Chief Data Officer Advisor Skill for Claude

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

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

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

Workflow 2 — Audit the data governance program

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

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

Workflow 3 — Evaluate platform decisions

• Capture current platform footprint and proposed alternatives.
• Run data_platform_evaluator.py to compare against weighted criteria

(TCO, time-to-value, openness, governance fit, AI readiness).

• Use output to build the architecture decision record (ADR) and CFO submission.

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"

• Inventory the current spend, headcount, tooling, BU-by-BU.
• Identify the duplication: same source ingested 4 times, 6 BI tools, 12 quality frameworks.
• Quantify the TCO and time-to-insight gap vs a consolidated platform.
• Stage the migration: don't try to centralize everything in 6 months.

"Our data governance is failing audits"

• Pull the audit findings and root cause each (people, process, evidence).
• Run data_governance_audit.py to score against the standard control set.
• Identify the top 5 controls to fix; assign owners and due dates.
• Stand up a quarterly internal audit before the next external audit.

"We need a chief data officer — am I one?"

• Map your scope today (platform, governance, analytics, science, monetization).
• Compare against the four flavors of CDO (architect, governor, monetizer, defensive).
• Be honest about which one your company actually needs.
• If you don't have full board access, you're not a CDO yet; you're a head of data.

Anti-patterns to avoid

• Data strategy that doesn't tie to a business outcome. "Be a data-driven company" is not a strategy.
• Catalog-as-policy. A catalog with no enforcement teeth is shelfware. Tie classifications to access controls, not just to documentation.
• Quality as one team's problem. Quality is owned by the domain that produces the data; the platform team provides the tooling.
• Replatforming as a strategy. "We're moving from Redshift to Snowflake" is a tactic, not a strategy.
• The 4-year data lake. If you can't ship value in 6 months, you've over-scoped.
• Hiring a CDO with no platform partner. Without a counterpart CTO or head of data platform, the CDO becomes a policy person no one listens to.
• Mistaking dashboards for data products. A dashboard with no SLA and no owner is not a product.

References

• references/data-strategy-framework.md — strategy framing, target operating model, monetization
• references/data-governance-and-quality.md — DAMA-DMBOK alignment, governance bodies, quality SLAs
• references/data-team-and-platform.md — org design, role definitions, platform stack patterns

Related skills

• c-level-advisor/cto-advisor — for the broader tech platform decisions
• c-level-advisor/ciso-advisor — for data classification and security controls
• c-level-advisor/chief-ai-officer-advisor — for the AI ↔ data interface
• engineering/data-quality-auditor — for the deep DQ implementation
• engineering/senior-data-engineer — for pipeline implementation
• ra-qm-team/gdpr-dsgvo-expert — for personal data governance under GDPR

Output expectations

When the advisor runs, you should walk away with:

• A clear point of view (no "it depends" without a decision criterion)
• 2–4 concrete next actions with owners and timelines
• Open questions that materially change the recommendation
• References to scripts and reference docs that deepen the analysis