5 Phases of an AI Data Audit – Assessing Opportunity in the Enterprise

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

5 Phases of a Data Audit 950×540

Emerj serves enterprises to form their AI strategies, and data audits are part of Emerj's framework for identifying high-ROI AI projects. In this article, we'll break down a slice of this framework, walking through some pragmatic steps leaders can take to drive toward industry-leading outcomes in their organization.

In the domain of artificial intelligence, it’s known that experienced data scientists are needed for architecting effective solutions, and it’s also recognized that business leaders and subject matter experts must have a clear vision to drive toward successful business outcomes for AI projects.

The overlap needed to empower effective strategic decisions about investments in AI is less clear. While executive leaders needn't become data scientists, it’s critical that business leaders have a practical and conceptual - if not technical - understanding of the organization's data and infrastructure as a gateway to understanding AI opportunities in their organization.


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