中小企业提升数据成熟度，应先夯实知识基础。
Reviving our data foundations is the most disruptive step to data maturity
- 以模块化动态方式构建跨职能知识图谱
- 低侵入式策略适应现有数据流程
- 用实证说服决策层，重在数据根基
中小型成熟度企业要充分把握当前AI技术进步，最关键的突破是摒弃技术噱头，回归根本，建立或重建坚实的知识基础层。这一建议虽难说服管理层，但需以证据支撑，且实施过程应尽量减少对现有流程的影响。本文提出应重新定义能打动决策者的证据形式，并设计一种低影响的数据策略，以适应企业内持续变化的数据流与业务流程。我们坚信，只要采用模块化、动态化和跨职能的方式设计，知识图谱技术将在人工智能驱动的企业数据战略中变得不可或缺。
原文摘要 · Abstract (English)
The most disruptive step that enterprises of small-medium size and maturity can take to make the most of the latest technological advances in AI is to step back from the hype and focus on establishing or reviving a good knowledge foundation layer. It is a hard message to present to the executive team; therefore, it needs to be backed by evidence, and its implementation needs to be of minimal impact on the existing processes. In this vision statement, we discuss how we need to rethink what evidence speaks to the decision-makers and propose a low-impact data strategy that adapts to the existing and ever-changing data flows and processes across the company. We firmly believe that knowledge graph techniques will increasingly become non-negotiable in the data strategy of an AI-powered enterprise, provided that we approach their design in a modular, dynamic and cross-functional way.
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