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Owned by Anas

Data Governance Circle

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A global community for data professionals and business leaders to learn, share, and grow together around Data Governance best practices.

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49 contributions to Data Governance Circle
Introduction
Greetings everyone. I’m Avo Semerjian, based in Los Angeles, with extensive experience leading global cybersecurity and data privacy initiatives across hybrid environments. I look forward to learning from this group and contributing where I can. Nice to meet you all.
1 like • 20h
Hi Avo, great to see you here! Managing privacy across hybrid environments is a massive challenge right now—glad to have a veteran in the room. We look forward to learning from your experience on the front lines!
Intro
Hello All, Excited to be a part of community. I work as a Global Data Strategy Lead for the Fintech organisation. I’m here to learn from the community, exchange ideas, and contribute where I can—especially on topics around data strategy, governance, and building resilient data capabilities.
0 likes • 20h
Hi and welcome! We really value having senior practitioners here, especially with the complexity of Fintech data strategy. Please do share your experiences, the community will definitely benefit from your perspective on building resilient capabilities. See you in the threads!
Practical Lessons on AI Governance in Production Systems
One thing I’m seeing repeatedly with AI governance: Most governance frameworks fail because they live outside where decisions actually happen. Top learnings from recent work: - AI risk is rarely a model issue — it’s a context + data + ownership issue - Policies defined upfront don’t survive runtime without enforcement hooks - “Human in the loop” breaks down without clear decision rights and escalation paths - Agents amplify governance gaps faster than dashboards ever did Key challenge ahead: Governance must move from review-time controls to runtime guardrails — embedded in data access, memory, orchestration, and action execution. Curious how others here are handling governance inside live AI workflows, not just around them.
0 likes • 7d
100% agreed @Rakesh Khanduja. The biggest friction point I see is indeed that policies 'die' once they leave the PDF document. Unless enforcement hooks are baked into the API or the data access layer, governance remains a theoretical exercise. Regarding the 'context' issue: do you find that your teams are struggling more with defining the technical guardrails or the business definition of 'acceptable risk' before deployment?
Shape a career around Data Governance
Hello everyone! I’d like to ask a question as I’m looking to formally shape my professional path around Data Governance. Over the years, I’ve been doing governance-related work in practice, often within small teams or departments inside larger organizations, even if it wasn’t always labeled as such. At this stage, I’d like to better understand how Data Governance roles are defined in the industry and how to position my experience accordingly. Does anyone have useful resources or advice they could share? Thanks!! 😁
2 likes • 7d
Hi @Montse Puiggròs Maldonado , and welcome! You are in a very common situation. In my experience, the best Data Governance professionals often start by doing "stealth governance" inside other roles before they ever get the official title. Regarding industry definitions, be careful not to get too hung up on job titles as they vary wildly from company to company. Instead, focus on the capabilities you were delivering (e.g., Data Quality, Metadata Management, Stewardship). I am actually currently finalizing a training module specifically on "How to land your first official Data Governance role" because bridging the gap between past experience and future positioning is the #1 hurdle I see. While I finish that resource, I’d suggest you list out the 3 main data problems you solved in your previous roles. We can help you translate those into "Governance terminology" right here.
🏗️ "Build vs. Buy" is the Wrong Question. Ask This Instead.
From this artice. The Gist We often debate AI adoption in terms of cost or speed (SaaS vs. Custom). But this CTO perspective flips the script: the real currency isn't money, it's Control. - Buying (SaaS/Wrapper): You gain speed, but do you lose data sovereignty? - Building (In-house/Open Source): You gain control, but is your data governance mature enough to feed the beast? The Verdict: Governance isn't just a safety net; it’s the deciding factor. If you can't guarantee data quality and security, both paths lead to failure. Let’s Discuss: 1. The "Control" Tax: Are you willing to pay a premium (building in-house) just to keep full governance over your data lineage? 2. Vendor Trust: When "buying" AI, does your governance team actually vet the vendor's data handling, or is it just a procurement checkbox?
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Anas Harnouch
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@anas-harnouch-2229
Data Strategy & Governance @ PwC From Data Strategy to Execution Governance, Architecture & Data Products for Analytics & AI

Active 10h ago
Joined Oct 10, 2025
INTJ