Data Driven Organization Structure —

Data Driven Organization

Data Driven Organization Structure Data Culture Analytics Team Platform KPI Framework Decision Making Dashboard Governance Quality Literacy Production

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Model	Structure	ข้อดี	ข้อเสีย	เหมาะกับ
Centralized	ทีม Data รวมศูนย์	Standard เดียว	Bottleneck ช้า	องค์กรเล็ก-กลาง
Decentralized	Data Analyst ทุก BU	เร็ว เข้าใจ Business	ไม่มี Standard	องค์กร Agile
Hub and Spoke	ทีมกลาง + ทีมย่อย	Balance ทั้งสอง	ซับซ้อน	องค์กรกลาง-ใหญ่
Data Mesh	Domain-owned Data Products	Scale ได้ดีมาก	ต้อง Mature	องค์กรใหญ่ Tech-first

Team Structure

# === Data Team Organization ===





from dataclasses import dataclass





@dataclass


class DataRole:


    role: str


    responsibility: str


    skills: str


    reports_to: str


    salary_range: str





roles = [


    DataRole("Chief Data Officer (CDO)", "Data Strategy, Governance, Culture",


        "Leadership, Business Strategy, Data Architecture",


        "CEO", "200-500K+ THB/month"),


    DataRole("Head of Analytics", "Analytics Team Lead, KPI Framework",


        "Analytics, BI, Team Management, Business Acumen",


        "CDO / CTO", "120-250K THB/month"),


    DataRole("Data Engineer", "Data Pipeline, Infrastructure, ETL",


        "Python, SQL, Spark, Airflow, Cloud (AWS/GCP)",


        "Head of Data Engineering", "60-150K THB/month"),


    DataRole("Data Analyst", "Business Analysis, Dashboard, Reporting",


        "SQL, Python, BI Tools, Statistics, Communication",


        "Head of Analytics", "35-80K THB/month"),


    DataRole("Data Scientist", "ML Models, Prediction, Experimentation",


        "Python, ML/DL, Statistics, A/B Testing",


        "Head of Data Science", "60-150K THB/month"),


    DataRole("Analytics Engineer", "Data Modeling, dbt, Data Quality",


        "SQL, dbt, Data Modeling, Testing",


        "Head of Analytics", "50-120K THB/month"),


    DataRole("Data Governance Lead", "Data Quality, Policy, Compliance",


        "Data Management, PDPA/GDPR, Cataloging",


        "CDO", "80-150K THB/month"),


]





print("=== Data Team Roles ===")


for r in roles:


    print(f"  [{r.role}]")


    print(f"    Responsibility: {r.responsibility}")


    print(f"    Skills: {r.skills}")


    print(f"    Reports to: {r.reports_to} | Salary: {r.salary_range}")

Data Platform Architecture

# === Data Platform Stack ===





# Data Ingestion


# Airbyte / Fivetran → Extract from 300+ sources


# Sources: MySQL, PostgreSQL, MongoDB, APIs, S3, Kafka


#


# Data Warehouse


# BigQuery / Snowflake / Redshift


# Raw Layer → Staging Layer → Mart Layer


#


# Transformation (dbt)


# dbt run → Transform raw data to analytics-ready


# dbt test → Data quality checks


# dbt docs → Auto-generated documentation


#


# Orchestration


# Airflow / Dagster / Prefect


# Schedule and monitor all pipelines


#


# BI / Dashboard


# Looker Studio / Metabase / Superset


# Self-service analytics for all teams


#


# Data Catalog


# DataHub / Amundsen


# Search, discover, understand data assets





# dbt model example


# -- models/marts/finance/monthly_revenue.sql


# WITH orders AS (


#     SELECT * FROM {{ ref('stg_orders') }}


#     WHERE status = 'completed'


# ),


# revenue AS (


#     SELECT


#         DATE_TRUNC('month', order_date) AS month,


#         SUM(total_amount) AS revenue,


#         COUNT(*) AS order_count,


#         COUNT(DISTINCT customer_id) AS unique_customers


#     FROM orders


#     GROUP BY 1


# )


# SELECT


#     month,


#     revenue,


#     order_count,


#     unique_customers,


#     revenue / order_count AS aov,


#     LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,


#     (revenue - LAG(revenue) OVER (ORDER BY month)) /


#         NULLIF(LAG(revenue) OVER (ORDER BY month), 0) * 100 AS growth_pct


# FROM revenue





@dataclass


class PlatformLayer:


    layer: str


    tool: str


    purpose: str


    cost: str





stack = [


    PlatformLayer("Ingestion", "Airbyte (OSS) / Fivetran", "Extract data from all sources", "Free OSS / $1-5K/mo"),


