How to Manage CAE Data Effectively: 4 Real-World Strategies

Every CAE engineer has been in this room. A colleague left six months ago and took the preprocessing logic with them. A new program kicks off. You export the solver results, rename the files, reformat the schema, align the variables, rebuild the validation script. Not because the work is new. Because the work that was done last time cannot be reused.

You have rebuilt the same thing three times across three programs, slightly differently each time, because each time you had to.

This is not a personal productivity problem. It is a systemic one, and the numbers back it up: research consistently shows that engineers and data professionals spend roughly 40 to 45 percent of their time on data preparation rather than on the analysis itself. NAFEMS has been documenting this pattern across the engineering simulation community for two decades. For CAE teams working across multiple solvers, physical test environments, and program cycles, the number is often higher.

The cost is not the data. The cost is the rebuild.

This article covers what CAE data management actually is, why it matters beyond the engineer’s workflow, the five manual steps most teams repeat every cycle, and the four strategies organisations use to fix it, with the honest trade-offs of each.

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What is CAE data management?

CAE data management is the discipline of turning raw Computer-Aided Engineering output. from solvers like Ansys, Siemens Star-CCM+, Hexagon, OpenFOAM, or any finite element or CFD tool, into a structured, queryable, reusable asset that the next engineer, the next program, or the next analysis can build on without starting over.

It includes everything between the moment a simulation finishes and the moment the data can be used: automated ingestion from the solver environment, standardisation of schemas, units, and variable names across runs, capture of metadata (mesh, boundary conditions, material models, solver version) as structured fields, storage in a queryable layer rather than as flat files on a shared drive, and reusable analysis workflows that do not have to be rebuilt every time.

CAE data management sits inside a broader category called engineering data infrastructure, the foundational layer that connects simulation, test, and manufacturing data so engineering teams can build on it rather than around it.

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Why CAE data management matters

The business case for investing in CAE data management comes down to three compounding outcomes. Each one is hard to measure in isolation. Together they are the difference between an engineering organisation that scales and one that resets every program.

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1. Preparation work collapses

The five manual steps that eat 40-plus percent of engineering time (covered in the next section) disappear as automated, enforced processes. The next DoE study starts from a clean, structured dataset on day one, not from raw solver output that has to be wrestled into shape for three days first. Teams that implement CAE data management well typically see DoE turnaround drop by 50–70 percent.

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2. Logic stays written

The KPI extraction, validation rule, or analysis dashboard an engineer builds once becomes a reusable workflow that runs automatically on every new dataset. The next engineer does not rebuild. They build on top. This is how one good analysis becomes a reusable capability rather than a one-off success that gets celebrated and then forgotten.

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3. Knowledge stops leaving

When workflows are structured, the institutional knowledge they represent survives team changes. An engineer moves programs, retires, or takes a role elsewhere, and the pipeline they built continues to run. The accumulated engineering intelligence of your organisation stops resetting every two years.

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The bottom line: CAE data management converts engineering hours from manual rework into design iteration, cuts program-level turnaround, and turns knowledge from a personal asset into an institutional one.

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The 5 manual steps CAE engineers repeat every cycle

If you have worked on more than one CAE program, you have done these five steps by hand, probably dozens of times each.

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Step 1 - Export and reformat solver output

Every solver writes in its own proprietary format. You convert, rename, and often flatten the structure before the data can be read by anything downstream. Half an hour to an hour per run.

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Step 2 - Align variable names and units across runs

The convention the last engineer used is not quite the convention you are using. You spend an hour reconciling before you can compare runs or pool results across programs.

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Step 3 - Reconcile metadata by hand

Which mesh, which boundary conditions, which material model, which turbulence setting. Often buried in a notes file, a file name, or a folder hierarchy. You capture it in a spreadsheet for the dozenth time.

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Step 4 - Load and pre-process into the analysis environment

Your Python, MATLAB, or notebook environment needs the data in yet another specific shape. You write or rebuild a preprocessing script to get there. For the program that started six months ago, this alone is often a full day.

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Step 5 - Validate and cross-reference

You check the results against the previous run. You find something broken. You figure out which step introduced it. You fix it. Repeat until the data is trustworthy enough to use.

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Collectively, this is three to six hours per design variant for most CAE teams. Multiply by variants per DoE, by DoEs per program, by programs per year, by engineers on the team - and this is where the 40 percent of engineering time goes.

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Sound familiar?

You don’t have to stay stuck in these five steps. See how FEV cut DoE turnaround by 70% by automating exactly this layer.

read related case study

see how much you loose

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The 4 strategies for managing CAE data

There are four real-world strategies organisations use to manage CAE data. Each one works for a specific context. Here are the honest trade-offs of each.

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Strategy 1 - Shared drives and spreadsheets

The default. Files go into a shared folder structure. Metadata lives in a spreadsheet everyone is supposed to update. Conventions are maintained by engineer discipline.

Pros

• Zero cost. No tooling, no setup.
• Fine for small teams running a single program.

Cons

• Conventions fragment the moment team composition changes. By program three, discipline is gone.
• No enforced schema, no queryability, every comparison is a manual file search.
• Metadata lives in spreadsheets that drift out of sync with reality.
• No lineage tracking. When models break, you cannot reconstruct what changed.
• Does not scale past 5–10 engineers or beyond one active program.

