Helping businesses turn data into insights with AI strategy and machine learning — executive-ready dashboards, automated pipelines, and predictive models — from Tokyo, across Asia-Pacific and beyond.
Trilingual (EN / JP / ZH) · 10+ Years · 6 Cities across Asia-Pacific
Companies across Asia-Pacific are drowning in data but starving for decisions. Scattered spreadsheets, disconnected dashboards, and manual reporting slow everything down — especially in regulated industries like banking, healthcare, and insurance where data governance is non-negotiable.
I bridge that gap. With 10+ years spanning BI development (Tableau, Power BI), ETL pipeline architecture (GCP, Microsoft Fabric, BigQuery), data science and ML, and data governance (Collibra, Informatica), I deliver end-to-end solutions — from raw data to board-room dashboards. My B.S. in Computer Science & Information Engineering provides the technical foundation; my decade of real-world delivery provides the judgment.
What makes my approach different: I'm trilingual (English, Japanese, Mandarin Chinese) with hands-on experience across Tokyo, Hong Kong, Shenzhen, Taipei, Shanghai, and Kuala Lumpur. I work seamlessly with Japanese enterprise clients, Chinese tech teams, and English-speaking stakeholders — often in the same engagement.
Designing and implementing KPI dashboards and self-service analytics tools using Tableau, Power BI, and Domo. Building semantic models, DAX measures, and interactive reports for executive decision-making across industries.
Architecting automated ETL pipelines using GCP Cloud Functions, BigQuery, Snowflake, and Microsoft Fabric. Building data marts, data warehouses, and data migration solutions across enterprise systems like SAP and Dynamics 365.
Building predictive models for customer churn, segmentation, sales forecasting, RFM analysis, and NLP. Full data science lifecycle from feature engineering and model training to visualization and stakeholder reporting.
Implementing data governance frameworks aligned with the DMBOK 2 standard (DAMA International). Building data catalogs, data lineage, metadata management, data quality monitoring, and master data management for enterprise compliance and decision-making.
Replaced fragmented departmental reporting with a single source of truth. Stakeholders went from waiting days for excel reports to exploring live KPIs independently. Eliminated metric discrepancies across departments and turned data skeptics into daily self-service users.
Eliminated manual reporting entirely for a fast-growing e-commerce company. Leadership went from stale spreadsheets to real-time dashboards that refresh automatically. The system scaled seamlessly through holiday peak traffic without any manual intervention or additional cost.
Brought an insurance company from "we can't trace where our numbers come from" to passing a regulatory audit with zero critical findings. Built full data lineage so auditors could trace any figure back to its source. Dramatically improved data quality and cut the time to assess impact of system changes from days to hours.
Turned "we're losing customers and don't know why" into a system that identifies at-risk customers before they leave — and tells the retention team exactly what to do about each one. Significantly reduced monthly churn and retained substantial recurring revenue that would have otherwise walked out the door.
Designed the data architecture that let a B2B SaaS platform serve thousands of tenants without compromising on security or performance. Enterprise customers got dedicated isolation while SMB customers stayed cost-efficient — and every tenant saw only their own data in embedded dashboards, no exceptions.
An interactive data art piece visualizing Tokyo's 23 special wards as a living network organism. Real population density data drives node size, color, and orbital motion. Mouse interaction reveals ward-level statistics and creates dynamic connections across the data mesh.
Built the data backbone for an autonomous racing program where split-second decisions determine outcomes. Race engineers went from delayed batch reports to live dashboards showing every sensor in near real-time. Pit strategy decisions became dramatically faster, and tire degradation predictions gave the team a clear competitive edge.
Download the Data Strategy Readiness Checklist — 22 questions aligned with the DMBOK 2 framework across all 11 knowledge areas. See exactly where your data management capabilities stand and where to invest next.
A behind-the-scenes look at how Tokyo Pulse was built — the design decisions, the iterations, and what it takes to make data feel alive.
How I transformed dry census data from Tokyo's 23 wards into an interactive, breathing artwork that reveals the hidden rhythms of one of the world's greatest cities.
