Published: 20 February 2024
尽管经过数十年的努力，许多数据和分析治理及主数据管理（MDM）项目仍然以失败告终，在编制数据问题目录和建立大型委员会方面投入再多精力也收效甚微。尽管并不缺少成功的最佳实践，但这些实践并未得到广泛采用。数据和分析（D&A）领导者只有摒弃过时的做法，才有可能取得成功。
尽管经过多年的努力，以及无数失败的尝试，数据和分析治理依然是许多企业机构面临的一项重大挑战，而其希望采用的最佳实践已然过时。
数据产品中D&A技术采用的热度高涨，但由于治理不力，缺乏与实际业务需求的协调，越来越多的数据产品、计划和项目未能获得成功。
AI技术的热度正盛、采用激增，企业机构可借此机会更新过时的D&A治理实践，使AI治理也能够利用此类最佳实践。
企业机构如果希望通过以控制为导向或以合规为重点的D&A治理方式获得收益，则很难争取到业务领导者的支持，因为这些方法无法展现D&A投资的整体价值。
技术供应商声称，AI和机器学习（ML）（尤其是生成式人工智能[GenAI]）能够实现D&A治理的自动化，但事实并非如此。
将D&A治理工作与D&A战略和优先业务成果挂钩，合理调整D&A治理工作。避免在初始阶段就设立大型委员会、制定长达50页的规划或建立数据目录。在实现业务成果的过程中，可根据需要逐渐完善这些项目。
采用以自适应D&A治理为中心的灵活方法，避免对相关工作进行静态、过时和僵化的假设。这项工作应根据业务需求进行扩展和灵活调整。
不应仅仅根据数据产品的受欢迎程度来决定是否开发产品。如果没有“市场”或缺乏需要复用数据或洞察的明确业务需求，那么生产这些数据产品就会造成浪费。
避免受到AI和ML以及GenAI热炒的影响，充分了解自动化和识别技术在D&A治理相关具体工作实践（如实体发现和解析）中的作用（如数据质量）。
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Analysts:
Andrew White, Guido De Simoni, Saul Judah, Sally Parker, Donna Medeiros, Lydia Clougherty Jones, David Pidsley, Sarah Turkaly
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