This post provides a comprehensive maturity model for assessing Technical Infrastructure within an organization. It covers four key dimensions: Governance ToolsData IntegrationMetadata Management, and Automation Level. Each dimension includes specific questions to evaluate the technical capabilities supporting data governance.

Assessment Overview

The following sections assess the organization’s technical infrastructure for data governance, focusing on tool utilization, data integration, metadata management, and automation. Each question includes maturity levels (1 to 5) with evaluation guidelines to determine your organization’s current state.

Dimension 1: Governance Tools

Focuses on the utilization of tools to support data governance.

1.1 Is a data catalog used?

LevelDescriptionEvaluation Guideline
Level 1No usage.No usage exists.
Level 2Limited usage.Limited usage occurs.
Level 3Basic usage.Basic usage exists.
Level 4Regular usage.Regular usage occurs.
Level 5Systematic usage.Systematic usage is proven.

1.2 Is there an MDM (Master Data Management) tool?

LevelDescriptionEvaluation Guideline
Level 1No tool.No tool exists.
Level 2Informal tool.Informal tool exists.
Level 3Basic tool.Basic tool exists.
Level 4Standardized tool.Standardized tool exists.
Level 5Advanced tool.Advanced tool is proven.

1.3 Are tools integrated enterprise-wide?

LevelDescriptionEvaluation Guideline
Level 1No integration.No integration exists.
Level 2Limited integration.Limited integration occurs.
Level 3Some integration.Some integration exists.
Level 4Mostly integrated.Mostly integrated tools.
Level 5Fully integrated.Fully integrated tools are proven.

1.4 Is training on tool usage provided?

LevelDescriptionEvaluation Guideline
Level 1No training.No training exists.
Level 2Informal training.Informal training occurs.
Level 3Basic training.Basic training exists.
Level 4Regular training.Regular training occurs.
Level 5Comprehensive training.Comprehensive training is proven.

1.5 Do tools contribute to real-time management?

LevelDescriptionEvaluation Guideline
Level 1No contribution.No contribution exists.
Level 2Limited contribution.Limited contribution occurs.
Level 3Some contribution.Some contribution exists.
Level 4Mostly contributes.Mostly contributes to management.
Level 5Fully contributes.Fully contributes is proven.

Dimension 2: Data Integration

Focuses on the integration and accessibility of data sources.

2.1 Are data sources integrated?

LevelDescriptionEvaluation Guideline
Level 1No integration.No integration exists.
Level 2Limited integration.Limited integration occurs.
Level 3Some integration.Some integration exists.
Level 4Mostly integrated.Mostly integrated sources.
Level 5Fully integrated.Fully integrated sources are proven.

2.2 Is access to integrated data easy?

LevelDescriptionEvaluation Guideline
Level 1No access.No access exists.
Level 2Limited access.Limited access occurs.
Level 3Some access.Some access exists.
Level 4Mostly accessible.Mostly accessible data.
Level 5Fully accessible.Fully accessible data is proven.

2.3 Is the integration process standardized?

LevelDescriptionEvaluation Guideline
Level 1No standardization.No standardization exists.
Level 2Informal standardization.Informal standardization occurs.
Level 3Basic standardization.Basic standardization exists.
Level 4Mostly standardized.Mostly standardized process.
Level 5Fully standardized.Fully standardized process is proven.

2.4 Is integrated data used for governance?

LevelDescriptionEvaluation Guideline
Level 1No usage.No usage exists.
Level 2Limited usage.Limited usage occurs.
Level 3Some usage.Some usage exists.
Level 4Mostly used.Mostly used for governance.
Level 5Fully used.Fully used for governance is proven.

2.5 Is real-time integration possible?

LevelDescriptionEvaluation Guideline
Level 1No integration.No integration exists.
Level 2Irregular integration.Irregular integration occurs.
Level 3Regular integration.Regular integration occurs.
Level 4Some real-time.Some real-time integration exists.
Level 5Fully real-time.Fully real-time integration is proven.

Dimension 3: Metadata Management

Focuses on the system for creating and managing metadata.

