16+ years · ex-FICO · ex-EY · DAMA CDMP Master · one of ~430 worldwide · Verify credential ↗

Ram Balasubrahmanian

Q: How does Ram’s work map to PDPL, NDMO and SAMA in the Gulf?

PDPL is modelled on GDPR and NDMO maps to DAMA-DMBOK, frameworks Ram has worked with directly — GDPR-aligned controls delivered vendor-side for global financial-services clients, and DAMA-DMBOK applied across enterprise platforms (CDMP Governance 83%, Data Quality 88%). He brings the proven parent control plane — 232M+ records protected with audit-ready evidence —; his PDPL and NDMO work to date is advisory analysis and working demos, not production delivery.

Q: How does Ram’s work map to GDPR, the EU AI Act and DORA?

He has delivered GDPR-aligned controls from the vendor side for global financial-services clients (classification, masking, lineage, audit evidence) and published GDPR/EU-AI-Act advisory analysis, and he has built the AI controls the EU AI Act now mandates (data governance, logging, human-oversight gates, model controls), demonstrable as three live systems.

Q: What measurable outcomes has Ram delivered?

At FICO: 95% incident reduction in 3 months, 232M+ records protected per extraction cycle, $2M+ annual failure costs eliminated, $10M+ platform ARR supported, and onboarding from 4 weeks to 2–3 days through metadata-driven quality controls across 50+ enterprise clients.

Q: Does Ram build RAG and AI systems himself?

Yes. Ram built Golden Record RAG Pipeline and MediGovern RAG — two working retrieval-augmented generation systems, each with a live app, documented API, and open-source code — his own independent builds, not client production systems. He also built Pipeline Pulse, a DMBOK-aligned data governance platform demo. All three are at ram-bala.com/demos/.

Q: Where can I see working proof beyond the resume?

Three live systems are available at ram-bala.com/demos/ — Golden Record RAG Pipeline, MediGovern RAG, and Pipeline Pulse. Each has a live app and an animated architecture walkthrough; the two RAG pipelines (Golden Record and MediGovern) are open-source with production APIs.

Trusted data for regulated scale.

When regulators ask questions, boards need answers — I build the governance, controls, and audit evidence that make data defensible. 95% fewer incidents, 232M+ records protected, $10M+ ARR supported across 50+ regulated financial-services clients at FICO.

95%Incident reduction

232M+Records protectedper extraction cycle

$2M+Annual savings

50+Enterprise clients

See the working systems →Case studies Executive summary (PDF)

RAG & AI BuilderAnthropic · Claude · MCPNewsletter · 3,000+ subscribersex-FICO · EYDAMA CDMP MasterBengaluru · India · GCC · Europe · APAC

ex-FICO Senior Managerex-EYDAMA CDMP Master3,000+ Newsletter SubscribersRAG demos, open-source50+ Enterprise Clients

95%

Incident reduction

within 3 months

232M+

Records protected

per extraction cycle

$2M+

Annual cost saved

failure costs eliminated

50+

Enterprise clients

onboarded and governed

$10M+ ARR

Platform ARR supported

enterprise client revenue

Executive Brief

For leaders who need data they can defend in the boardroom.

“If you can’t trace it, you can’t trust it. If you can’t measure it, you can’t defend it.”

I convert data risk into audited business capability — combining governance policy, engineered controls, and evidence that holds up under regulator, auditor, and CFO scrutiny.

Executive summary (PDF) →

Endorsement excerpt
"A rare combination of strong technical skills, deep domain knowledge, and an exceptional ability to influence others. … He is metric driven and extraordinarily accountable."
Ken Bouley · Direct Manager, FICO · Executive Leadership

Risk95% incident reduction

Fewer production failures, faster RCA, audit-ready evidence for every escalation.

ValueOnboarding: 4 weeks → 2–3 days

Metadata-driven quality at scale — AI and analytics on trusted, governed data.

Cost$2M+ annual savings

Recurring failure costs eliminated through prevention controls, not firefighting.

FICOEYDAMA InternationalHCL Technologies

Operating Model

How production incidents become governed systems.

