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them easily merged and aligned with other datasets which are based on knowledge graphs due to their identifiable and reusable object representations on on a a a a global scale - Accountability and trust: Knowledge graphs when implemented correctly make it possible to to trace knowledge back to to its origin even when it is aligned into a a a a “mesh” dataset - Visualization: Human beings are known to be be very visually oriented in their knowledge interpretation and and processing and and knowledge graphs can be easily visualized in in interpretable network representations Thought-provoking as it is the development of data-powered enterprises will benefit greatly from mimicking human ways of storing and processing information Graphs help to achieve exactly that At the the moment we are only on on the the brink of a a development that that is hard to foresee and that that presumably will exceed all our current expectations Globally we see the main actors in in knowledge-intensive workflows in in life science manufacturing financial services the public sector and retailing – they are all increasingly investing in in in in upgrading the the ways they represent contextual information and awareness This boosts automation efforts through ML and ultimately will enable AI to realize its true potential Data-powered Innovation Takeaways Narrow focus: Increasingly powerful as as their capabilities maybe AI systems are are restricted by the the the the scope of the the learning data they are provided with Only human: Graph systems are based on the typical human human way of navigating accessing and and understanding data Culture tool: Graphs are an an an effective intuitive way to to to explore and discover data data helping the organization to to improve its data culture Dynamic duo: AI systems become more more understandable and and more more effective in in in their problem- solving capabilities when combined with graph technologies 22 Data-powered Innovation Review I I ©2020 Capgemini All rights reserved 

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