#process analysis

Process Analysis: Marimo Care December 2017 ig

Swimlane diagram vs cross functional flowchart: what’s the difference? “Swimlane diagram” vs “cross functional flowchart” sounds like a difference. Most of the time it is not. In day-to-day process work, these phrases are usually describing the same deliverable: A flowchart where steps are arranged in lanes to show who does what and where handoffs occur. So why do both terms exist? Because different communities use different labels: • “Swimlane diagram” is common in general business and project work • “Cross functional flowchart” is common in operations, quality, and Lean documentation • “Deployment flowchart” shows up in Six Sigma language Here is the practical difference that matters: A basic flowchart answers: “What happens next?” A swimlane or cross functional flowchart answers: “Who owns each step, and where does work cross boundaries?” That “crossing” is the point. Handoffs create delay, rework, and approvals. Which should be used? Use the term your audience searches and recognizes. If the team is asking for accountability, use “swimlane diagram”. If the team is asking for a standard process artifact, use “cross functional flowchart”. On a website, the winning approach is to treat them as related terms and cover both intents in the same topic cluster. A simple decision guide: Use a swimlane diagram / cross functional flowchart when: • there are 2+ roles or departments involved • delays are suspected at handoffs • approvals and queues need to be made visible • the goal is governance, auditability, or automation planning Use a basic flowchart when: • ownership is not the question • it is a single-person or single-system procedure • the goal is training on the sequence only Now the leverage: If swimlanes are drawn, every change becomes redraw work. If swimlanes are data, the diagram becomes maintainable. In Visio Data Visualizer, lanes come from the Function column in the dataset. Change Function in Excel, re-import, and the swimlane view updates. That means 1 process model can generate: • department view • role view • system touchpoint view • value stream lens view (VA/BVA/NVA) Same steps. Same connections. Different lens. Common mistakes when teams request these diagrams: • mixing departments and systems in the same lane scheme • creating 25+ lanes (nobody reads it) • leaving lane names inconsistent (Ops vs Operations) Quick test: Convert 20 steps into a dataset, import once, then reassign 1 step to a different Function and refresh. If the view changes cleanly, the swimlane diagram is now a living asset instead of shelfware. #Visio #ProcessMapping #SwimlaneDiagram #CrossFunctionalFlowchart #BusinessAnalysis #Operations Best starting point: use a Data Visualizer-ready template and a small example dataset, then scale only after the round-trip import is reliable. process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work

Swimlanes as data: the function field (post 1) Most “swimlane diagrams” fail for 1 simple reason. The swimlanes are trapped inside the drawing. That means every organizational change, every role tweak, and every “can you show it by system instead?” request turns into diagram surgery: moving boxes, nudging connectors, cleaning up formatting, exporting again, repeating. Visio Data Visualizer works differently. In Data Visualizer, swimlanes are not “drawn”. They are assigned. The lane label comes from 1 column in the dataset: Function = the swimlane owner for that step. That is the unlock. Once Function is data, the swimlanes become a controlled field that can be changed in Excel and re-rendered in Visio on demand. A practical way to use it: 1. Keep Step IDs stable 2. Keep Next Step IDs stable (those define the arrows) 3. Use Function for the lane assignment 4. Optionally use Phase for columns (stage, lifecycle, etc.) Then the workflow becomes: • Update ownership = change 1 cell in Function • Re-org = remap Function values with a simple lookup table • New “view” = create a copy of the dataset and reclassify Function for the lens Common choices for Function: • Department (Sales, Finance, Operations) • Role (Analyst, Manager, Reviewer) • System (SAP, ServiceNow, Email, Spreadsheet) • Lens categories (Value-Added, Business-Value-Added, Non-Value-Added) The 1 rule that keeps it sane: Each step should have 1 primary lane owner. If multiple owners exist, capture them in a separate column (Secondary Owner) or split the step. Otherwise the swimlane view becomes ambiguous and handoff counts become unreliable. Common Function mistakes that create bad diagrams: • Same lane written 3 ways (Ops, Operations, Operations Team) • Trailing spaces that create “phantom lanes” • Over-granularity (40 lanes that no one can read) • Under-granularity (1 lane called “Team” that hides handoffs) A simple quality checklist: • 1 canonical name per lane • No blanks in Function • Lane names reflect the decision the diagram must support • The dataset imports cleanly every time (no surprises) Why this matters commercially: When swimlanes are data, Excel can quantify the map: • handoffs (lane changes) • approvals (tagged steps) • rework loops (back edges) • where work clusters by owner And that is what leaders actually want from “a swimlane diagram”. Not a picture. Insight and decision support. Quick test: Convert 20 steps, import once, then change 1 Function value and refresh. If that round-trip works, the process map just became maintainable. #Visio #SwimlaneDiagram #ProcessMapping #BusinessAnalysis #DataVisualizer #Operations Next move: normalize the Function field first, then scale conversion. That prevents drift and keeps every future view fast to produce. process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work