This is the discipline behind the 95% incident reduction — not a framework slide, but how I run data operations in production.

See proof →

Detect

Metadata checks, SLA signals, reconciliation, and monitoring surface anomalies before they become escalations.

Resolve

ServiceNow routing, RCA evidence, and clear ownership paths close incidents with audit-ready context.

Prevent

Shift-left gates, reusable DQ rules, and defect review boards stop recurring failures upstream.

Govern

Lineage, stewardship, privacy controls, and regulatory evidence attach to every operating rhythm.

Scale

Reusable playbooks accelerate client onboarding and quality without custom rework each time.

▶ The Data City · the framework in 90 seconds

About

About Ram Balasubrahmanian.

I help regulated enterprises turn fragmented data operations into trusted operating systems: governed, observable, audit-ready, and built for AI adoption. At FICO, and earlier at EY, I have worked where engineering reliability, governance control, regulatory evidence, and business adoption meet.

At FICO I scaled data operations for 50+ enterprise clients, reduced production incidents by 95% in three months, protected 232M+ regulated records per extraction cycle, eliminated $2M+ in recurring annual failure costs, supported $10M+ in platform ARR, and compressed onboarding from 4 weeks to 2–3 days with metadata-driven quality controls — managing a 25+ member team with 11 direct reports across the US, Canada and India, and owning governance for multiple data services: data governance, data quality and data management fundamentals.

Since April 2025 I’ve run an independent data & AI governance practice — a Data Steward engagement via Toptal, governed-RAG demos I built myself, and a LinkedIn newsletter to 3,000+ subscribers . I build governed AI, not just advise on it. See independent-practice deliverables.

I lead data governance, data quality, DataOps, metadata and AI-control programmes for BFSI and other data-intensive organisations. Bengaluru-based, working with teams across India, the GCC, Europe and APAC.

GCC & PDPL governance perspective · EU GDPR & AI-Act perspective · Full EDP knowledge hub

Practice areas

What I work on

Core — data governance & quality

Governance operating models — Detect → Resolve → Prevent → Govern → Scale
Metadata-driven data quality and reusable control frameworks
DataOps and reliability — SLAs, root-cause evidence, audit-ready operations
MDM, golden record and metadata management

Edge — AI governance & advisory

AI governance operating models and responsible-AI controls
Governed retrieval-augmented generation and model-risk evidence
Regulatory crosswalks — GDPR (vendor-side delivery); PDPL, NDMO and the EU AI Act (advisory)

Case Studies

Five proof points. One operating discipline.

95%fewer incidents

$2M+annual savings

232M+records protected

20–100 hrs→<4 hrsworst-case resolution

50+clients governed

Reliability, built into operations.

Data operations across 50+ enterprise clients needed fewer escalations, faster ownership, and clearer root-cause evidence for every production incident.

Detect → Resolve → Prevent → Govern → Scale — metadata validation, ServiceNow routing, RCA evidence, shift-left gates, Defect Review Boards.

95%fewer incidents

<4hworst-case resolution

$2M+saved annually

ServiceNow · AWS · Python · Boto3 · RCA evidence

Executive takeawayReliability stopped being firefighting and became a governed, audit-ready operating rhythm — the same discipline scales to any regulated platform.

Quality that scales without custom rework.

Client onboarding depended on custom validation scripts, slowing delivery and making quality controls harder to reuse.

4 wks → 2–3dclient onboarding

45%fewer escalations

AWS Athena · Python · Boto3 · Apache Hop · Metadata config

Executive takeawayFaster onboarding came from reusable, metadata-driven controls — not more headcount. Quality that compounds instead of resetting with every client.

Privacy controls at regulated scale.

As AI/ML decisioning expanded, privacy risk and audit exposure increased across ingestion, training sets, and consumption.

232M+records protected

10KTPS throughput

AWS KMS · IAM · Python · Lineage controls

Executive takeawayPrivacy became an engineered control with evidence attached — not a policy promise. Defensible the moment a regulator asks.

