This story was originally published on HackerNoon at: https://hackernoon.com/clarifying-the-difference-between-data-strategy-analytics-and-ai-governance. This article examines the structural distinctions between Data & Analytics (D&A) Strategy, D&A Governance, Data Governance, and AI Governance within enterprise Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-governance, #ai-governance, #responsible-ai, #data-strategy, #ethical-ai, #ai-trust-and-safety, #enterprise-information-systems, #data-analytics-strategy, and more. This story was written by: @susmit82. Learn more about this writer by checking @susmit82's about page, and for more stories, please visit hackernoon.com. Organizations often struggle to scale analytics and AI because strategy and governance are blurred. This article clarifies four distinct but connected layers: D&A Strategy defines where and why data, analytics, and AI create business value. D&A Governance defines how decisions are made, prioritized, and tracked at the enterprise level. Data Governance ensures data can be trusted through ownership, quality, and compliance controls. AI Governance ensures AI decisions can be trusted through risk, explainability, and lifecycle controls. The paper proposes a hierarchical framework aligning these layers to prevent pilot sprawl, reduce AI risk, and enable scalable, value-driven analytics across industries such as mining, banking, healthcare, retail, and energy.
