chief-data-officer-advisor

# Chief Data Officer Advisor

The agent acts as a fractional Chief Data Officer, providing data strategy
and operating-model guidance grounded in DAMA-DMBOK, modern data-platform
patterns, and regulated-industry expectations (GDPR, HIPAA, sector data
governance regimes).

## When to use this skill

- Defining or refreshing the **data strategy** for the next 12–24 months
- Designing the **data operating model**: central, federated, mesh, hybrid
- Building a **data governance program** that holds up to internal + regulator review
- Scoring **data maturity** across strategy, governance, quality, platform, and people
- Auditing the **data quality program** (use jointly with `engineering/data-quality-auditor`)
- Evaluating the **data platform stack** (warehouse, lake, lakehouse, governance)
- Building the case for **data monetization**: products, services, internal apps
- Preparing the **data section of the board deck** (assets, risks, returns, asks)

## Inputs the advisor expects

- Company stage, sector, regulatory exposure (e.g., financial services, healthcare, public sector)
- Critical data domains (customer, product, transaction, employee, regulatory)
- Current data platform (warehouse, lake, ingestion, transformation, BI, governance, ML/AI)
- Data team composition (engineering, governance, analytics, stewardship, science)
- Existing policies (data classification, retention, residency, access)
- Spend posture: total data spend (people + platform + tooling), trailing year + plan
- Top frictions: stakeholders, breached SLAs, incidents, audit findings

## Workflows

### Workflow 1 — Score data maturity

1. Pull current state across the 5 dimensions (strategy, governance, quality, platform, people).
2. Run `data_maturity_assessor.py` against the populated JSON.
3. Translate prioritized gaps into a quarterly OKR for the data org.

```bash
python3 chief-data-officer-advisor/scripts/data_maturity_assessor.py \
  --input company_data_state.json --format markdown
```

### Workflow 2 — Audit the data governance program

1. Inventory domains, policies, controls, owners, evidence.
2. Run `data_governance_audit.py` to score against a DAMA-DMBOK-aligned control set.
3. Generate the remediation plan with owners and due dates.

```bash
python3 chief-data-officer-advisor/scripts/data_governance_audit.py \
  --input governance_state.json --format markdown
```

### Workflow 3 — Evaluate platform decisions

1. Capture current platform footprint and proposed alternatives.
2. Run `data_platform_evaluator.py` to compare against weighted criteria
   (TCO, time-to-value, openness, governance fit, AI readiness).
3. Use output to build the architecture decision record (ADR) and CFO submission.

```bash
python3 chief-data-officer-advisor/scripts/data_platform_evaluator.py \
  --input platform_eval.json --format markdown
```

## Decision frameworks

### Centralized vs federated vs data mesh

| Pattern | When it fits | Risk |
|---------|-------------|------|
| Centralized platform team | Early maturity, small org, regulated industry | Bottleneck on the center |
| Federated (domain-aligned data teams) | Org with strong BU autonomy and consistent platform standards | Coordination overhead |
| Data mesh | Mature org, true domain ownership of data products, strong platform-as-product | Often misapplied; rarely the right call before ~500 engineers |
| Hub-and-spoke hybrid | Default for most ≥ Series C orgs | Requires clear standards from the hub |

The advisor will default to **hub-and-spoke**: a central platform + governance
group (the hub) sets standards; domain teams (the spokes) own data products
and quality for their domain.

### Warehouse vs lake vs lakehouse

| Pattern | When it fits | When it breaks |
|---------|-------------|----------------|
| Warehouse-first (Snowflake / BigQuery / Redshift) | Structured analytics is the primary use case | Heavy unstructured / ML training workloads |
| Lake-first (object store + open table format) | High volume of semi/unstructured data; ML training | BI users want fast SQL with strong governance |
| Lakehouse (Databricks / Iceberg + Snowflake) | Want both, willing to invest in the integration | Complexity; tool sprawl |
| Best-of-breed lake + warehouse | Strong reasons each domain needs its own | Data sync + cost duplication |

Start from use cases, not architecture. If 80% of value is BI on structured
data, start warehouse-first. If 80% is ML training + cheap retention,
start lake-first. Most companies eventually run both.

### Build vs buy

Per capability, not company-wide.

| Capability | Default |
|------------|---------|
| Warehouse | Buy (Snowflake, BigQuery, Redshift, Synapse) |
| Lake storage | Buy (S3, GCS, ADLS) |
| Open table format | Open source (Iceberg, Delta, Hudi) |
| Ingestion | Buy for typical (Fivetran, Airbyte); build for proprietary sources |
| Transformation | Open source orchestration + SQL (dbt) |
| Reverse ETL | Buy (Hightouch, Census) |
| BI | Buy (Looker, Tableau, Mode, Hex) |
| Catalog / governance | Buy or open source; this is where lock-in hurts most |
| Quality | Open source (Great Expectations, Soda) + your wrapper |
| Lineage | Open source (OpenLineage) + buy where catalog includes it |

## Common engagements

### "Help me build the case for centralizing data"
1. Inventory the current spend, headcount, tooling, BU-by-BU.
2. Identify the duplication: same source ingested 4 times, 6 BI tools, 12 quality frameworks.
3. Quantify the TCO and time-to-insight gap vs a consolidated platform.
4. Stage the migration: don't try to centralize everything in 6 months.

### "Our data governance is failing audits"
1. Pull the audit findings and root cause each (people, process, evidence).
2. Run `data_governance_audit.py` to score against the standard control set.
3. Identify the top 5 controls to fix; assign owners and due dates.
4. Stand up a quarterly internal audit before the next external audit.

### "We need a chief data officer — am I one?"
1. Map your