Ravi Kiran Rali

Researching and advising on the data, governance, and operating-model changes required for enterprise AI agents.

Independent advisory and applied research on AI-native data architecture. The work covers MDM, data engineering, and operating models designed to support AI agents alongside traditional analytics and reporting, published as long-form strategy notes.

• 5notesPublished strategy notes on AI strategy and measurement, the AI investment ladder, AI-native MDM, AI-assisted data engineering, and the hybrid FSO-FSP operating model for CROs.
• Developed the AI-native MDM thesis, replacing rules-based batch hubs with semantic matching, learned survivorship, and event-driven settlement, with an 18-month strangler-fig migration path and a year-2 financial crossover model.
• Mapped the agentic-era data engineering platform: an agent control plane and a context and semantic plane sitting over lineage, observability, evals, policy, and unit cost, with the trust plane sequenced first.
• Advising leaders on where AI investment pays back, and when organizations should improve their data foundation before investing further in AI.

Designed an enterprise AI platform and governance framework for a leading global CRO operating in more than 100 countries.

Parexel is a $2B+ clinical research organization. I owned enterprise data strategy, AI architecture, and governance across clinical and commercial operations, covering scoping, pricing, scheduling, project management, finance, forecasting, clinical data management, site and investigator selection, and FSO/FSP resource management.

• ~60%Reduced analyst research time by architecting and launching an enterprise AI platform using LLM and knowledge-graph technology for intelligent search, protocol summarization, and site and investigator insight across the global study portfolio, which accelerated study start-up decisions.
• Designed a knowledge-graph and LLM pipeline joining structured clinical metadata to unstructured protocol documents through a RAG architecture, allowing operational teams to query trial intelligence in natural language with governed, auditable data access.
• ~40%Reduced downstream reconciliation effort by architecting the data governance, MDM, and data quality roadmap for global clinical operations, enabling audit-ready lineage across multi-study portfolios.
• 8+domainsEstablished an Architecture Review Board governing data platform investment across eight or more business domains, reducing rework and maintaining regulatory alignment.
• Accountable for delivery quality, budget oversight, and risk mitigation across the enterprise data and AI architecture portfolio.

Technical leadership for a data products startup and governance blueprints for its clients.

Provided solution architecture guidance and consulting services to a data products company and its enterprise clients across AWS and Azure.

• Designed data lakehouse and operational data store architectures on AWS and Azure, consolidating siloed sources into unified analytics-ready platforms and reducing client time-to-insight from weeks to days.
• Delivered data governance blueprints covering classification frameworks, metadata management, and data catalog configurations, giving clients a structured path to regulatory-ready data assets.
• Advised on data integration pipelines, MDM, data classification, and cataloging as part of end-to-end governance implementation.

Five years delivering data management programs for Fortune 500 clients, covering architecture, delivery, commercial ownership, and team leadership.

Senior advisory leader in KPMG's national data practice, leading teams of 10 to 15. My responsibilities included technical sales, delivery management, architecture, and commercial oversight.

• 3 wks → 2 daysReduced business-user time-to-insight and cut data error rates by 35% by delivering an enterprise Modern Data Platform on Snowflake with Informatica IDMC (MDM, Data Quality, AXON catalog), giving clients trusted master data and self-service analytics.
• ~30%Reduced compliance remediation cost by architecting an enterprise data governance program covering a policy engine, stewardship operating model, data quality KPIs, and metadata taxonomy, which helped clients pass regulatory audits.
• 3environmentsDesigned and delivered MLOps solutions for a large automobile manufacturer, building GenAI and RAG-ready data architecture with governed vector store integration and LLM-ready pipelines, adopted in three client production environments.
• Developed an enterprise policy engine integrating organizational policy, technical metadata, data quality, and data privacy requirements into enforceable controls.
• Built a TOGAF-aligned enterprise data architecture practice covering architecture views, roadmaps, review methods, and implementation standards, using Spark, Erwin, and PowerDesigner for architectural modeling.

Anti-money-laundering transaction hub and analytics architecture for a global bank.

Owned the data integration solution roadmap and architecture for the AML Transaction Hub, analytics, self-service reporting, search analytics, and predictive analytics, supporting AML monitoring where false positives increase review costs and false negatives create significant regulatory risk.

• 15+sourcesDelivered an AML Transaction Hub integrating more than fifteen data sources, enabling real-time suspicious activity detection, improving SAR filing accuracy by 30%, and meeting OCC and FinCEN regulatory obligations on schedule.
• ~25%Lowered false positive rates in transaction monitoring through automated data cleansing pipelines and an enterprise data governance framework for the AML domain, which reduced analyst review cost.
• Established data stewardship roles, policies, and procedures. Delivered conceptual and logical data models and ran vendor and tooling evaluation for AML and risk data solutions.
• Led big data architecture and the delivery teams implementing risk, compliance, and analytics projects.

An early implementation of metadata-driven data-hub architecture.

Architected a software product that let organizations use metadata from across their systems to assemble enterprise data hubs built on enterprise models in a NoSQL database.

• Designed a metadata-driven architecture where hub content could be consumed across enterprise patterns including data integration, BI and analytics, and microservices, without rebuilding per consumer.
• Proved out enterprise-model-on-NoSQL patterns for accelerating metadata reuse and data hub assembly.
• This work anticipated today's use of metadata as an operational foundation for enterprise AI.

The canonical data model behind a health insurer's business transformation, with an early application of graph technology to MDM.

Created the vision and led implementation of the enterprise data modeling and integration hub capability at Blue Cross Blue Shield of North Carolina, supporting an enterprise-wide business transformation program.

• 20+touchpointsArchitected the Enterprise Canonical Data Model powering the transformation, supporting consistent data exchange across more than twenty integration touchpoints and reducing integration build time by 40% through reusable canonical patterns.
• ~35%Reduced duplicate master records by applying the Neo4j graph database to MDM entity resolution, which established the foundation for a scalable enterprise MDM program.
• Built canonicals usable across REST services, ETL, and messaging integration patterns rather than a separate model per channel.
• Ran proofs-of-concept on Hadoop and graph databases at an early stage of their enterprise adoption.

Data strategy and architecture for liquidity risk management during the post-2008 regulatory rebuild.

Defined the vision and roadmap for the data strategy and solutions supporting Liquidity Risk Management systems.

• Delivered an architecture and governance roadmap for liquidity risk data, supporting post-2008 regulatory capital and liquidity reporting with auditable, fit-for-purpose data architecture.
• Evaluated tools and technologies against regulatory reporting constraints.

Six years combining architecture ownership with delivery accountability across multiple client accounts.

Consulting across Duke Energy (IBM), The Hanover Insurance Group, Teradyne, and ADP, covering data architecture, solution architecture, delivery management, and program management.

• Delivered enterprise data warehousing, BI and analytics, metadata management, MDM, data cleansing, data quality, and data governance solutions across financial services, insurance, and energy.
• Held architecture responsibility and delivery-management accountability at the same time across multi-year engagements.
• Developed experience in client scoping, estimation, staffing, and delivery accountability.

This role marked my transition from project delivery into regional practice leadership.

Started in delivery and moved into regional practice leadership for the Business Intelligence and Data Warehousing practice.

• Ran client relationship management, account management, pre-sales and sales, resource management, recruiting, training, pipeline planning, and budgeting for the regional BI and data warehousing practice.
• Built packaged domain solution offerings for financial services, insurance, life sciences, and government verticals.
• Owned mentoring and performance reviews for practice staff while continuing to contribute to architecture and client delivery.