real-time IoT data are expected to manage the increasing patient traffic of an aging population in hospitals and improve general operational efficiency [3]. In retail, customer behavioral patterns in combination with current events (seasons, sport events) can move on-demand-inventory to data-driven marketing.

5 Create a data visualization strategy

To maximize the value of IoT data, we need to learn how to integrate and analyze data from many IoT systems. In this case, IoT adopters will be able to see the full picture and make more objective data-driven decisions. Also dealing with large amounts of data can be overwhelming and result in getting “swamped”. But only deep insight into the streams of data allows companies to solve business problems, increase sales, cut costs, or find new revenue streams. A strategic approach can be broken down into three major steps: Cleaning, analyzing and finally visualization. The starting point is tidying up scattered data to separate the important from biased values or simply poor data. In a next step data manager should look into relevant information to be able to derive correct conclusions for the business – similar to a data cockpit. Last, visualization is used to derive value from data, which is also one of the most important steps. It determines how efficiently analysts can work with data assets, what insights they are able to extract and how their data strategy will develop over time. The quality and capabilities of data visualization directly influences your business strategy and potential benefits data applications can bring to the companies [4].

[1] https://www.gartner.com/newsroom/id/3598917
[2] https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world
[3] https://medium.com/iotforall/what-to-do-with-your-iot-data-in-2018-4fc408ed18a9
[4] https://medium.com/iotforall/why-you-need-a-data-visualization-strategy-fda3f872b29