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Graph computing technology essentially is a form of augmented intelligence (also shortened as AI, unlike artificial intelligence, it focuses more on leveraging graph’s superior computing power therefore augmenting human intelligence while analyzing data with celerity, depth, finer granularity, and flexibility). Graph augmented intelligence is much needed in many BI scenarios. According to a recent report by Gartner (2021), 80% of BI innovation will be powered with graph analytics by 2025. The % was at 50% by 2023 in Gartner’s 2020 projection.


BI encompasses how an enterprise acts upon its valuable business data. With graph augmented intelligence, a.k.a. BI+AI, a lot of unprecedented business scenarios can be empowered and realized with much faster time-to-value, lower TCO and higher ROI. The innovation and benefits of graph are illustrated in previous use cases, such as Liquidity Risk Management, Asset & Liability Management and Real-time Decision Making. In this use case, we’ll show you how graph computing and machine learning join force in a 1+1 >> 2 way to offer what traditional AI/ML couldn’t achieve.

Significance

This use case illustrates that graph technology can be leveraged as a booster for traditional ML/DL technologies, and it works out perfectly fine. The core concept is to unify BI and AI, so that business can be propelled forward swiftly. On the other hand, we all need to look at the efficiency, accuracy and efficacy of big-data and AI, while they are being deployed everywhere, do they deliver what they originally promised? The future of AI lies with augmented intelligence, which is a perfect companion of BI.

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