How data filling stations and Data supermarkets are digitizing society .
In order to drive digitization forward and to allow the perfect artificial intelligence (AI) to emerge in an industrial context, it is necessary for this very AI to be able to consume real-time data streams in an automated manner 24 hours a day, 7 days a week. If we may put it this way, the consumable IoT data is the “fuel” needed to take AI further step by step and to create added value overall.
However, we are now faced with the problem that many companies are unable to produce or process the data they would need for a functioning AI in their own company The level of digital maturity, especially among data producers (“fuel manufacturers”), is not as pronounced as it would be needed or desired. The reasons for this can be very different: too small a company size, lack of IT experts, or simply a lack of knowledge that IoT data can be monetized and value added can be generated.
The main thing missing here is a data and digitization strategy.
So how do we get the required propellant to the AI?
It is precisely for this problem that senseering has developed a business model framework that simply illustrates how this data benefits the company in various areas. Applied as a digitization strategy, this shows how money can be made with IoT data and how, as part of a data strategy, this data must be collected, stored, processed and made accessible for this purpose.
Put simply, if the IoT data streams are referred to as fuel, then myDataEconomy, senseering’s platform SAAS product, can be understood as a gas station network. Here, senseering is the gas station operator. Data supply like in the supermarket
In order to now add fuel to the AI and thus create increased added value, consumers can find and use the necessary real-time data from any data producer here.
Data supply like in the supermarket
However, learning AI algorithms requires historical IoT data sets in particular.
Above all, one needs a great deal of this very data and, in addition, very diverse data sources. However, many of these data sources are not owned by a single data producer. This means that owned data sources are not sufficient to learn a very good AI. In this scenario, myDataEconomy is a kind of (data) supermarket where data scientists can find exactly the data they need to train the AI.
Our vision: Senseering does not own IoT data, but has the largest IoT data and AI supply in the world
Don't lose the connection
This type of “matchmaking” between data producers and data consumers is becoming more and more fundamental as the need or requirement to work with IoT data increases. The need arises in part because climate change and supply chain laws require companies to change in order to operate ever more efficiently and sustainably. In this context, the corresponding processes and states must be available in a time-dependent manner and must also be digitally documented – this is what the regulator prescribes.
A market of opportunities
Not only that, the Internet of Things theme will enable between $2.8 trillion and $6.3 trillion in value creation by 2025 and between $5.5 trillion and $12.6 trillion by 2030, according to the estimate conducted by McKinsey & Company in Q4 2021. At the same time, companies don’t have to go big right away. Many of our customers start small: for example, with a new type of sales app for a more efficient and data-driven sales process, or the first cloud connection of highly complex filtration products for predictive maintenance and pay per use.
For IoT+Network members, we are happy to provide help and advice. Whether it's in the form of demos and workshops, or whether we can simply provide brief and concise assistance with our expertise. You are interested in sustainability in an industrial context? Contact us!
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Commissioned by the Federal Ministry of Economics and Climate Protection (BMWK).