Data integration · Analytics · AI automation

Fragmented enterprise systems, connected into data you can actually decide on.

EthanCorp designs and runs enterprise data integration, analytics modernisation and AI-enabled workflow automation — for organisations whose data already exists, and whose real problem is that none of it connects.

Built as durable systems, not demos.

12+ years in enterprise data & analytics
20+ enterprise systems integrated
150+ pipelines & workflows built
5+ countries supported
Reference architecture: fragmented source systems to governed decisions Source systems including ERP, MIS, CRM and finance feed an integration and orchestration layer. That layer loads a governed data model with a KPI dictionary and tested transformations, which serves executive reporting, operational dashboards and AI-assisted workflows. A reconciliation and monitoring band runs underneath every stage. Source systems ERP SAP · Dynamics 365 MIS / SIS iSAMS · PowerSchool CRM HubSpot Finance & ops Ledgers · spreadsheets Integration & orchestration Extract APIs · CDC · files Validate Contracts · schema Transform dbt · SQL · Python Load Idempotent · retried Governed model Semantic layer One definition per metric KPI dictionary 27+ standardised KPIs Lineage & tests Version-controlled Decisions Executive reporting Power BI · Tableau Operational dashboards Hourly / near-real-time AI-assisted workflows Retrieval + human review Runs underneath every stage Reconciliation against source · freshness monitoring · failure alerting that names the failing step · runbooks
The shape of most engagements: connect the sources, govern the model, then automate what repeats — in that order. The reconciliation band underneath is what makes the rest trustworthy.

Worked with, in production

Power BI Tableau SAP Microsoft SQL Server Microsoft Azure Amazon Web Services Google Cloud dbt Apache Airflow Talend Python OpenAI

Platform and vendor technologies used in delivered work. Client names are withheld under confidentiality — the numbers throughout this site come from those engagements.

The problem

You probably recognise at least three of these.

None of them are unusual. All of them are expensive, and all of them compound — because every workaround becomes something the next person has to work around.

Systems that do not talk to each other

ERP, MIS, CRM and finance each hold part of the picture. Joining them is a person with an export button.

The same metric, three different answers

Revenue, headcount, utilisation — each report defines them slightly differently, and nobody owns the definition.

Reporting that runs on spreadsheets

A monthly pack assembled by hand, by one person, from files that arrive by email. It works until they are on leave.

Numbers that arrive after the decision

A five-day close means leadership is steering on last month's data, which is a different job from steering on this week's.

Integrations nobody can safely change

Undocumented jobs written years ago, no tests, no lineage. Every schema change is a gamble taken at month-end.

AI that never leaves the pilot

The demo impressed everyone. Then nobody could say what it would cost to run, who reviews its output, or what happens when it is wrong.

What EthanCorp does

Four capabilities, usually delivered in this order.

Foundations before front-ends. Connecting the systems and agreeing the definitions is unglamorous work, and it is what makes everything after it hold.

See how each engagement is scoped

Selected work

The numbers, and what produced them.

Three enterprise engagements and two systems built and run by EthanCorp. Client names are withheld where confidentiality applies; the figures are the ones already published on my professional profile.

01 Confidential client

Enterprise data integration platform

Twenty-plus enterprise systems that each held part of the truth, connected into governed data flows with reconciliation built in — across twelve business units and five countries.

Read the case study

20+enterprise systems integrated
150+pipelines and workflows built
90%processing effort reduction

02 Confidential client

BI & decision intelligence modernisation

A five-day manual reporting cycle replaced with governed dashboards on standardised definitions — and the arguments about whose number was right replaced with one KPI dictionary.

Read the case study

5d → 0.5hreporting cycle
27+KPIs standardised
20+dashboards and reports delivered

03 Confidential client

AI workflow automation

A ten-step manual document workflow reduced to two guided steps, with an explicit human review gate — and the acceptance rate measured rather than assumed.

Read the case study

4h → ~10mtask completion time
10 → 2manual steps in the workflow
100+documents processed per day

04 Live

SAMI — retrieval-grounded iSAMS assistant

A production RAG assistant answering iSAMS questions from a curated knowledge base, with an admin console, cost visibility and a feedback loop — free and open to the iSAMS community.

