Enabling IoT to Establish a Sustainable Value Chain

Alexander Alten-Lorenz, Chief Architect, Digital Development & Technology, E.ON SE

Alexander Alten-Lorenz, Chief Architect, Digital Development & Technology, E.ON SE

IoT devices are becoming more and more intelligent and can now create meshed networks by themselves, switching from a sensor into an actor and transferring information solely for meshed neighbors. For example, a connected car could tell a future home that the homeowner will be at home in five minutes and the garage door and the door need to be unlocked in time, the lights need to be switched on, and the grid operator needs to be informed that the wallbox is now charging at 22 kV. In the near future, this will happen over direct meshed information cells, operated by connected devices, wearables, sensors, actors and mobile devices – in short: everything. And all cloud providers offer dozens of solutions to master the challenges in a number of different ways.

Self-organizing mesh networking and communication comes with a permanent flow of information, with massive IoT data streams; even classic Big Data frameworks such as Hadoop cannot handle this in a timely manner anymore. Coming along with the art of data, the need for data processing changes with the kind of data creation and ingestion. Most analyses will be done on the edge and during the ingestion stream when the data comes to rest. The data lake should be the central core to store data, but the data needs to be categorized and catalogued together with a proper, well-defined scheme and data description. The intended use of gravity generates needs to be applied as the motor of data-driven innovation.

Why? Batched processing helps predict value out of stored data while analyzing multiple other data points and storage facilities, but not to react in time. And timely information in IoT enables business processes to have valuable meaning at the time they occur. To do the job, stream processing frameworks such as Spark or Kafka are more suitable. Combining both techniques brings unmatched value and impact to the business, driven by the right use of data.

Big data needs to have almost perfect data management, data rights and data retention processes behind it

Stream processing during data transportation closes the gap between rapid data and data on rest. Mostly referring to the more costly IoT at edge computing, MQTT-enabled stream processing engines deliver high throughput over all kinds of compute instances, be it in a local data center, hybrid clouds or in public clouds.

The same is countable for available cloud technology. Every cloud provider has its own IoT solution zoo with its own lock-ins, but often they do not fit in with scaling plans either in complexity, missing or not well-implemented parts, or simply because the price model is not comparable to the margin from an IoT-based product. A combined approach of scalable cloud technology (which fits most) and own developments brings the most benefit at an affordable price tag, without mentioning the intellectual property a business gains and holds, instead of bringing this to providers and, therefore, competitors. Independent organizations such as “Linux Foundation Edge” provide the most useful insight into open source projects and initiatives.

Simply dumping data without having a vision behind it does not help to solve the problems companies face on their digital journey, especially when it comes to questions of revenue from IoT projects. Big data needs to have almost perfect data management, data rights and data retention processes behind it. Only this offers the possibilities to gain the full advantage of any kind of data, to open new revenues and sales streams, and to finally see all data-driven activity not as a cost-saving project (as most agencies and vendors promise) but as a revenue-creation project. Using modern cloud technologies moves organizations into the data-centric world, focusing on business and not operations.

Analyzing the data is the trickier part here—on the one hand, every data point brings valuable input, but, on the other hand, unlimited data storage also brings vulnerabilities to customer insights. 360-degree approaches are slightly concerning. At first, the value part of data collection needs to be questioned: which data is relevant for support, maintenance or emergencies, and which is important to generate sustainable revenue. Using streaming analysis provides valuable input at the point in time the information is needed to make decisions, but also opens up the possibility to route data into different data stores. It is always unquestionable that the value of customers is higher than the data gathered; implementing a state-of-the-art data ethic catalog is one of the main tasks analytics needs to cover.

We are moving quickly to a so-called interconnected world; continuously connected systems will dominate our future lives, introducing new business models by combining business areas, which were not even in the range of combined business models. The future CIO needs to know what implications data has and what uncountable value this data can generate, and also weight what threats uncontrollable data collection can cause. Building new data-driven business will be an incredibly exciting job in the future: things never done before are now possible. Embrace this.