Data Governance · Business Intelligence · AI Governance
Aniket Dwivedi
Data Governance & BI Leader · Solutions Architect
Fifteen years turning messy enterprise data into something people — and now AI agents — can actually trust and act on. I work at the join between governance, analytics and architecture, because that's where the value hides.
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About
I started as a Tableau developer in 2012 and spent the next decade learning why good dashboards fail.
They fail because the data underneath them was never owned by anyone. That realisation pulled me from visualisation into governance, and eventually into architecture — you cannot fix trust at the presentation layer.
Since then I've built governed data foundations in asset management, investment banking, insurance, healthcare, retail and global manufacturing. I've migrated legacy estates, administered Tableau Server at 500+ workbook scale, and written the governance frameworks that decide who owns what.
What I care about now is the agentic shift. Every organisation wants AI agents acting on its data; very few have data an agent should be trusted with. The unglamorous work — ownership, stewardship, quality rules, lineage — is suddenly the thing that decides whether AI helps or quietly multiplies your existing errors.
I hold DAMA's CDMP and TOGAF, and I've stayed hands-on throughout: Python in Tableau, Snowflake modelling, Agentforce pilots. I think leaders in this field lose their judgement the moment they stop building.
Outside the enterprise work I write about governance and the modern data stack, and I speak at governance forums. Based in Pune, working with teams across India, Europe and the US.
What I do
Four disciplines, one problem.
Governance, analytics, AI and architecture are usually run as separate programmes. The value shows up exactly where they meet.
Data governance that operates
Federated ownership, stewardship and data products with named owners and SLAs. Councils that decide, working groups that execute — built on five pillars: people, data, data stores, processes, governance. Glossary, lineage and stewardship wired through Collibra rather than living in a slide deck.
Business intelligence at scale
Certified Tableau dashboards, standardised KPIs, row-level security and tuned extracts — governed self-service instead of conflicting numbers. Cloud and Server administration end to end, plus the migration work of getting a legacy Cognos or Excel estate onto something maintainable.
Agentic AI, governed
Agentforce agents built in Agent Studio with Prompt Builder — topics, actions and guardrails — grounded through Data 360 over Snowflake, so leadership can ask natural-language questions about quality, lineage and governance gaps and get auditable answers. With responsible-AI controls in from the start.
Enterprise data architecture
Target-state BI and data architecture across Azure, Microsoft Fabric and Snowflake, translating business and regulatory requirements into decision-ready reporting. TOGAF-grounded, dimensional modelling where it belongs, and pipelines in Python, Alteryx and Tableau Prep.
Experience
Fifteen years, nine roles.
From Tableau developer to Chief Data Office — the through-line has always been making data trustworthy enough to decide on.
Assistant Vice President, Chief Data Office
Defining target-state BI and data architecture, turning business and regulatory requirements into governed, decision-ready reporting. Solutions Architect working deep in the Salesforce ecosystem.
- Built the “House of Data” governance-reporting layer — one consistent view of KPIs, ownership and delivery across the CDO
- Delivered certified Tableau dashboards and standardised KPIs for senior risk, controls and investment stakeholders
- Piloting Agentforce agents grounded via Data 360 over Snowflake for auditable, natural-language governance answers
- Architected governed foundations across Azure, Microsoft Fabric and Snowflake, integrated with Collibra
- Delivered Python-in-Tableau analytics — TabPy scoring, k-means clustering, Hyper API extracts
Independent BI & Data Governance Consultant
Governed Tableau architecture, migration and reporting for asset management, banking, healthcare and product-engineering clients — each engagement run end to end.
- Advised on Tableau Server-to-Cloud migration, Tableau Pulse and agentic-AI adoption for large estates
- Salesforce (via Aditi Consulting): tested and validated next-generation Tableau — Cloud, Pulse, Agentforce
- Healthcare (via Escalent): governed financial dashboards across revenue, cost and margin
Associate Vice President
Tableau Design Architect delivering credit-risk analytics for Consumer & Corporate Banking in a highly regulated environment.
- Architected analytics for credit-card exposure, delinquency and portfolio risk
- Built governed Snowflake foundations and Python/Alteryx ETL across Oracle and Big Data backends
- Strengthened reporting controls and data readiness for analytics and AI/ML use cases
Senior Process Manager
Led delivery for retail and software reporting across multiple concurrent workstreams.
