Data has been used strategically by companies for a long time, whether to generate reports or to segment customers.
Now, though, in addition to using data as a resource for corporate activities, companies are also producing pure 'Data Products' as a direct source of revenue.
We divide Data Products into three different categories:
- Data as a Service. In this case, data itself is the product. This category includes securities indices, address data, weather data, etc.
- Data-Enhanced Products. These are data-based additional functions which modify a traditional product to increase its value (turning vehicles into self-driving vehicles, for example).
- Data as Insights. Data on the perception or the usage of a product is used to improve the way a product is marketed (a function which makes the data valuable).
The shift from 'data as a means to an end' to 'data as a basis for adding value' represents a new perspective, one which has arisen as a result of the digital transformation. The management of Data Products, too, differs from the management of other products in several key aspects. i.e. when it comes to analysing the value-added chain, modelling markets, determining customer tasks and (thus) in defining the value proposition.
For many years, inovex has focused strategically and technologically on the specifics of Data Products. We are therefore in a position to provide comprehensive support for our customers. We help them to use existing data pools to add value, to identify missing data for a valuable product, to define new offerings, to open up new customer groups to offer data to, to generate, test and implement ideas for innovative business models, and (of course) for all technical questions relating to data management and data analysis. Our highly qualified team of Big Data engineers and data scientists means that we have all the requisite implementation expertise under one roof.
Further reading: “Datenprodukte für Deutschlands größten Fahrzeugmarkt“ (PDF, 1.05 MB) (in German)
Further reading: “Erfolgsfaktoren von Datenprodukten“ (PDF, 2.04 MB) (in German)
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