3.1 Is metadata created?

LevelDescriptionEvaluation Guideline
Level 1No creation.No creation exists.
Level 2Limited creation.Limited creation occurs.
Level 3Some creation.Some creation exists.
Level 4Regular creation.Regular creation occurs.
Level 5Systematic creation.Systematic creation is proven.

3.2 Is metadata standardized?

LevelDescriptionEvaluation Guideline
Level 1No standardization.No standardization exists.
Level 2Informal standardization.Informal standardization occurs.
Level 3Basic standardization.Basic standardization exists.
Level 4Mostly standardized.Mostly standardized metadata.
Level 5Fully standardized.Fully standardized metadata is proven.

3.3 Is metadata searchable?

LevelDescriptionEvaluation Guideline
Level 1Not searchable.Not searchable.
Level 2Limited searchability.Limited searchability exists.
Level 3Some searchability.Some searchability exists.
Level 4Mostly searchable.Mostly searchable metadata.
Level 5Fully searchable.Fully searchable metadata is proven.

3.4 Is metadata regularly updated?

LevelDescriptionEvaluation Guideline
Level 1No updates.No updates exist.
Level 2Irregular updates.Irregular updates occur.
Level 3Some updates.Some updates occur.
Level 4Regular updates.Regular updates occur.
Level 5Real-time updates.Real-time updates are proven.

3.5 Is metadata used for analysis?

LevelDescriptionEvaluation Guideline
Level 1No usage.No usage exists.
Level 2Limited usage.Limited usage occurs.
Level 3Some usage.Some usage exists.
Level 4Mostly used.Mostly used for analysis.
Level 5Fully used.Fully used for analysis is proven.

Dimension 4: Automation Level

Focuses on the degree of automation in governance processes.

4.1 Are governance tasks automated?

LevelDescriptionEvaluation Guideline
Level 1No automation.No automation exists.
Level 2Limited automation.Limited automation occurs.
Level 3Some automation.Some automation exists.
Level 4Mostly automated.Mostly automated tasks.
Level 5Fully automated.Fully automated tasks are proven.

4.2 Are automation tools used?

LevelDescriptionEvaluation Guideline
Level 1No tools.No tools are used.
Level 2Informal tools.Informal tools are used.
Level 3Basic tools.Basic tools are used.
Level 4Standardized tools.Standardized tools are used.
Level 5Advanced tools.Advanced tools are proven.

4.3 Does automation improve efficiency?

LevelDescriptionEvaluation Guideline
Level 1No efficiency.No efficiency exists.
Level 2Limited efficiency.Limited efficiency occurs.
Level 3Some efficiency.Some efficiency exists.
Level 4Mostly efficient.Mostly efficient automation.
Level 5Fully efficient.Fully efficient automation is proven.

4.4 Is the scope of automation expanding?

LevelDescriptionEvaluation Guideline
Level 1No expansion.No expansion exists.
Level 2Limited expansion.Limited expansion occurs.
Level 3Some expansion.Some expansion exists.
Level 4Regular expansion.Regular expansion occurs.
Level 5Systematic expansion.Systematic expansion is proven.

4.5 Does automation operate in real-time?

LevelDescriptionEvaluation Guideline
Level 1No real-time.No real-time operation exists.
Level 2Irregular operation.Irregular operation occurs.
Level 3Regular operation.Regular operation occurs.
Level 4Some real-time.Some real-time operation exists.
Level 5Fully real-time.Fully real-time operation is proven.

How to Use This Model

Use the evaluation guidelines for each question to assess your organization’s maturity in technical infrastructure for data governance across all dimensions. Identify gaps in tool usage, data integration, metadata management, and automation levels, then take steps to progress toward higher maturity levels by adopting advanced tools, standardizing processes, and increasing automation for real-time governance.

By ByteBloom Morgan

The author has lived and breathed the life of a data steward for years, wrestling with data to keep organizations on track. Through countless hours of consulting—both giving and receiving advice—learned one thing: explaining and leading data governance is no easy feat.

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