Swimlanes as data: the function field (post 1) Most “swimlane diagrams” fail for 1 simple reason. The swimlanes are trapped inside the drawing. That means every organizational change, every role tweak, and every “can you show it by system instead?” request turns into diagram surgery: moving boxes, nudging connectors, cleaning up formatting, exporting again, repeating. Visio Data Visualizer works differently. In Data Visualizer, swimlanes are not “drawn”. They are assigned. The lane label comes from 1 column in the dataset: Function = the swimlane owner for that step. That is the unlock. Once Function is data, the swimlanes become a controlled field that can be changed in Excel and re-rendered in Visio on demand. A practical way to use it: 1. Keep Step IDs stable 2. Keep Next Step IDs stable (those define the arrows) 3. Use Function for the lane assignment 4. Optionally use Phase for columns (stage, lifecycle, etc.) Then the workflow becomes: • Update ownership = change 1 cell in Function • Re-org = remap Function values with a simple lookup table • New “view” = create a copy of the dataset and reclassify Function for the lens Common choices for Function: • Department (Sales, Finance, Operations) • Role (Analyst, Manager, Reviewer) • System (SAP, ServiceNow, Email, Spreadsheet) • Lens categories (Value-Added, Business-Value-Added, Non-Value-Added) The 1 rule that keeps it sane: Each step should have 1 primary lane owner. If multiple owners exist, capture them in a separate column (Secondary Owner) or split the step. Otherwise the swimlane view becomes ambiguous and handoff counts become unreliable. Common Function mistakes that create bad diagrams: • Same lane written 3 ways (Ops, Operations, Operations Team) • Trailing spaces that create “phantom lanes” • Over-granularity (40 lanes that no one can read) • Under-granularity (1 lane called “Team” that hides handoffs) A simple quality checklist: • 1 canonical name per lane • No blanks in Function • Lane names reflect the decision the diagram must support • The dataset imports cleanly every time (no surprises) Why this matters commercially: When swimlanes are data, Excel can quantify the map: • handoffs (lane changes) • approvals (tagged steps) • rework loops (back edges) • where work clusters by owner And that is what leaders actually want from “a swimlane diagram”. Not a picture. Insight and decision support. Quick test: Convert 20 steps, import once, then change 1 Function value and refresh. If that round-trip works, the process map just became maintainable. #Visio #SwimlaneDiagram #ProcessMapping #BusinessAnalysis #DataVisualizer #Operations Next move: normalize the Function field first, then scale conversion. That prevents drift and keeps every future view fast to produce. process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work

Visio diagram to excel: what people mean… “Convert a Visio diagram to Excel” is one of those searches that hides 5 different problems. People type the same phrase, but they want very different outcomes. Here are the most common meanings behind that query: 1. “Get the text out of the boxes” They want a list of steps. 2. “Get the connections out” They want a table that shows which step leads to which step. 3. “Get swimlanes into rows” They want owners (roles, departments, systems) tied to each step. 4. “Create a dataset that can regenerate the diagram” They want Visio Data Visualizer-ready data (Step IDs + Next Step IDs). 5. “Make the process analyzable” They want counts: handoffs, approvals, loops, rework, waiting. Excel can do parts of this, but the missing piece is usually structure. A diagram export is not the same as a process dataset. A dataset has: • Stable Step IDs • Next Step IDs that define the flow (branching is multiple IDs in one cell) • Shape Type (Start, Process, Decision, End) • Function (lane owner) • Optional Phase (stage) That is enough to regenerate the diagram and analyze the process. So what should happen after “Visio to Excel”? If the goal is only a step list: a simple extraction may be fine, but it will not support flow analysis. If the goal is “keep the diagram current”: the data has to be importable back into Visio. Visio Data Visualizer expects TSV (tab-separated values) with strict rules (no blank rows, correct headers). If the goal is “process improvement”: structure the data so Excel can answer questions: • Handoffs = Function changes across connected steps • Rework loops = Next Step IDs that point backward • Approval load = tag approvals and pivot by owner • Delay = classify steps as Active vs Waiting This is also the moment AI (artificial intelligence) becomes useful. Tables produce better summaries and checks than screenshots. A practical way to start without boiling the ocean: convert 20 steps, import successfully, then change 1 row and refresh the diagram. That round-trip proves the process is now a maintained model, not a picture. If you’re searching “Visio to Excel”, the best first question is: Do you want a spreadsheet of shapes… or a dataset that behaves like a model? Because only the second one stays current without redraw work. Quick translation guide: • “Visio to Excel” = extract steps + owners • “Visio to CSV” = same idea, different file format • “Export Visio diagram” = usually shapes, not relationships • “Data Visualizer import” = the process as a real dataset (model) • “Analyze process map” = counts, pivots, and lens views If rankings are the goal, the winning pages are the ones that answer the intent behind the phrase and show the next step clearly: template, example dataset, then a lightweight way to convert a real diagram. #Visio #Excel #ProcessMapping #BusinessAnalysis #DataVisualizer process improvement, process mapping, operations, business analysis, workflow, visio, swimlane, automation, lean, standard work

