Achieve more leeways in handling your data.
The modeling of a graph-based data platform allows you to evaluate your data much more flexibly and to use it in a process-optimized way - independent of your other proprietary software applications such as ERP or CRM systems.
The visualization of query results as a graph creates transparency and guarantees a high user acceptance.
Working with the "original data", the direct availability of work results from other departments and the decoupling and parallelization of task completion bring an enormous increase in speed and allow significantly more agility in daily business.
Every data-object ("node") is a potential anchor-point for further relations to new data-objects. The system can be expanded at any time and can be ideally introduced using agile methods. A "big bang" is not necessary, you can start with a focused part of the data network and then expand as needed ("think big - start small")
Modeling does not reflect your your current organizational structure or the production processes and is not limited to your own company. The possibility of integrating partner data and different domains (compliance regulations) is pre-condition for covering legally obligations and using "open data".
Relational database models are basically "designed" for a specific application. Queries that were not conceived in the original scenario may not be realizable at all or only with great restrictions.
A graph-based data platform allows the definition of any queries that generate cross-domain views with enormous performance.
The modeling of e.g. abstract effect structures and functional relationships - in addition to the technical implementation - allows the explicit mapping of knowledge that was previously only anchored in the heads of individual employees.
Every data atom stays in the system from it's moment of creation. Deleting is not possible, only deactivating is offered. Thus a sudden loss of data is prevented. Every node has its own history where the explicit documentation of changes and users can be found. The end result is a kind of "semantic blockchain" - a documentation of a product including its history of creating.
A consistent, graph-based database is a basic prerequisite for further considerations in the direction of AI/KI and learning systems.
Following the principle: "Before you start talking about big data, take care of your small data