Read the case study

17in-depth guides in the knowledge base
~6,400embedded vectors backing retrieval
218automated frontend and backend tests

05 In development

LangThang AI — tour advisory chatbot

A retrieval-grounded tour advisor for the Vietnamese market, built to test a two-layer NLU design: deterministic rules first, the language model only where the rules run out.

Read the case study

96backend tests (unit and integration)
2-layerNLU: rules first, LLM on ambiguity
4-stateclosing flow to a qualified lead

How I work

Five principles that decide what gets built.

These are not values on a wall — they are the reasons a proposal gets narrowed, a launch gets delayed, or a request gets pushed back on.

Systems, not demos

Anything shipped should still be running, and still be maintainable, twelve months later. A demo that impresses a steering committee and then rots is a cost, not a delivery.

Foundations before front-ends

Data schemas, state tracking, clean contracts and written procedures come before the dashboard skin. A beautiful report on an ungoverned model is a faster way to be confidently wrong.

Automate the repetitive; preserve the human judgement

Automation removes the copy-paste, never the decision. Human-in-the-loop is a design feature, not an admission that the model is not good enough yet.

Ship v1, measure, iterate

An over-engineered master plan that never goes live has delivered nothing. I would rather put a narrow, working v1 in front of real users and let their behaviour decide what v2 contains.

Radical transparency about cost and failure

Token budgets, API rate limits, latency, schema drift, the failure point you will hit in week three — said up front. Buyers who were warned stay; buyers who were sold magic churn.

How an engagement actually runs

Technical depth

The stack behind the work.

Tools are the supporting detail, not the argument. This is what the delivered systems actually run on.

Languages & data engineering

  • SQL
  • Python
  • Java
  • dbt

Integration & orchestration

  • Talend
  • SSIS
  • Apache Airflow
  • Azure Data Factory
  • n8n
  • REST / OpenAPI

Databases & warehouses

  • SQL Server
  • Oracle
  • MySQL
  • SAP HANA
  • SAP BW
  • SingleStore
  • PostgreSQL

Cloud & data platforms

  • Microsoft Fabric
  • Azure
  • AWS
  • Google Cloud

BI & analytics

  • Power BI
  • Tableau
  • SAP BusinessObjects
  • Looker Studio

AI & automation

  • OpenAI
  • Claude
  • Gemini
  • RAG / Qdrant
  • Python automation
  • Human-in-the-loop review

Enterprise systems

  • SAP ERP
  • Dynamics 365 / CRM
  • HubSpot
  • iSAMS
  • PowerSchool

DevOps & delivery

  • Git
  • GitHub Actions
  • Azure DevOps
  • Docker
  • CI/CD

Who you would be working with

Dat Tran — Enterprise Data & AI Analytics Architect

EthanCorp is a one-person consultancy. There is no bench, no account manager and no junior team: the person who scopes the work is the person who builds it and the person who hands it over.

Twelve-plus years across enterprise data integration, BI delivery and analytics engineering — SAP and Microsoft estates, multi-country operations, and lately AI-enabled workflows with a human review step kept deliberately in place. Currently Application Integration & Data Analytics Lead for Asia at Cognita.

More about the background

Dat Tran, founder of EthanCorp

Insights

Field notes, not thought leadership.

What tends to break, why, and the design decision that prevents it — written from delivery, without naming clients.

Integration8 min read

Enterprise data integration: what usually breaks

Integration projects rarely fail on the connector. They fail on identity, on late-arriving corrections, and on the fact that nobody agreed what a record means. Six failure modes, and the design decision that prevents each.

Read

BI & reporting7 min read

Why executive dashboards fail

The dashboard is usually not the problem. It gets built against available fields instead of a named decision, ships without retiring the spreadsheet it replaced, and has no owner when a number looks wrong.

Read

All insights

Have a data, analytics or automation problem that should not need another workaround?

Tell me what is breaking and what you have already tried. If EthanCorp is not the right fit, I will say so and point you somewhere better.

Response time
Within two business days
Based in
Ho Chi Minh City, Vietnam — working across Asia and remote