Main hub: Visio Data Visualizer.
Automation opportunity assessment – turn process maps into an automation lens
Automation fails when the target is chosen by opinion. A dataset-first process map makes automation assessment repeatable: classify steps, re-render the view, and pick candidates with evidence.
What an automation opportunity assessment is
An automation opportunity assessment is the discipline of identifying which process steps should be automated first and which should not. The goal is to build an automation pipeline that reduces cycle time and rework without locking in a broken process.
Key idea: if the process map is a maintained dataset, automation assessment becomes repeatable.
Automation terms: RPA stands for Robotic Process Automation. It is one automation approach, not the only one.
The automation lens (dataset-first)
Visio Data Visualizer renders a diagram from a strict dataset. A lens is a derived dataset that keeps the same Step IDs and connectors but changes the classifications that drive the diagram layout.
| Data Visualizer field | Set it to | What this reveals |
|---|---|---|
| Function (swimlanes) | High automation potential, Medium, Low | Where automation can safely start and where it should not. |
| Phase (columns) | Human work, System work, Approval | Whether the process is dominated by execution, systems friction, or governance friction. |
Need strict formatting rules and a clean starter file? Use the Data Visualizer template. If an import fails, use import troubleshooting.
High vs low automation potential (starter criteria)
This is a practical first-pass rubric. It is not a perfect model. It is good enough to prioritize.
| Tier | Typical characteristics |
|---|---|
| High | Rules-based, stable inputs, low exception rate, clear outputs, high frequency or high cost per transaction. |
| Medium | Some rules exist but exceptions are common, data quality is inconsistent, partial automation (assist) is realistic. |
| Low | Judgment-heavy steps, negotiation, investigation, unpredictable inputs, automation would lock in the wrong behavior. |
Swivel-chair work
Manual copy/paste between systems and tools. This is often high potential once inputs are standardized.
Approval overload
Approvals everywhere is rarely an automation problem. It is usually a criteria and decision-rights problem.
How to build the automation lens
- Create the canonical dataset. Stable Step IDs and correct Next Step IDs matter most.
- Render the baseline diagram. Confirm the dataset imports cleanly.
- Create a derived automation dataset. Copy the dataset and keep Step IDs and connectors unchanged.
- Classify each step. Set Function to High, Medium, or Low automation potential. Set Phase to Human work, System work, or Approval.
- Import and prioritize. Pick the first 3–5 automation candidates with the best risk-to-return profile.
Starting from an existing Visio diagram and the dataset is the bottleneck? The dataset generator converts a diagram into the Data Visualizer dataset format. Start with Lite, then move to Standard when the dataset needs to scale.
What to measure once it is in data
- Count approvals and review steps (governance friction)
- Count handoffs between teams (coordination cost)
- Count loops and rework returns (quality and criteria issues)
- Identify clusters of System work that indicate integration opportunities
Related lenses
FAQ
Is this only for RPA (Robotic Process Automation)?
No. The lens helps identify candidates for any automation approach, including workflow tools, integrations, and template-driven standardization. RPA is just one option.
Why not just automate the approvals?
Many approval chains exist because criteria are unclear. Automating approval routing often speeds up the wrong work. A better first move is to clarify thresholds and decision rights, then automate stable execution steps.
Is this affiliated with Microsoft Visio?
No. Visio and Visio Data Visualizer are Microsoft products. This site provides independent guidance and a dataset generator that supports a dataset-first workflow.