AI governance from model to evidence.

AI/ML across credit decisioning and fraud detection required stronger explainability, model-risk controls, and regulatory transparency.

Model-lifecycle governance with explainability controls, drift monitoring, challenger/champion evidence, human review gates, and regulatory documentation.

Model governance · Explainability · Python · AWS · ServiceNow

Executive takeawayAI stayed auditable from model to decision — explainability, review gates, and evidence as defaults, not afterthoughts.

50+clients governed

Live Proof

Three working demos. Governed AI and data platforms, built independently.

Most leaders talk about AI governance. These are three systems I designed and shipped — open, click, and inspect any of them right now.

Flagship demo

Golden Record RAG Pipeline

Entity resolution, survivorship, lineage and vector RAG for fintech compliance — live on 89,198 records. Open the live app, an animated architecture walkthrough, and a guided playbook, all in-page.

89,198source records20,502golden records35,000RAG chunks

Open demo hub →

Featured · RAG

MediGovern RAG

Governed retrieval-augmented generation — live dashboard, documented production API, and open-source pipeline. Built with Claude & Anthropic tooling.

Proves: I design and ship governed AI — not just advise on it. A working RAG system with a live dashboard, a documented API, and public source code.
Why it matters: Regulated enterprises don't need another unbounded chatbot. They need AI whose answers stay grounded in approved sources and can be defended to an auditor.

Built around

Retrieval-augmentedGrounded answersDocumented APIOpen-source pipeline

Operating modelRetrieve → ground → generate → cite → audit-log

Open demo hub →Live Dashboard GitHub

Enterprise Data Platform

Pipeline Pulse

DMBOK-aligned platform demo — metadata-driven DQ, governance controls, lineage, and AI governance in working software.

Proves: DMBOK as working software — governance, data quality, lineage, and AI controls you can click through, not slideware.
Why it matters: It turns abstract DAMA knowledge areas into an operating picture a CDO, auditor, or steward can actually use.

Governance screens

Metadata-driven DQLineage & stewardshipAudit evidence hubRBAC / ABACAI governance

Control planeArchitecture → quality → governance → evidence → AI control

Open demo hub →Open Demo EDP Hub

Go Deeper

Full guides, runbooks, and perspectives.

The homepage is your executive summary. Explore the full knowledge hub, learning tracks, templates, and articles on dedicated pages.

Knowledge HubEnterprise Data Platform Guide6 learning tracks · runbook · templates · DMBOK screens · field toolsOpen full EDP hub →PerspectivesData & AI Leadership ArticlesDataOps · AI governance · GCC & PDPL · incident reductionRead perspectives →Newsletter · 3,000+The Governed Data & AI BriefWeekly LinkedIn newsletter for data and AI leadersSubscribe →

Capabilities

Four pillars. Sixteen years in production.

Governance & DAMA

Data governance, stewardship, DMBOK, metadata, MDM, business glossary, and audit evidence across regulated BFSI.

AI, RAG & Responsible ML

AI governance, RAG pipelines, model risk, explainability, Claude/Anthropic, and governed AI in production.

DataOps & Reliability

Detect → Resolve → Prevent → Govern → Scale, metadata-driven DQ, incident reduction, ServiceNow, and shift-left quality at scale.

Privacy, GDPR & AI-Act

GDPR-aligned controls delivered vendor-side for global financial-services clients — plus EU-AI-Act-aligned AI controls (data governance, logging, human oversight, model controls) demonstrated in working demos and published advisory analysis. SOX, DPDP and ISO 27001 / SOC 2 control discipline from vendor-side delivery — the foundation PDPL and NDMO build on. PDPL, NDMO and DORA: working knowledge and advisory analysis, not production delivery.

Full domain breakdown in the EDP guide

Testimonials

What leaders who worked with me say.

All 11 on LinkedIn →

11 LinkedIn recommendations from direct managers and senior peers across FICO, EY, HCL and earlier roles.

15SPOT Awards at FICOOutstanding performance recognition

11LinkedIn recommendationsFrom direct managers and senior peers

Experience

Sixteen years. Larger scope. The same operating discipline.

