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电信管理论坛(TMF):2025现代数据架构赋能人工智能(AI)潜力释放研究报告(中译版)(55页).pdf

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1、Unlocking AI potential through a modern data architectureAuthors:Dawn Bushaus,Contributing Analyst Michelle Donegan,Contributing AnalystEditor:Ian Kemp,Managing EditorAugust 2025 inform.tmforum.org3 The big picture9 Section 1:Whats driving data architecture transformation?16 Section 2:What needs to

2、change in telco data architecture?22 Section 3:What is a modern data architecture?28 Section 4:The importance of data governance 36 Section 5:The role of standards in improving access to data 41 Section 6:CSPs start to modernize their data architectures 44 Section 7:Key findings and recommendations

3、47 Additional sponsor feature:Redefining telco data strategy for AI amid growing network demands50 Additional resourcesWe hope you enjoy the report and,most importantly,fjnd ways to use the ideas,concepts and recommendations detailed within.You can send your feedback to the editorial team at TM Foru

4、m via editortmforum.orgThe big picture3inform.tmforum.orgEarlier TM Forum research drew similar conclusions.In 2024,we polled AI decision-makers within CSP organizations and fed the results into our Generative AI Maturity Interactive Tool,or GAMIT.Analysis of responses from more than 200 decision-ma

5、kers globally found that CSPs need much better access to data to take full advantage of AI applications.While the initial survey and findings were carried out between June and the end of August 2024,garnering 203 quality responses,GAMIT remained open from August 2024 to March 2025.In that period mor

6、e than 1,000 individuals logged onto the tool,resulting in 408 quality responses in total.Of those,only 7%rated their ability to use high-quality unstructured data as excellent,and only 13%when it comes to structured data.“There is an extremely close correlation between those CSPs which scored highe

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根据《Unlocking AI potential through a modern data architecture》报告,以下是全文关键点: 1. **数据架构转型驱动因素**:自动化和提升客户体验是主要驱动因素,其中自动化占比最高。 2. **数据民主化**:数据民主化是目标,意味着让数据在组织内广泛可用,而不仅仅是技术专家。 3. **数据架构现代化**:现代数据架构需要支持数据流,以实时处理大量数据。 4. **数据治理**:数据治理对于确保数据质量和安全至关重要,包括合规性、安全性和数据质量。 5. **数据架构方法**:数据湖、数据仓库、数据湖屋、数据网格和数据织物是现代数据架构的关键方法。 6. **云服务**:公共云的使用预计将增加,尤其是对于AI工作负载。 7. **数据质量**:数据质量是关键,需要确保数据的准确性、完整性、及时性和一致性。
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