Data Architecture Strategy

What is Data Architecture Strategy (for a Data Architect)?

Data Architecture Strategy defines how your organization collects, stores, shares, governs, and activates data to achieve measurable business outcomes. As a Data Architect, you convert goals (e.g., faster analytics, ML enablement, compliance) into an actionable target architecture, a standards-based platform roadmap, and a realistic build-vs-buy plan.

Who this is for

• Data Architects defining or modernizing an enterprise data platform.
• Senior Data/Analytics Engineers growing into architecture leadership.
• Platform Leads responsible for standardizing tooling and practices.

Prerequisites

• Comfort with data warehousing, batch/stream processing, and data modeling.
• Basic understanding of cloud services (object storage, compute, networking).
• Familiarity with CI/CD, IaC concepts, and observability basics.

Outcomes

• Define a target data architecture tied to business domains and outcomes.
• Evaluate build vs buy with a transparent scoring model.
• Author a platform roadmap with standards and adoption milestones.
• Create data product contracts with SLOs for quality and freshness.
• Plan for scale and cost with simple capacity and TCO models.

Learning path

Worked examples

Example 1 — Target architecture outline

# Domains: Sales, Marketing, Finance
# Core capabilities
- Ingestion: batch (files), streaming (events)
- Storage: object storage (raw), warehouse (curated), feature store (ML)
- Processing: orchestration, streaming jobs, dbt/ELT
- Serving: BI semantic layer, APIs, reverse ETL
- Governance: catalog, lineage, PII tagging, RBAC, data contracts
- Observability: data quality checks, freshness SLOs, cost dashboards

# ASCII view
[Sources] --(ingest)--> [Raw/Obj Store] --(ELT)--> [Warehouse]
   |                                 \
   +--(events)--> [Stream] --(proc)--> [Feature Store]

[Warehouse] --(BI/SQL)--> [Dashboards]
[Warehouse] --(Reverse ETL)--> [Ops Apps]
[Contracts+Catalog] span across all

Example 2 — Build vs Buy scoring

{
  "criteria_weights": {"time_to_value": 0.30, "TCO": 0.25, "scalability": 0.20, "operability": 0.15, "lock_in": 0.10},
  "scores": {
    "build": {"time_to_value": 2, "TCO": 3, "scalability": 4, "operability": 3, "lock_in": 5},
    "buy":   {"time_to_value": 5, "TCO": 4, "scalability": 4, "operability": 4, "lock_in": 2}
  }
}
# Weighted total (0-5 scale)
# build = 2*.30 + 3*.25 + 4*.20 + 3*.15 + 5*.10 = 0.60 + 0.75 + 0.80 + 0.45 + 0.50 = 3.10
# buy   = 5*.30 + 4*.25 + 4*.20 + 4*.15 + 2*.10 = 1.50 + 1.00 + 0.80 + 0.60 + 0.20 = 4.10
# Decision: Buy (faster value, manageable TCO)

Example 3 — Platform roadmap (4 quarters)

• Q1: Raw zone + catalog + identity/RBAC. Pilot ingestion for Sales.
• Q2: Curated warehouse + dbt standards + data contracts v1. First BI dashboards.
• Q3: Streaming pipeline + quality SLOs + cost FinOps dashboard.
• Q4: Feature store + reverse ETL + multi-domain onboarding playbook.

# Naming convention (example)
raw.<domain>_<source>_<entity>__y=<YYYY>/m=<MM>/d=<DD>
wh.<domain>.<model_type>_<entity> (e.g., wh.sales.dim_customer)

Example 4 — Technology selection criteria (YAML)

must_have:
  - supports_sql_acid
  - columnar_storage
  - fine_grained_rbac
  - region_residency_controls
nice_to_have:
  - time_travel
  - built_in_lineage
  - serverless_auto_scaling
constraints:
  - budget_tier: medium
  - talent_pool: strong_sql_medium_python
evaluation:
  - benchmark_tpch_sf100
  - security_review
  - operability_walkthrough

Example 5 — Cost and scalability planning

# Simple monthly TCO model (illustrative)
warehouse_compute_hours = 400   # hrs
warehouse_price_per_hour = 4.0  # $/hr
object_storage_tb = 50          # TB
object_storage_price = 20       # $/TB
stream_msgs_million = 800       # million msgs
stream_price_per_million = 0.40 # $/M

monthly_cost = (warehouse_compute_hours*warehouse_price_per_hour) \
             + (object_storage_tb*object_storage_price) \
             + (stream_msgs_million*stream_price_per_million)
# monthly_cost = 1600 + 1000 + 320 = $2,920

Plan thresholds: when cost > budget or p95 latency > SLO, trigger scaling or optimization playbooks.

Example 6 — Data product contract (JSON)

{
  "product": "customer_ltv",
  "version": "1.2.0",
  "owner": "growth_analytics",
  "schema": {
    "customer_id": "string",
    "ltv_usd": "number",
    "as_of_date": "date"
  },
  "SLOs": {
    "freshness_minutes": 60,
    "availability_percent": 99.5,
    "accuracy_percent": 98
  },
  "quality_checks": ["not_null(customer_id)", "non_negative(ltv_usd)"]
}

Drills and exercises

• Write three business outcomes and a measurable KPI for each.
• Sketch your target architecture and mark cross-cutting concerns.
• Define five tech selection must-haves and three nice-to-haves.
• Create a decision matrix for one build-vs-buy choice.
• Draft a 2-quarter roadmap with clear adoption gates.
• Write a data product contract with 2 SLOs and 2 quality checks.
• Estimate monthly TCO with at least three cost drivers.

Common mistakes and debugging tips

Mini project: Design a platform strategy for a subscription app

Goal: Enable churn prediction and revenue analytics within 2 quarters.

Deliverables

• Target architecture diagram and NFRs.
• Tech selection criteria + build-vs-buy decision for streaming and warehouse.
• Two data product contracts: subscriptions, churn_features.
• Q1–Q2 roadmap with adoption gates and TCO estimate.

Acceptance checklist

• Each product has schema, ownership, and SLOs.
• Roadmap includes at least one pilot and a rollback plan.
• TCO modeled at current and 3x scale.
• Risks and mitigations listed (top 5).

Next steps

• Pick one domain and draft a minimal target architecture with SLOs.
• Write your first data product contract and add two quality checks.
• Estimate a 2-quarter roadmap and TCO at 1x and 3x scale.
• Then, take the Skill Exam below to validate your understanding.