Independent Consultant — Data & AI Governance · Apr 2025 – Present

Independent practice — what I have built since 2025.

Since April 2025 I’ve run an independent data & AI governance practice: a Data Steward engagement via Toptal, The Governed Data & AI Brief (3,000+ subscribers), three working systems built end-to-end (Golden Record RAG, MediGovern RAG, Pipeline Pulse), the public Enterprise Data Platform hub, and the DAMA CDMP Master credential (certified September 2026).

Independent practice · since 2025

3working demos, built independently

3,000+newsletter subscribers

CDMPMaster certified 2026

6EDP learning tracks published

Built & shippedMediGovern RAG

Full retrieval-augmented generation pipeline — live dashboard, production API on Railway, open-source on GitHub. Governed AI in working software.

Live dashboard →

ConsultingToptal — Data Steward

Enterprise Data Steward engagement via Toptal — metadata standards, DQ rules, and stewardship workflows for a global client.

Built & shippedPipeline Pulse

DMBOK-aligned enterprise data platform demo — metadata-driven DQ, governance controls, lineage, and AI governance screens.

Open demo →Published weeklyThe Governed Data & AI Brief

LinkedIn newsletter for data and AI leaders — governance, DataOps, responsible AI, and production lessons. 3,000+ subscribers and growing.

Subscribe →PublishedEnterprise Data Platform Hub

Public runbook, interactive guide, 6 learning tracks, downloadable templates, and DMBOK teaching companion for practitioners.

Open EDP hub →Credentials advancedDAMA CDMP Master · Anthropic · Scrum

CDMP Master certified September 2026. Anthropic Claude & MCP — 6 Certificates of Completion. Scrum Master 2025, Data-Driven Decision Making 2025.

View credentials →Thought leadershipPublic perspectives & case studies

Articles on 95% incident reduction, AI governance operating models, and GCC/PDPL data governance — grounded in FICO production experience.

Read perspectives →

Independent Consultant — Data & AI Governance

Consulting, Applied AI Governance & Thought Leadership

Apr 2025 – Present Independent practice

Consulting: Enterprise Data Steward engagement via Toptal — metadata standards, DQ rules, and stewardship workflows for a global client.
Applied AI (independent builds): Designed, built, and deployed MediGovern RAG (live dashboard, production API, open-source repo) and Pipeline Pulse EDP platform demo.
Thought leadership: Launched The Governed Data & AI Brief(3,000+); published EDP knowledge hub, runbook, templates, and leadership perspectives.
Credentials: DAMA CDMP Master certified 2026 (Credential #21979773); Anthropic Claude & MCP — 6 Certificates of Completion; Scrum Master 2025; Data-Driven Decision Making 2025.

FICO

Senior Individual Contributor (Sr. Engineer)

Oct 2024 – Mar 2025 Lateral move, at my request

Moved to the senior individual-contributor track at my own request, at the same grade, to focus on delivery.
Delivered six major DPL/ADE releases on schedule and shipped COGS-reduction capabilities.

FICO

Senior Manager — Data Management, Enterprise Data Platform

Jun 2022 – Sep 2024 2 yrs 3 mos

Scaled Detect → Resolve → Prevent → Govern → Scale governance for 50+ enterprise clients, reducing production incidents 95% within three months and cutting worst-case resolution from 20–100 hours to under 4.
Managed a 25+ member team with 11 direct reports across the US, Canada and India — hiring, performance management, and the governance tooling roadmap.
Owned governance for multiple data services across the platform — data governance, data quality and data management fundamentals (metadata, standards, stewardship).
Established PII safeguards and audit-trail governance protecting 232M+ regulated records per cycle; supported $10M+ ARR and delivered $2M+ annual savings.

