2. Data governance frameworks for CAE

CAE simulation data is not a by-product of engineering work; it is the evidence base on which design decisions, certification claims, and product liability defenses rest. A governance framework is the set of explicit decisions about who may create, change, approve, and retire that evidence, and under what conditions. Without such a framework, simulation data degrades into a shared drive of inconsistently named solver decks, orphaned result files, and models whose provenance cannot be reconstructed six months later. This chapter describes how engineering organizations structure that framework: the roles that carry accountability, the policies that constrain behavior, the standards that make data machine-interpretable, and the compliance obligations that connect CAE artifacts to regulatory and contractual requirements.

Why CAE Needs Its Own Governance Model

Generic enterprise data governance, typically built around master data management for customers, materials, or parts, does not transfer cleanly to simulation. CAE data has three properties that demand specialized treatment. First, it is derived and versioned: a result file is meaningless without the exact solver input, solver version, and boundary conditions that produced it, so lineage is a first-class requirement rather than a nice-to-have. Second, it is large and heterogeneous: a single nonlinear explicit run may produce tens of gigabytes across input decks, restart files, and post-processing databases, each with different retention economics. Third, it is decision-bearing: a fatigue life prediction or crash pulse directly supports a design release, which means the data falls under the same audit scrutiny as the drawing it justifies.

A workable framework therefore separates four concerns that are often conflated: accountability (who owns the data and its quality), rules (what must be true before data is trusted), conventions (how data is named, structured, and linked), and obligations (what external parties require). The diagram below shows how these concerns interlock and where each one acts on the simulation lifecycle.

Roles: Assigning Accountability Without Bottlenecks

The most common failure in CAE governance is assigning all responsibility to a single "simulation data manager," who then becomes a bottleneck and, in practice, a filing clerk. Effective frameworks distribute accountability along three axes. The data owner is the engineering function lead (for example, the crashworthiness or thermal manager) who is accountable for the correctness and completeness of a simulation domain's data and signs off on its release. The simulation engineer is the creator: responsible for producing runs that conform to standards, attaching complete metadata, and flagging assumptions. The reviewer/approver is an independent qualified engineer who verifies that the model, boundary conditions, and results support the stated conclusion; independence here is a policy requirement, not a courtesy. The data steward operates the platform: manages the schema, monitors metadata completeness, runs quality reports, and escalates nonconformance. Finally, a governance board with representation from engineering, IT, quality, and legal owns the policy set itself and adjudicates exceptions.

A practical rule that prevents role confusion: accountability follows the decision, not the file. The engineer who signs the release memo owns the data package; the steward owns the platform's ability to prove it. When a design decision is later challenged, the framework must answer "who approved this, on what evidence, and was the evidence produced under the then-current standard?" — which is why role definitions must be written into the workflow tooling, not just an org chart.

Policies: Rules That Are Enforceable

Policies are only meaningful if they can be checked. A statement such as "all simulation data must be properly documented" is unenforceable; "every result record must carry a linked input deck, solver name and version, unit system, and approver identity before it can transition to Released state" is enforceable by a workflow gate. Good CAE policies typically cover five areas: metadata minimums (what must be captured at creation), state transitions (draft → reviewed → approved → released → superseded → archived, with who may trigger each), access classification (public, internal, restricted, export-controlled), retention and legal hold (how long each artifact class is kept and what suspends deletion), and exception handling (how a team may deviate, for how long, and with whose approval).

Standards: Making Data Machine-Interpretable

Standards are the technical layer that lets policies be automated. For CAE, the core standard is the data model: a defined schema for simulation entities and their relationships. A minimal but workable schema links an Analysis (the question being answered) to one or more Runs (solver executions), each Run to its InputSet (geometry revision, mesh, material cards, boundary conditions, solver deck), and each Run to its Results and Reports. Every entity carries a stable identifier, a revision, an owner, and a status.

Alongside the schema, organizations standardize naming and units. A naming convention that encodes program, subsystem, load case, and revision — for example PRJ-A_BIW_FRONTAL-40MPH_v03 — makes records findable and comparable without opening them. Unit conventions matter equally: mixed mm-tonne-s and m-kg-s decks in the same program have caused more than one silent comparison error. Result extraction standards (which nodes, which invariants, which averaging method) ensure that two engineers comparing peak intrusion are measuring the same quantity.

Compliance: Connecting CAE Evidence to Obligations

Compliance requirements enter CAE governance from outside engineering: regulatory safety cases, customer contract clauses that specify deliverable formats and review rights, export-control regimes that restrict who may access certain geometry or solver capabilities, and internal quality-system requirements for record retention. The governance framework's job is to translate each obligation into a concrete control. A contract requiring ten-year retention of certification evidence becomes a retention class applied to the relevant Analysis records, with deletion blocked and an audit query that lists all released analyses for the program. An export-control restriction becomes an access classification that gates both the geometry and any derived results.

Audit readiness is the practical test. A well-governed organization can answer, within minutes, questions such as: which analyses supported the last design release, who approved them, which solver version was used, and were any exceptions granted? If answering requires email archaeology, the framework exists on paper only.

A Worked Example: Enforcing Metadata at the Release Gate

Consider a team implementing the metadata minimum as a workflow gate in their simulation data management platform. The gate is a validation rule evaluated when an engineer attempts the transition from Reviewed to Released. A representative rule set, expressed as pseudo-configuration, might look like this:

gate: release_readiness
applies_to: Analysis.status == "Reviewed"
require:
  - linked_input_set.geometry_revision is not null
  - linked_input_set.solver_name in ["Abaqus", "Nastran", "LS-DYNA"]
  - linked_input_set.solver_version matches /^\d{4}(\.\d+)?$/
  - linked_input_set.unit_system in ["mm-tonne-s", "m-kg-s"]
  - results.extraction_template_id is not null
  - approver.role == "reviewer" and approver.id != creator.id
  - access_classification is not null
on_fail: block transition; return list of missing fields to creator
on_pass: stamp release record with timestamp, approver id, and standard revision

To validate the gate, the team runs a controlled test: create a draft analysis, attach an input set missing the unit system, and attempt release. Expected behavior is a blocked transition with the missing field named. A second test attaches a complete input set but sets the approver equal to the creator; the gate must reject it on the independence condition. A third test passes all conditions and should produce a release record whose stamped standard revision matches the currently active policy version. Typical failure symptoms during rollout are instructive: engineers bypassing the gate by uploading results directly to a shared drive (indicating the platform is not the path of least resistance), or gates passing records with placeholder values such as solver_version = "unknown" (indicating the validation is too permissive and needs an explicit allow-list). Both symptoms point to design fixes, not discipline problems.

Keeping the Framework Alive

Governance frameworks decay when they are treated as a one-time documentation exercise. The feedback loop shown in the diagram is the maintenance mechanism: periodic audits sample released analyses, check metadata completeness and approver independence, and feed findings back into policy revision. A quarterly review that examines exception requests is particularly valuable, because a rising exception rate in one area usually signals that a policy is misaligned with real engineering practice and should be revised rather than enforced harder. Standards likewise need versioning: when the result extraction template changes, the release record's stamped standard revision preserves the ability to interpret older evidence correctly. Treated this way, the framework becomes a lightweight, self-correcting system that makes simulation evidence defensible without slowing the engineering work it supports.