Skill: Assess Data Platform Maturity

Domain

technology

Description

Evaluates enterprise data platform maturity across data engineering, governance, analytics, and ML operations dimensions. Provides a comprehensive assessment for CDOs and CIOs to benchmark capabilities, identify gaps, and prioritize data infrastructure investments.

Business Rules

This skill implements a data platform maturity model based on industry frameworks (DCAM, DAMA-DMBOK):

1. •Data Engineering Maturity: Batch vs. streaming, ELT pipelines, data lakehouse architecture, DataOps practices
2. •Data Governance Maturity: Cataloging, lineage, quality rules, stewardship, privacy controls
3. •Analytics Maturity: Self-service BI adoption, semantic layers, embedded analytics, decision intelligence
4. •ML Operations Maturity: Feature stores, model registry, automated retraining, A/B testing infrastructure
5. •Data Culture Score: Data literacy programs, data-driven decision metrics, democratization index

Input Parameters

• •organization_name (string): Name of the organization
• •industry (string): Industry for benchmark comparison
• •data_volume_tb (float): Total data volume in TB
• •data_sources_count (int): Number of integrated data sources
• •real_time_pipelines (bool): Whether real-time/streaming pipelines exist
• •data_catalog_implemented (bool): Whether enterprise data catalog exists
• •data_lineage_automated (bool): Whether lineage is automatically tracked
• •data_quality_score (float): Current data quality score if measured (0-100)
• •self_service_bi_adoption (float): Percentage of business users with BI access (0-100)
• •ml_models_in_production (int): Number of ML models in production
• •feature_store_implemented (bool): Whether centralized feature store exists
• •mlops_automation_level (string): "manual", "semi_automated", "fully_automated"
• •data_literacy_program (bool): Whether formal data literacy training exists
• •data_mesh_adopted (bool): Whether data mesh/domain-oriented architecture is used
• •cloud_data_platform (string): Primary platform ("snowflake", "databricks", "bigquery", "redshift", "azure_synapse", "on_prem")

Output

Returns a maturity assessment with:

• •overall_maturity_level (int): 1-5 maturity level
• •overall_maturity_label (string): "Initial", "Developing", "Defined", "Managed", "Optimizing"
• •dimension_scores (dict): Scores for each maturity dimension
• •industry_percentile (int): Percentile ranking vs. industry peers
• •capability_gaps (list): Critical capability gaps identified
• •quick_wins (list): High-impact, low-effort improvements
• •strategic_investments (list): Major investments recommended
• •target_architecture (dict): Recommended target state architecture
• •roadmap_phases (list): Phased improvement roadmap
• •estimated_investment (dict): Investment ranges by phase
• •business_value_potential (dict): Projected value from improvements

Usage Example

from data_platform_maturity import assess_maturity

result = assess_maturity(
    organization_name="Acme Financial",
    industry="financial_services",
    data_volume_tb=500,
    data_sources_count=150,
    real_time_pipelines=True,
    data_catalog_implemented=True,
    data_lineage_automated=False,
    data_quality_score=72,
    self_service_bi_adoption=35,
    ml_models_in_production=12,
    feature_store_implemented=False,
    mlops_automation_level="semi_automated",
    data_literacy_program=True,
    data_mesh_adopted=False,
    cloud_data_platform="snowflake"
)

Tags

technology, data-platform, analytics, mlops, data-governance, cdo, data-engineering, lakehouse

Implementation

The maturity assessment logic is implemented in data_platform_maturity.py and references:

• •maturity_criteria.csv - Detailed criteria for each maturity level
• •industry_benchmarks.csv - Industry-specific benchmark data
• •capability_weights.csv - Weighting factors for capability scoring
• •investment_ranges.csv - Typical investment ranges by improvement type