How the organisation's data models, schemas, integration standards, and storage placements conform to defined architecture standards and support interoperability.
ARC-01
Strategic technology and platform investment decisions
How well-governed are decisions to invest in, select, or retire major data platforms and technologies, from a strategic fit and scalability perspective?
Maturity level descriptions
No strategic oversight exists for data platform and technology decisions; selection happens at the discretion of individual teams with no enterprise view. Teams select and adopt data technologies independently, with no reference to enterprise architecture principles or strategic fit.
Some awareness exists that platform decisions should align to enterprise architecture, but there is no formal review process, and decisions remain largely siloed. Individual technology decisions occasionally reference enterprise architecture principles informally, without a required review step.
A defined review process exists for major data platform and technology decisions, assessing scalability, resilience, and strategic fit before commitment. Significant platform or technology decisions go through a documented review assessing scalability, resilience, and strategic alignment.
Platform and technology investments are tracked as a managed portfolio, with scalability and resilience metrics reviewed regularly against organisational growth plans. A technology investment portfolio is actively managed, with scalability/resilience metrics reviewed against forecast organisational growth on a defined cycle.
Platform investment strategy is proactively adapted ahead of anticipated organisational growth or technology shifts, with resilience and scalability benchmarked externally. Investment decisions anticipate future scale and technology shifts rather than reacting to capacity constraints, with resilience benchmarked against external standards or peers.
ARC-02
Enterprise data movement and integration risk oversight
How well does the organisation understand and manage the risk associated with how data moves across systems and pipelines at an enterprise level — including single points of failure and technical debt?
Maturity level descriptions
No enterprise-level visibility exists into how data moves across systems; risk from pipeline failures or technical debt is unknown at executive level. Data movement and integration risk is understood only, if at all, by individual technical teams, with no consolidated executive-level picture.
Awareness exists that integration risk and technical debt are a concern, but there is no consolidated inventory or assessment at enterprise level. Technical debt and integration risk are discussed informally when problems occur, without a consolidated enterprise inventory.
A defined inventory of critical data pipelines and integration points exists, with known risk areas (single points of failure, significant technical debt) documented. A documented inventory identifies critical pipelines and flags known risk areas, reviewed by a responsible technical or architecture function.
Data movement risk is tracked against a remediation plan, with technical debt reduction resourced and progress reported to the executive team. A remediation plan addressing identified risk areas is resourced and actively progressed, with status reported to the executive team on a defined cycle.
Data movement risk is continuously monitored with automated resilience testing, and technical debt reduction is a standing, funded element of the technology investment strategy. Automated resilience or failure testing continuously assesses critical data pipelines, and technical debt reduction is embedded as an ongoing, funded investment stream rather than a one-off remediation effort.