2022 Development of Data and AI Integration in China

Source:iResearch July 29,20226:31 PM

Traditional data warehouse and the separation of data lake and data warehouse make data and AI integration and enterprises’ agile decision-making difficult. Since data silos still exist, decision-making is not based on full data. The circulation of data results in high costs, long cycle and lack of timeliness. Based on separated storage, cache and computing, data lake, data warehouse and AI data unify metadata management, which can achieve the best result in data volume, cost, efficiency and agility。

Open-source model contributes a lot to the ecology of data and AI. However, this doesn't mean all companies need to build their own data and intelligence platforms through open-source products. In fact, most companies focus on their core businesses and choose commercial data and intelligence platforms that have stable performance, data-intelligence integration, end-to-end automation and intelligence and do not need operation or maintenance. Enterprises that are more flexible and open have lower IT talent replenishment costs.

Rising Data Volume and Unstructured Data Proportion 

Global data volume is surging at an annual growth rate of over 59%. 80% of the data are unstructured or semi-structured. The amount of data in China is rising at a higher rate. Object storage-based data lakes are increasingly common thanks to the rising data volume and proportion of non-structured data. Unified management, query, and use of data are the new challenges.

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