About
Enterprise intelligence: enablement, governance, strategy and ethics
Iryna Feldman works on the enablement, governance, strategy and ethics of enterprise intelligence: the framework, the policies and the cross-functional process that let an organization adopt it responsibly, built on nearly two decades in enterprise data and analytics. This site is where, with a little help from her 'friend', she writes about that journey: what works, what does not, and what responsible practice looks like in a regulated industry. The views here are her own.
Getting an organization from curiosity to intelligence in production, with the business owning the use cases.
Enterprise-wide business enablement built from inception: the intake, the cross-functional process with Legal, Compliance, Privacy and IT, and the partnership with executive leadership on where intelligence goes first.
The guardrails that let an organization move: adoption inside real regulatory constraints.
Chairs the enterprise AI governance committee; built the framework and policies from scratch and runs them through real regulatory review.
Connecting investment in intelligence to outcomes leadership actually tracks, sequenced against the data it depends on.
Partners with executive leadership on data and intelligence strategy; business owner in the enterprise data modernization program, including the lakehouse initiative.
Fairness, transparency and accountability treated as design requirements, not a review at the end.
Google Cloud Generative AI, LLMs and Responsible AI credential; MIT CDOIQ review board; the ethics and governance lane of her open-source work.
The data foundation intelligence stands on: trustworthy enough that people stop keeping their own copy.
Analytics roadmaps with defined success metrics; founded the Data Governance Committee; an enterprise data catalog and a BI platform rebuilt on a modern SaaS stack; machine learning in production in the cloud.
Hiring, structuring and keeping the teams that senior people want to stay on.
Leads an enterprise data organization spanning data engineering, AI engineering, BI and data science; retention and engagement that senior people notice; a leadership pipeline built for emerging talent.
MIT International Conference on Information Quality
Evaluated submissions on data strategy, governance and analytics leadership for the 18th Annual Symposium.
Reference →CDO Magazine
Panelist on enterprise adoption of machine learning and NLP.
Email and LinkedIn are the fastest routes for speaking engagements, panel invitations, or professional inquiries.