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

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

สรุป

Data Driven Organization Structure Culture Analytics Team Platform dbt BigQuery KPI Framework Decision Making Dashboard Governance Quality Maturity Model Production