How to build data science platforms - Part 4: Database scalability and business models
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What does a modern data science platform need to offer companies real added value?
Without data, there are no data products or analyses! What sounds so banal is one of the most important requirements for modern data science platforms: The possibility to connect various data sources and develop new use cases, data products and entire business models. Let’s take a quick look at what this is all about.
New business models are emerging instead of result islands
Data projects often aim at a result – they are developed for a specific purpose and answer the corresponding questions comprehensively. By their very nature, however, these are often result or project islands. There is no real exchange within the company.
However, the findings from the analysis projects can also be used to create new business areas. The best example of this is the development of new sales potential from a predictive maintenance approach. Instead of minimizing downtimes in their own production and manufacturing, companies can use it to expand their range of services. Alternately to pure sales and maintenance, machine builders can provide proactive support and pure machine availability.
Data science platforms such as YUNA automatically detect machine downtimes or underutilized capacities. The identified, unused resources can be offered as services to other companies by the owners of the machine parks and thus generate new revenues. Even more important: The sustainability of the companies is also promoted in this way.
In addition, these platforms put companies in an even stronger position to take active action. New digital services and process solutions can be developed much more quickly and independently. Innovative strength grows and the market position is consolidated.
CONCLUSION: Data science platforms can be used to identify new relations. By providing information for different user groups, these findings can be used to identify new sales channels and business fields and to successfully open them up.
Data scalability must be guaranteed
It is not only in biology that growth is an incremental process for one’s own existence. In the economy, too, growth is an indicator of a company’s health and therefore a “natural” process. Platforms must also be able to process growing numbers of users and data volumes. This includes the connection of various physical and digital data sources, such as sensors, CRM data or entire (database) systems. Only when the various pieces of information converge at a central point, real insights can be generated and projects implemented which support the company in its strategy and strengthen its market position.
The same applies to the application by different user groups. Only when the broad mass in a company can use a platform, i.e. also beyond the technical specialists, information and connections can be discovered and implemented promisingly in new analysis projects. At the same time, existing processes are constantly optimized and adapted to new circumstances. Ideally, the company can use one and the same platform for the various projects, such as predictive maintenance, energy management and shopping basket analysis. Only if ALL projects can be planned, developed, started and managed in ONE environment and most of the company can independently contribute projects, real scalability is guaranteed.
CONCLUSION: For companies, platforms offer immense added value if they can process different use cases and data types. In the best case, they establish a connection between the individual use cases.
In the next part: Data visualization and information content