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

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 ให้ทุกู้คืนอ่านข้อมูลเป็น
Self-service: สร้าง Dashboard ที่ทุกู้คืนเข้าถึงได้โดยไม่ต้องขอ
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