FICO

Manager · Enterprise Data Platform, Data Operations & Governance

Dec 2018 – Jun 2022 3.5 years

Automated metadata-driven data quality checks using AWS Athena, Python, and Boto3 — reducing incidents by 50% in three months and onboarding clients in 2–3 days (from 4 weeks) with zero custom rework per client.
Governed 75GB+ batch workloads and 2.5M–3M API calls per cycle with SLA-driven processing and reconciliation controls across 50+ enterprise clients.
Embedded ingestion controls including control files, checksums, header/footer validation, and duplicate rejection — reducing downstream escalations by 45%.

FICO

Lead / Associate Manager · Data Management Platform

Oct 2016 – Dec 2018 2 years

Built enterprise ingestion, validation, transformation, and reconciliation pipelines for decisioning and analytics platforms serving global financial clients.
Delivered COBOL-to-JSON transformation for 3,500-column datasets, 25–35GB files, and 5M records within a 2-hour SLA.
Led technical reviews, RFP inputs, and client demonstrations that supported new enterprise client wins.

HCL Technologies

Technical Lead · Data Migration, Reconciliation & Guidewire Integration

Apr 2015 – Oct 2016 1.5 years

Spearheaded Guidewire Insurance Suite migrations for global teams using reusable ETL, validation, and reconciliation frameworks.
Built validation, cleansing, and SQL optimisation workflows for BFSI and publishing clients.

EY · Cognizant · RR Donnelley

Associate Tech Lead / Programmer Analyst / Software Engineer

Jul 2009 – Mar 2015 6 years

Delivered data migration, reporting, SQL automation, and enterprise application support across consulting and delivery roles.
Progressed from software engineering into data management, reconciliation, and platform delivery leadership — the foundation for everything that followed at FICO.

Perspectives

Perspective shaped by production work.

Case Study · DataOps

Reducing enterprise data incidents by 95% in 3 months

The answer was not more tooling. It was a disciplined operating model — Detect → Resolve → Prevent → Govern → Scale — where every failure moved from symptom to ownership, evidence, root cause, prevention, and governed scale.

Read article

Executive Perspective · AI Governance

AI governance is an operating model, not a document

Model cards matter, but they are not enough. BFSI teams need explainability, privacy checks, drift monitoring, human review, and evidence that stays current after deployment.

Read article

Regional Insight · GCC

What GCC data programs need now

Saudi Arabia's PDPL and UAE governance expectations are turning data control into an operating requirement. Policies, stewardship, lineage, and evidence need to work in production, not only on paper.

Read article

All perspectives

Official Verified Credentials

DAMA CDMP Master Credential Wall

DAMA International Certified Data Management Professional — three domain exams passed; Master level certified September 2026 (Practitioner level earlier).

CDMP Certification Pathway

✓

Associate

Foundation exam

✓

Practitioner

Foundation + 2 Specialist

★

Master

Foundation + 2 Specialist + All 3 exams >80% & Approval by DAMA

← You are here

Data Management Fundamentals

Foundation Exam

88%

✓ Verify Credential

Data Governance

Specialist Exam

83%

✓ Verify Credential

Data Quality

Specialist Exam

88%

✓ Verify Credential

CDMP Master

Master Level

#21979773

✓ Verify Credential

88%

Data Management Fundamentals

Covers the DAMA-DMBOK knowledge areas. Foundation of the CDMP certification pathway.

Foundation Exam · #19686465

83%

Data Governance

Specialist-level: governance frameworks, stewardship, policy hierarchies, accountability and decision rights for regulated enterprises.

Specialist Exam · #19940610

88%

Data Quality

Specialist-level: DQ frameworks, measurement methodologies, enterprise quality governance, and improvement programs.

Specialist Exam · #20023492

AI Implementation · AI-Assisted Development · Governed RAG

Building governed AI, hands-on.

I don't just advise on AI governance — I build. Completed Anthropic’s Claude & MCP certificate courses (6 Certificates of Completion), hands-on with RAG pipelines, vibe coding, and AI-assisted development. From MediGovern RAG to Pipeline Pulse, I ship working AI systems with governance built in.