    PlatformLayer("Warehouse", "BigQuery / Snowflake", "Central data storage + compute", "$500-5K/mo"),


    PlatformLayer("Transform", "dbt Core (OSS) / dbt Cloud", "SQL-based transformation + testing", "Free OSS / $100/mo"),


    PlatformLayer("Orchestration", "Airflow / Dagster", "Schedule and monitor pipelines", "Free OSS / $500/mo"),


    PlatformLayer("BI Dashboard", "Metabase (OSS) / Looker Studio", "Self-service analytics", "Free OSS / Free"),


    PlatformLayer("Data Catalog", "DataHub (OSS)", "Data discovery and lineage", "Free OSS"),


    PlatformLayer("Data Quality", "Great Expectations / Soda", "Automated data testing", "Free OSS / $300/mo"),


    PlatformLayer("Governance", "Custom + DataHub", "Access control, PDPA compliance", "Custom"),


]





print("\n=== Data Platform Stack ===")


total_min = 0


for s in stack:


    print(f"  [{s.layer}] Tool: {s.tool}")


    print(f"    Purpose: {s.purpose} | Cost: {s.cost}")

KPI Framework and Maturity

# === Data Maturity Assessment ===





@dataclass


class MaturityLevel:


    level: int


    name: str


    characteristics: str


    data_usage: str


    next_step: str





levels = [


    MaturityLevel(1, "Ad-hoc", "ไม่มีระบบ ข้อมูลกระจาย Excel-based",


        "ดูข้อมูลเมื่อถูกถาม ไม่มี Dashboard",


        "สร้าง Data Warehouse รวมข้อมูล"),


    MaturityLevel(2, "Reactive", "มี Dashboard แต่ดูย้อนหลัง",


        "รู้ว่าเกิดอะไรขึ้น (What happened)",


        "เพิ่ม Analysis ตอบ Why"),


    MaturityLevel(3, "Proactive", "วิเคราะห์หาสาเหตุ ตั้ง Alert",


        "รู้ว่าทำไม (Why) + แจ้งเตือนอัตโนมัติ",


        "เพิ่ม Prediction ML Model"),


    MaturityLevel(4, "Predictive", "ใช้ ML ทำนายอนาคต",


        "รู้ว่าจะเกิดอะไร (What will happen)",


        "เพิ่ม Automation Decision"),


    MaturityLevel(5, "Prescriptive", "AI แนะนำ Action อัตโนมัติ",


        "รู้ว่าควรทำอะไร (What should we do)",


        "Continuous Improvement"),


]





print("Data Maturity Model:")


for l in levels:


    print(f"  [Level {l.level}: {l.name}]")


    print(f"    Characteristics: {l.characteristics}")


    print(f"    Data Usage: {l.data_usage}")


    print(f"    Next Step: {l.next_step}")





# KPI Framework


kpi_framework = {


    "Executive": "Revenue Growth, Market Share, Customer Satisfaction, NPS",


    "Marketing": "CAC, ROAS, Conversion Rate, Channel Attribution",


    "Sales": "Pipeline Value, Win Rate, Sales Cycle, Quota Attainment",


    "Product": "DAU/MAU, Retention, Feature Adoption, NPS",


    "Engineering": "Deploy Frequency, MTTR, Change Failure Rate, Uptime",


    "Finance": "Burn Rate, Runway, Unit Economics, Gross Margin",


    "HR": "eNPS, Turnover Rate, Time to Hire, Training Hours",


}





print(f"\n\nKPI by Department:")


for k, v in kpi_framework.items():


    print(f"  [{k}]: {v}")

เคล็ดลับ

Culture: ผู้บริหารต้อง Lead by Example ใช้ข้อมูลตัดสินใจ
Literacy: จัด Data Literacy Training ให้ทุกคนอ่านข้อมูลเป็น
Self-service: สร้าง Dashboard ที่ทุกคนเข้าถึงได้โดยไม่ต้องขอ
Quality: ข้อมูลที่ผิดเป็นอันตราย ตรวจสอบคุณภาพอัตโนมัติ
Start Small: เริ่มจาก 1 Use Case ที่ให้ ROI สูงสุด แล้วขยาย

Data Driven Organization คืออะไร

องค์กรใช้ข้อมูลตัดสินใจทุกระดับ C-Level Operation Data Infrastructure Analytics Dashboard KPI Real-time Data Literacy Governance

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