Introduction

TODO: 2-3 paragraph introduction explaining what the Data Maturity Model is and who should use it. Cover the problem it solves — most organizations don't have a clear picture of where they stand on data capability, making it impossible to prioritize investment or set realistic expectations for AI readiness. The model provides a shared language for data maturity across technical and business stakeholders.

TODO: Second paragraph covering the model's five dimensions: Data Collection, Data Storage, Data Governance, Data Quality, and Data Analytics. Explain how each dimension is scored independently, and how the composite score determines the organization's overall maturity level.

TODO: Third paragraph on practical application — use the model in a workshop with data and business leaders, score each dimension collaboratively, then use the results to create a prioritized data improvement roadmap. This model is typically completed in a 2-day facilitated session.

Dimension 1: Data Collection

TODO: Overview of the Data Collection dimension — how systematically the organization captures data from internal systems, external sources, and operational processes.

Inventory Data Sources

TODO: Guide the team through inventorying all active data sources — transactional systems, event streams, third-party data feeds, manual data entry processes. Score each source on: completeness (does it capture everything needed?), frequency (real-time vs. batch vs. ad hoc?), and reliability (does it consistently deliver data?).

Assess Collection Gaps

TODO: Identify data that the organization needs but doesn't currently collect. Map gaps to specific business decisions that are made without data today. Prioritize gap-filling by business impact — which missing data would most change the quality of decisions?

Dimension 2: Data Storage and Architecture

TODO: Overview of the Data Storage dimension — how data is stored, organized, and made accessible across the organization.

TODO: Single authoritative data store or well-integrated set of stores — no hidden shadow databases
TODO: Data organized in layers (raw, curated, analytical) with clear ownership of each layer
TODO: Storage solution appropriate for data volume, query patterns, and budget constraints
TODO: Backup and disaster recovery tested and documented
TODO: Data retention policies defined and enforced by the storage layer, not manual process
TODO: Access patterns optimized — hot data in fast storage, cold data archived cost-effectively

Dimension 3: Data Governance

TODO: Overview of the Data Governance dimension — the policies, roles, and processes that ensure data is used appropriately and reliably across the organization.

Define Data Ownership

TODO: Every dataset needs an owner responsible for quality, access decisions, and lifecycle management. Walk through how to assign data ownership, what the owner's responsibilities are, and how to escalate data quality issues when no clear owner exists.

Establish Access Controls

TODO: Data access should be role-based and audited. Cover how to implement principle of least privilege for data access, how to track who accessed what data and when, and how to handle access requests from new teams or for new use cases.

Document Data Lineage

TODO: Every analytical dataset should have traceable lineage back to source systems. Explain why lineage matters — when data quality issues arise, lineage tells you where the problem originated. Cover available tools for lineage tracking from simple metadata documentation to automated lineage capture.

Dimension 4: Data Quality

TODO: Overview of the Data Quality dimension — the reliability, accuracy, and consistency of data across the organization.

TODO: Data quality dimensions defined for each critical dataset: completeness, accuracy, timeliness, consistency, uniqueness
TODO: Automated quality checks run at ingestion — invalid records quarantined, not silently dropped
TODO: Quality metrics tracked over time — dashboards showing trends, not just point-in-time snapshots
TODO: SLAs for data freshness defined and monitored — downstream teams know when data will be ready
TODO: Data quality issues escalated to data owners, not buried in engineering backlogs
TODO: Root cause analysis process for quality incidents — fixes address causes, not symptoms

Dimension 5: Data Analytics Capability

TODO: Overview of the Data Analytics dimension — the organization's ability to turn data into insights and decisions.

Assess Self-Service Analytics

TODO: How many business users can answer data questions without engineering support? Cover the spectrum from purely engineering-dependent analytics to fully self-service BI. Assess the tools available, user training, and data model quality that enables or blocks self-service.

Evaluate Advanced Analytics Readiness

TODO: Is the organization ready to move beyond descriptive analytics (what happened?) to predictive (what will happen?) and prescriptive (what should we do?) analytics? Cover the prerequisites: clean historical data, analytical talent, and use cases where statistical models can improve decisions.

Maturity Levels

Level 1Reactive

TODO: Data is captured ad hoc in response to specific requests. No centralized storage — data lives in spreadsheets, email attachments, and departmental databases. Reporting is manual and time-consuming. Data quality is unknown and inconsistent.

Level 2Repeatable

TODO: Core transactional data is captured reliably. Basic data warehouse or database in place for reporting. Standard reports produced on a regular schedule. Quality issues are known but addressed reactively rather than systematically.

Level 3Defined

TODO: Formal data architecture with documented pipelines from source to analytical layer. Data governance roles established. Quality monitoring automated for key datasets. Business users can access data self-service through BI tools.

Level 4Managed

TODO: Data treated as a strategic asset with formal ownership, SLAs, and quality commitments. Proactive quality monitoring across all critical datasets. Advanced analytics in use for key business decisions. ML models in production for at least one domain.

Level 5Optimizing

TODO: Data platform is a competitive advantage. Real-time data feeds operational decisions. ML models continuously retrained and improved. Data products managed with product management rigor — roadmaps, user research, OKRs. Organization contributes data quality learnings back to the broader ecosystem.

Using Your Scores

TODO: Guidance on interpreting composite maturity scores. Cover how to weight dimensions by business context — a retail organization may prioritize data collection and analytics over governance, while a financial services firm may weight governance and quality most heavily. Explain how to translate scores into a prioritized improvement roadmap with clear milestones.

Data Maturity Model