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Geschäftsmodell: Grundlagen der Datenmonetarisierung

Interessanter Artikel über verschiedene Geschäftsmodelle zur Datenmonetarisierung.

External data monetization models vary by level of value impact to customers, analytics sophistication, and revenue potential.

Data as a service. Also known as data syndication, this is the simplest of the three business models. Anonymized and aggregated data are sold either to intermediate companies or end customers who mine the data for insights. For example, telecommunications companies provide aggregated and anonymized customer geolocation data to local governments, allowing city planners to design more effective traffic management systems and officials to better establish “smart city” technology solutions. Customers can also be the downstream or upstream players in a company’s value chain: Grocery retailer Kroger captures shopping data generated by its rewards card and sells it to consumer packaged-goods companies thirsty for a deeper understanding of their customers’ shopping habits and evolving tastes and preferences.
Insight as a service. Companies also can combine internal and external data sources, applying advanced analytics to provide actionable insights. AkzoNobel has created a decision-support model for ship operators to enable fuel and CO2 savings. They make available to ship operators and owners an advanced analytics-enabled mobile iOS app that provides continuous performance prediction of coating technologies. This approach empowers vessel operators by allowing financial and performance benefit analysis of coating choices, thus optimizing important investment decisions.
Analytics-enabled platform as a service. This is the most complex of the three business models, and it offers the greatest value to customers. Companies use sophisticated and proprietary algorithms to generate enriched, highly transformed, customized real-time data delivered to customers via cloud-based, self-service platforms. The model allows access to new markets, sometimes building an entirely new business. One example, GE’s Predix platform, provides additional value to customers through data-based services that increase the efficiency of its machines. GE delivers integrated and technology-enabled energy management systems (EMS) for lighting and energy to commercial, industrial, and municipal customers, such as San Diego, California and Jacksonville, Florida. They combine the capabilities of GE’s energy-efficient LEDs, cutting-edge sensors, cloud-based software, and advanced analytical models. Through Predix, GE makes predictive and prescriptive analysis available to its customers around energy use, maintenance, and other outcomes, allowing cost-reduction decisions by simplifying energy processes, leading to automation and operational efficiencies.

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