#import example

Basic flowchart dataset example (visio data visualizer) If Visio Data Visualizer keeps rejecting your dataset, start with a basic flowchart example that is known to import. A basic flowchart dataset is the simplest version of the model: • Process Step ID • Process Step Description • Next Step ID • Connector Label (optional) • Shape Type No swimlanes. No phases. Just a clean graph. Why this matters: Most import failures come from small data issues that are hard to see in a big file: • duplicate IDs • missing referenced IDs • invalid shape type values • blank rows (including a blank line at end-of-file) • branching stored incorrectly A known-good basic dataset lets you validate the rules in minutes: 1. Import the example into Basic Flowchart (Data Visualizer) 2. Confirm it renders cleanly 3. Replace the step text with your own steps 4. Add or change connections by editing Next Step IDs 5. Re-import to regenerate Before import: remove blank lines and confirm headers match exactly. 3 practical tips that prevent headaches: • Connector Label can be left blank, but it is useful for decisions (Yes/No, Approved/Rejected) • treat Step IDs like primary keys – stable forever, never reused • if a step branches, store multiple next steps in 1 cell: 7,8 (comma-separated, no spaces) • keep Step Description consistent (verb + object + condition) so the process reads clearly When should a basic flowchart be used instead of a swimlane diagram? • early drafting and discovery • documenting a simple workflow with 1 owner • building the first “process as data” model before adding lanes • troubleshooting imports (basic flowchart isolates the core dataset rules) When to move to swimlanes (cross functional flowcharts): • multiple teams • lots of handoffs • ownership and approvals are part of the problem • lanes (Function) and phases (Phase) are needed to represent reality Once the process is a dataset, more becomes possible: • updates become row edits, not diagram surgery • audits become counts (handoffs, approvals, loops, rework) • AI analysis becomes reliable because it can read the dataset, not a screenshot • multiple stakeholder views become possible by reclassifying lanes and phases And if you need to convert an existing .vsdx diagram into a dataset without rebuilding by hand, a dataset generator can do the first pass quickly. Lite can validate the workflow on the first 20 steps. Standard removes the limit for full diagrams. A simple rule of thumb: If the diagram takes longer to update than the process takes to change, the diagram will never be trusted. A dataset-first workflow fixes that. #Visio #DataVisualizer #Flowchart #ProcessMapping #Excel #BusinessAnalysis #ContinuousImprovement process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work

Cross functional flowchart dataset example (visio data visualizer) Need a clean cross functional flowchart dataset example that actually imports? Most “templates” online fail because Visio Data Visualizer is strict: • headers must match exactly • Step IDs must be unique • every Next Step reference must point to an existing Step ID • branching is stored as comma-separated IDs (no spaces) • blank lines in a tab-separated values (TSV) file can break the import A practical way to stop guessing is to start from a known-good dataset, import once, then adapt the rows to your process. A workflow that holds up in real teams: 1. Start with a cross functional flowchart dataset that includes: Process Step ID Process Step Description Next Step ID Connector Label (optional) Shape Type Phase (stage / column) Function (swimlane / owner) 2. Import into Visio Cross-Functional Flowchart (Data Visualizer) If it renders cleanly, the model is valid. 3. Only then, edit in Excel: • rename lanes (Function) to match how the organization actually speaks • add phases (Phase) that reflect the timeline (intake, review, execute, close) • add steps and connections by editing Next Step IDs • standardize wording so steps read consistently (verb + object + condition) 4. Regenerate the diagram from the updated dataset That’s the round-trip that keeps swimlane diagrams current without redrawing. Two rules that prevent most errors: • treat Step IDs like primary keys – stable forever, never reused • when a decision branches, store multiple next steps in 1 cell: 12,13 A quick dataset hygiene checklist: • no trailing spaces in IDs or lane names • keep lane names controlled (Ops vs Operations vs Operations Team becomes chaos) • avoid “do everything” steps like “Handle request” • prefer short, specific step text that can be measured Common failure patterns when importing: • “Invalid value in column Shape Type” (shape names must be allowed values) • “The following IDs were referenced but not found” (missing Next Step targets) • “Duplicate Process Step ID” (2 rows share the same ID) These are data problems, not Visio problems. Fix the table and re-import. If the goal is to convert an existing .vsdx diagram into this format without rebuilding by hand, a dataset generator makes the first pass fast. Lite can validate the workflow on the first 20 steps. Standard removes the limit for full diagrams. Why this matters: Once the process is data, everything gets easier: • audits become counts (handoffs, approvals, loops) • updates become row edits, not diagram surgery • AI analysis becomes reliable because it can read the dataset, not a screenshot • multiple views become possible by reclassifying Function and Phase If swimlane diagrams are part of your job, a working dataset example is the fastest way to get traction. #Visio #DataVisualizer #SwimlaneDiagrams #CrossFunctionalFlowchart #ProcessMapping #Excel #BusinessAnalysis #ContinuousImprovement process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work