Verdict: Works for small teams, fails at scale. The most common starting point and the most common reason teams eventually need something better.

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Strategy 2 -In-house scripted automation

A senior engineer or small internal team builds custom Python scripts, Bash pipelines, or internal web tools to automate exports, file handling, and preprocessing. Often spun up when a team outgrows shared drives but cannot justify a platform purchase.

Pros

• Tailored exactly to how your team works.
• Reasonable cost for the first year (mostly engineer time).
• No vendor dependency; full control of the codebase.
• Can solve 60–70% of the manual step problem for one team.

Cons

• Knowledge concentrates in one or two people. When they leave, the pipeline often dies with them.
• Maintenance burden grows, every solver update, every new program adds rework.
• No standardisation across teams: CFD builds its own, FEA builds its own, nothing connects.
• Rarely extends to metadata management, lineage, or cross-team queryability.
• The engineer building the automation is not doing engineering. Opportunity cost is real.

Verdict: Works for a single team with a strong technical lead. Rarely survives their departure. The internal automation layer is often rebuilt from scratch every 3–5 years.

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Strategy 3 - Traditional PDM/PLM extension

Extend an existing Product Lifecycle Management (PLM) or Product Data Management (PDM) system, Teamcenter, Windchill, 3DEXPERIENCE, Aras, to also manage CAE data. Usually pitched by the PLM vendor as a natural expansion of what the organisation already owns.

Pros

• Leverages infrastructure and licensing you already have.
• Strong lineage, governance, and access control.
• Good fit when simulation data is tightly coupled to design data (same PLM).
• Enterprise-grade audit trails for regulated industries.

Cons

• PLM systems were built for CAD data, not simulation data, they struggle with multi-gigabyte solver results, proprietary formats, and mesh-dependent variables.
• Deployments often take 12–18 months and regularly run over budget.
• User experience for CAE engineers is poor, engineers avoid the system, which defeats the purpose.
• Rigid data models; hard to adapt to solver-specific metadata.
• High cost per seat, high integration cost, high ongoing admin cost.

Verdict: Works best in organisations where PLM is already deeply embedded and simulation data volume is moderate. Often painful, expensive, and slow to adopt in simulation-heavy engineering teams.

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Strategy 4 -Purpose-built engineering data infrastructure (Key Ward)

The most recent category. Purpose-built platforms designed specifically for engineering data, simulation, test, and manufacturing. Rather than forcing CAE data into a system built for CAD, or asking engineers to build infrastructure themselves, they provide the infrastructure layer underneath the tools engineers already use.

Key Ward connects directly to solver environments (Ansys, Siemens, Hexagon, OpenFOAM), standardises schemas, units, and metadata on ingest, stores results in a structured queryable layer, and keeps reusable workflows as shared assets. The engineer’s existing tools stay where they are. The layer underneath makes them work together.

Pros

• Engineering-native: built for simulation data volumes, formats, and metadata from the start.
• Connects to your existing solvers without changing how engineers work day to day.
• Faster to deploy than PLM extensions (weeks rather than months).
• Workflows and logic are reusable across programs and team members.
• Queryable data means downstream AI, surrogate models, and dashboards work on structured inputs.
• Knowledge survives team changes.
• Teams typically see measurable ROI within one program cycle.

Cons

• A newer category than shared drives or PLM, fewer case studies publicly available.
• Requires initial setup to connect solver environments and define canonical schemas.
• Not the right fit for teams of 1–3 engineers working on a single program, shared drives are probably enough.
• A paid platform, though typically significantly less than enterprise PLM extensions.

Verdict: The strongest option for CAE teams of 10+ engineers running multiple programs or needing AI/ML on simulation data. The category where most competitive engineering organisations are now investing.

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See Key Ward in action

Two ways to evaluate whether this is the right strategy for your team. Most CAE leaders want one of two things: see the platform work on their own simulation data, or read how another team made it work.

book a demo

read related case study

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Which strategy is right for your team?

The short version:

• Shared drives if you have a small team, one program, and no AI ambitions.
• In-house scripting if you have one strong technical lead and can accept the risk they leave.
• PLM extension if you already have PLM deeply embedded and simulation data volume is moderate.
• Purpose-built engineering data infrastructure if you run multiple programs, have 10+ engineers, or want AI and surrogate modelling to actually work on your simulation data.

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What is Key Ward?

Key Ward is an engineering data infrastructure platform used by teams at automotive, aerospace, and industrial manufacturers to manage CAE, test, and manufacturing data as a single structured layer. The platform connects directly to the solvers and test environments engineers already use, Ansys, Siemens Star-CCM+, Hexagon, OpenFOAM, DAQ systems, and standardises their outputs into queryable, reusable datasets. On top of that layer, teams run their existing analysis workflows, build surrogate models, and deploy engineering AI that actually reaches production.

Example of what this looks like in practice: a global engineering services provider working on hydrogen combustion CFD used Key Ward to cut DoE turnaround by 70%, not by speeding up the solver, but by automating the data preparation cycle that used to eat weeks per program.

Key Ward sits alongside your existing tools. It does not replace your solvers, your test environment, or your PLM. It is the layer underneath that makes them work together. For more on the broader concept, see what is engineering data infrastructure, or our deep dive on why 95% of engineering AI pilots fail.