Data Maturity

The exercise of authority, control and shared decision-making over the management of data assets. Includes policies, standards, roles, responsibilities and accountability structures.

Planning, implementation and control of activities that apply quality management techniques to ensure data is fit for purpose. Covers profiling, cleansing, monitoring and remediation.

Defining the blueprint for managing data assets by aligning with organizational strategy. Includes conceptual, logical and physical data models, and the overall data architecture.

Processes related to the movement and consolidation of data within and between systems. Covers ETL/ELT, APIs, data pipelines, real-time integration and interoperability standards.

Ensuring data is protected from unauthorized access and that privacy regulations are met. Covers access control, encryption, classification, consent management and regulatory compliance.

Managing the core shared data entities (customers, products, locations) to ensure a single source of truth across the organization.

Enabling evidence-based decision-making through reporting, analytics, data science and AI/ML capabilities. Covers BI tooling, self-service analytics and advanced analytics.

Managing data about data - including business glossaries, data catalogs, data lineage, and technical metadata to enable understanding and trust in data assets.

Building organizational capability to read, work with, analyze and communicate with data. Covers training, change management, and fostering a data-driven culture.

The technical infrastructure, processes and practices that support reliable data delivery. Covers DataOps, platform engineering, environments, and operational excellence.