- Campaign analytics across 50+ airport locations for a U.S. airline, Salesforce-embedded with role-based access
- Migrated 30+ dashboards from Cognos to Tableau with KPI mapping, validation and sign-off
- Embedded data-quality checks that reduced production issues
Senior Data Specialist — BI Program Lead & Architect
Owned the full BI lifecycle for global supply chain and procurement in Emerson's Core Data Analytics CoE.
- Delivered 150+ Tableau dashboards globally as Data-as-a-Service with reusable governed sources
- Administered global Tableau Server — 500+ workbooks, 80+ extracts, migrations and tuning
- Governance, MDM and anomaly monitoring that helped identify $20M in supply-chain savings
- Led and mentored the BI developer team; set the CoE's reporting and governance standards
Senior Research Analyst
Predictive analytics for a major U.S. insurer.
- Python predictive-risk models forecasting loss patterns from claim, location and climate data
- Insurance KPIs across lines of business on Oracle/DB2 with geo-coded dashboards
- GDPR-compliant predictive platform; A/B testing that improved customer loyalty by 14%
Data Visualisation / BI Developer
Tableau trainer and developer building dashboard capability for customer-experience programmes.
- Delivered Tableau training and enablement, raising design standards across analyst and developer teams
- Customer-satisfaction and sentiment analytics on Teradata for service-recovery programmes
Tableau Developer / Business Analyst
Retail and pharma analytics for Walmart and Office Depot.
- Migrated 25+ Cognos reports to Tableau on Teradata with managed refreshes and permissions
- Python dynamic reports tracking competitor price indices to drive dynamic pricing
- Loyalty, customer-segmentation and market-basket analytics with store-traffic prediction
Freelance Data Analyst
Market and competitor research in the equity crowdfunding sector — SWOT analysis, survey design and insight-driven recommendations that shaped early-stage investment approaches.
Certifications
Credentials in active use.
Each of these maps to something on the job — not collected for the wall.
Certified Data Management Professional
The governance backbone.
✓ CertifiedTOGAF Enterprise Architecture
Architecture method and framework.
✓ CertifiedAgentforce Specialist
Agent Studio, Prompt Builder, guardrails.
✓ CertifiedData 360 Consultant
Data Cloud — harmonisation and identity.
✓ CertifiedTableau Platform
Visualisation, semantics, server admin.
✓ CertifiedSAFe Agilist
Delivery at programme scale.
✓ CertifiedAzure Data Platform
Fabric, OneLake and Purview.
✓ CertifiedLakehouse & Unity Catalog
Governed lakehouse architecture.
✓ CertifiedGoogle Cloud Platform
Cloud data engineering and analytics.
✓ CertifiedAI Governance
Model risk, transparency, accountability.
✓ CertifiedSkills & stack
What I actually work in.
Governance & data management
Analytics & BI
Data platforms & engineering
AI, ML & programming
Leadership & delivery
Writing & speaking
Thinking out loud.
Selected pieces on governance, BI and the agentic AI era — with the reach they earned.
A data governance framework, five pillars
Presented at a Governance Forum: people, data, data stores, processes and governance. The point that landed hardest — governance thrives when roles are genuinely defined. Owners, stewards, custodians and users each hold a distinct piece of the trust chain.
Governance that executes itself
The next breakthrough isn't another framework — it's the shift from documented governance to active, metadata-driven governance. Data discovered automatically, sensitive fields classified in real time, lineage tracked continuously, audit evidence generated as part of the workflow rather than after the fact.
Govern your AI before you use AI to govern data
AI can detect anomalies, suggest classifications, summarise lineage and draft audit evidence. What it cannot do is own accountability. The future isn't AI-driven governance — it's human-led governance, supported by AI-enabled intelligence.
Stop shipping dashboards. Start shipping decisions.
The question is no longer “can we visualise our data?” but “can an AI agent trust our data enough to act on it?” Azure for infrastructure gravity, a semantic trust layer in the middle, agentic analytics on top. Teams still debating which BI tool are answering last decade's question.
“Agents are only as trustworthy as the data beneath them. Ingest high-quality, well-governed data and the agentic layer becomes genuinely reliable. Skip that foundation, and you've simply automated your data problems.”
Get in touch
Let's talk data.
Open to conversations on data governance, BI leadership, AI governance and enterprise data architecture — roles, advisory work or collaboration.
mailme@aniketdwivedi.com