Enterprise Data Governance Plan for Artificial Intelligence Initiatives: A Framework for Innovate Software Consulting Inc Ltd

Paper Details
Manuscript ID: 2126-0424-4077
Vol.: 2 Issue: 3 Pages: 36-54 Mar - 2026 Subject: Artificial Intelligence And Machine Learning Language: English
ISSN: 3068-1995 Online ISSN: 3068-109X DOI: https://doi.org/10.64823/ijter.2603004
Abstract

This paper presents an enterprise data governance plan for all artificial intelligence (AI) projects at Innovate Software Consulting Inc Ltd. The company offers Oracle Human Capital Management (HCM) Cloud services, business-to-business (B2B) credit risk tools, electronic Integrated Healthcare Management Systems (e-IHMS), and enterprise analytics solutions. The plan covers seven governance areas: data ownership, data quality, privacy and security, lifecycle management, accountability, ethical AI, and ongoing compliance monitoring. It builds on the AI vision statement, ethical AI framework, team structure proposal, and executive review exercise. Two AI tools, Claude (Anthropic) and Gemini (Google), simulate reviews from four C-suite leaders: Chief Legal Counsel, Chief Financial Officer, Chief Operating Officer, and Chief Executive Officer. A critical reflection ties together the feedback and identifies areas for improvement. The plan follows the NIST AI Risk Management Framework and maps to GDPR, CCPA, HIPAA, FCRA, and ECOA rules.

Keywords
Data Governance Artificial Intelligence Enterprise Stewardship Ethical AI NIST AI RMF Data Quality Privacy Compliance RACI Accountability Hub-and-Spoke Governance Synthetic Data Bias Mitigation
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Cite this Article

Shivanand R Koppalkar (2026). Enterprise Data Governance Plan for Artificial Intelligence Initiatives: A Framework for Innovate Software Consulting Inc Ltd. International Journal of Technology & Emerging Research (IJTER), 2(3), 36-54. https://doi.org/10.64823/ijter.2603004

BibTeX
@article{ijter2026212604244077,
  author = {Shivanand R Koppalkar},
  title = {Enterprise Data Governance Plan for Artificial Intelligence Initiatives: A Framework for Innovate Software Consulting Inc Ltd},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {3},
  pages = {36-54},
  doi =  {10.64823/ijter.2603004},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212604244077/enterprise-data-governance-plan-for-artificial-intelligence-initiatives-a-framework-for-innovate-software-consulting-inc-ltd},
  abstract = {This paper presents an enterprise data governance plan for all artificial intelligence (AI) projects at Innovate Software Consulting Inc Ltd. The company offers Oracle Human Capital Management (HCM) Cloud services, business-to-business (B2B) credit risk tools, electronic Integrated Healthcare Management Systems (e-IHMS), and enterprise analytics solutions. The plan covers seven governance areas: data ownership, data quality, privacy and security, lifecycle management, accountability, ethical AI, and ongoing compliance monitoring. It builds on the AI vision statement, ethical AI framework, team structure proposal, and executive review exercise. Two AI tools, Claude (Anthropic) and Gemini (Google), simulate reviews from four C-suite leaders: Chief Legal Counsel, Chief Financial Officer, Chief Operating Officer, and Chief Executive Officer. A critical reflection ties together the feedback and identifies areas for improvement. The plan follows the NIST AI Risk Management Framework and maps to GDPR, CCPA, HIPAA, FCRA, and ECOA rules.},
  keywords = {Data Governance, Artificial Intelligence, Enterprise Stewardship, Ethical AI, NIST AI RMF, Data Quality, Privacy Compliance, RACI Accountability, Hub-and-Spoke Governance, Synthetic Data, Bias Mitigation},
  month = {Apr},
}
Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.