Inside the data engineering behind Formula 1 telemetry — from 300 sensors per car to real-time pit strategy. A breakdown of the streaming architectures that power modern motorsport.
Lessons from the Entrepreneur First startup accelerator in Hong Kong. What it's really like to find a co-founder, validate AI SaaS startup ideas, and go from zero to one — and why I'm ready to do it again.
The analytics infrastructure that scales from 100 to 100,000 customers — event collection, data warehouse, transformation, and BI for SaaS product teams.
A practical framework for building an enterprise AI strategy. Learn how to identify high-value AI use cases, build the right data foundations, and scale from pilot to production.
The three multi-tenant data architecture patterns for SaaS — shared database, database-per-tenant, and hybrid. How to choose the right one before your Series B forces a rebuild.
A practical guide to building an effective data strategy. Learn how to align data infrastructure, governance, and analytics with business goals to unlock measurable value from your data assets.
An honest comparison of Tableau and Power BI from a consultant who has deployed both. Covers cost, data modeling, visualization, enterprise fit, and when to choose each tool.
A case study on implementing centralized data visualization with Microsoft Fabric, DAX semantic models, and Power BI self-service analytics for a consulting firm.
Simba Hu specializes in business intelligence, data science, machine learning, and data engineering. With 10+ years of experience, he helps businesses turn raw data into actionable insights using BI tools (Tableau, Power BI), cloud platforms (GCP, AWS), and data technologies like Python, SQL, Snowflake, and BigQuery.
Simba Hu offers end-to-end data and AI consulting: BI dashboard development with Tableau, Power BI, and Domo; ETL pipeline architecture using GCP, Microsoft Fabric, and Snowflake; data science and ML models for customer segmentation, churn prediction, and sales forecasting; data governance with Collibra, Informatica, and Alteryx; and data migration services.
You can reach Simba Hu via email at simba.hu@outlook.com or connect on LinkedIn. Based in Tokyo, Japan, he is a trilingual professional (English, Japanese, Mandarin Chinese) available for part-time positions, contract engagements, and consulting projects across Japan and globally.
Simba Hu has delivered data solutions in banking and finance, healthcare, insurance, e-commerce, digital marketing, IT consulting, and software. He has worked at companies across Tokyo, Hong Kong, Shenzhen, Taipei, Shanghai, and Kuala Lumpur — working with international consulting firms, startups, and enterprise clients.
Key achievements include: building centralized Power BI reporting with Microsoft Fabric at a major consulting firm, engineering automated ETL pipelines using GCP Cloud Functions for e-commerce, executing SAP-to-Dynamics 365 data migrations, implementing data governance with Collibra and Informatica, and building customer segmentation models for banking clients.
Yes. Simba Hu has experience in machine learning (scikit-learn, XGBoost, TensorFlow), NLP (NLTK, spaCy, gensim), chatbot development (Line Bot, Dialogflow), and generative AI. He validated AI startup ideas at a leading accelerator program in Hong Kong and has built predictive models for customer churn, segmentation, sales forecasting, and sentiment analysis.
Yes. His consulting approach is aligned with the DMBOK 2 framework (Data Management Body of Knowledge) by DAMA International, covering all 11 knowledge areas: data governance, data architecture, data modeling & design, data storage & operations, data security, data integration & interoperability, document & content management, reference & master data, data warehousing & BI, metadata management, and data quality. Download the free DMBOK-aligned Data Strategy Readiness Checklist to assess your organization's maturity.
Data governance is the exercise of authority, control, and shared decision-making over data assets — the foundational knowledge area in the DMBOK 2 framework. It ensures data accuracy, security, regulatory compliance (GDPR, APPI, CCPA), and accessibility across your organization. Simba Hu has implemented governance frameworks using Collibra, Informatica, and Alteryx for enterprise clients in insurance and banking.
Looking to build an AI strategy, deploy machine learning models, or integrate generative AI into your product? Based in Tokyo, I work with clients across Japan and globally. I offer complimentary initial consultations to understand your data challenges and recommend the right approach.