Anthropic — 6 Certificates of CompletionMCP · Model Context ProtocolRAG PipelinesAI-Assisted DevelopmentResponsible AIAPI Deployment

Additional certifications

◆Anthropic AI

Claude & MCP — 6 Certificates of Completion

Scrum Master

2025

SAFe

Agile at Scale

Guidewire

Data Hub & InfoCenter

Data-Driven Decision Making

2025

Executive summary

One-page executive summary

Download PDF →

Featured · RAG

MediGovern RAG dashboard

Open live demo →

EDP Demo

Pipeline Pulse platform

Open demo →

Newsletter

3,000+ subscribers

Subscribe →

11 recommendations

Open profile →

GitHub

MediGovern RAG repo

View code →

FAQ

Common questions.

Who is Ram Balasubrahmanian?

Ram Balasubrahmanian is a Senior Data Governance and AI Governance leader with 16+ years across FICO, EY, and regulated BFSI platforms. He is a DAMA CDMP Master who builds governed, auditable data systems for enterprise scale and AI adoption. Read full background.

How does Ram’s work map to PDPL, NDMO and SAMA in the Gulf?

PDPL is modelled on GDPR and NDMO maps to DAMA-DMBOK, frameworks Ram has worked with directly — GDPR-aligned controls delivered vendor-side for global financial-services clients, and DAMA-DMBOK applied across enterprise platforms (CDMP Governance 83%, Data Quality 88%). He brings the proven parent control plane — 232M+ records protected with audit-ready evidence —; his PDPL and NDMO work to date is advisory analysis and working demos, not production delivery. Read the GCC & PDPL crosswalk.

How does Ram’s work map to GDPR, the EU AI Act and DORA?

He has delivered GDPR-aligned controls from the vendor side for global financial-services clients (classification, masking, lineage, audit evidence), plus published GDPR/EU-AI-Act advisory analysis and working demos. He has also built the kind of AI controls the EU AI Act now mandates — data governance, logging, human-oversight gates and model controls — which you can open as three live demos. Read the EU GDPR & AI-Act crosswalk.

What is Ram working on now?

Since April 2025 he has run an independent data & AI governance practice — a Data Steward engagement via Toptal, and he shipped MediGovern RAG and Pipeline Pulse, grew The Governed Data & AI Brief to 3,000+ subscribers, published the EDP knowledge hub, and achieved the DAMA CDMP Master credential (2026). See independent-practice deliverables.

What measurable outcomes has Ram delivered?

At FICO: 95% incident reduction in 3 months, 232M+ records protected per extraction cycle, $2M+ annual failure costs eliminated, and onboarding from 4 weeks to 2–3 days through metadata-driven quality controls across 50+ enterprise clients. Driven by the Detect → Resolve → Prevent → Govern → Scale operating model. Read the case study.

What is Ram's approach to AI governance?

AI governance as an operating model — not a document. Model lifecycle controls, explainability evidence, privacy checks, drift monitoring, human review gates, and audit trails embedded in production rhythm. Read the AI governance perspective.

Does Ram build RAG and AI systems himself?

Yes. Ram built MediGovern RAG — a full retrieval-augmented generation pipeline with production API and open-source code. He holds 6 Anthropic Claude & MCP Certificates of Completion and is passionate about governed AI in production. See demos.

Where can I see working proof beyond the resume?

Ram built MediGovern RAG, Pipeline Pulse, and publishes The Governed Data & AI Brief(3,000+). Explore full guides · Live demos.

“Governance sets the rules, quality keeps the promise, lineage proves it, metadata explains it, security protects it — trusted data is all of them working as one operating rhythm.”

Contact

Let’s talk about the data outcomes you need next.

In 30 minutes, we can map your governance, AI, or DataOps challenge to the operating models I have built across FICO, EY, and regulated BFSI platforms.

What happens in 30 minutes

You outline the governance, AI, or platform outcome you need
I map it to directly relevant operating experience
We identify where governed data and AI would change your outcomes
You leave with a clear point of view and practical next steps

Book a 30-minute conversation

Pick a time above — or email ram@ram-bala.com

Emailram@ram-bala.com LinkedInView profile Phone+91 9972657810 WhatsAppMessage on WhatsApp