One process model, many views (pt. 3 of 3) The most expensive process mapping problem is not “drawing”. It’s redraw. Every time a leader asks for a new view: • “Show it by department.” • “Now show it by system.” • “Now show it by risk.” • “Now show it as a value stream map.” …teams create another diagram, and drift multiplies. The alternative is the same operating model used by serious architecture and process tools: Model = the dataset (the process as data). View = a diagram generated from that dataset. Lens = a controlled reclassification that produces a new view without changing the underlying process. This is “one process model, many views”. What has to stay stable: • Step IDs (unique, durable) • Next Step IDs (connections) • Step descriptions (can change, but IDs stay) Everything else is metadata that can be reclassified for different audiences. A practical approach that works with lightweight tooling: 1. Create the canonical dataset (the system of record) One row per step, plus connections. 2. Render the baseline view in Visio Data Visualizer Swimlanes and phases show the operational story. 3. Create lens datasets (copies or derived tables) The steps and arrows stay the same. Only Function and Phase (and optional lens columns) change. Examples of high-value lenses: • Value stream lens: Function = VA/BVA/NVA, Phase = Active/Waiting/Rework • Governance lens (RACI – Responsible, Accountable, Consulted, Informed): Function = Accountable role • Systems lens: Function = system of record used at each step • Automation lens: Function = automation potential (high/medium/low) • Risk lens: Function = risk category, Phase = control type (preventive/detective/corrective) Why this works in the real world: • Updates become row edits, not diagram surgery • Variants stop drifting because they share the same IDs and connections • Reviews get easier because the dataset can be versioned and diffed • Excel can quantify the map (handoffs, approvals, loops, counts by lens) • AI (artificial intelligence) tools become usable because they can read structured tables instead of screenshots Fast validation move: Convert 20 steps, import successfully, then change 1 lane assignment in the dataset and refresh. That round-trip is the moment the process stops being a picture and becomes a maintained model. If only one thing gets standardized, standardize Step IDs. That small discipline is what makes “many views” possible without chaos. Once the model exists, the work shifts from “draw and defend” to “measure and improve”. It also becomes easier to onboard new teams because the dataset is teachable. That speeds adoption. #ProcessMapping #Visio #DataVisualizer #BusinessAnalysis #Operations #ContinuousImprovement process mapping, raci, automation, risk and control, operations, business analysis, workflow, systems integration, decision rights

Turn a process diagram into process data (pt. 1 of 3) Every process diagram already contains a dataset. It is just trapped inside the picture. That is why so many process maps fail: the map is “done”, but nobody can maintain it without reopening Visio and doing diagram surgery. If you can identify 3 things, you can turn a diagram into process data: 1. The steps (boxes) 2. The connections (arrows) 3. The owners and stages (lanes and phases) That is it. Once the process is a table, everything changes: updates become lightweight, analysis becomes spreadsheet-native, and Visio becomes a renderer instead of a drawing tool. Here is the minimum structure that works with Visio Data Visualizer: • Process Step ID (unique, stable) • Process Step Description (short, action-oriented) • Next Step ID (the Step ID(s) that follow) • Connector Label (optional) • Shape Type (Start, Process, Decision, End) • Phase (stage, optional) • Function (lane owner) A few rules matter more than people expect: • Step IDs must be stable. Do not renumber the process every edit. • Every Next Step ID must exist somewhere in the table. • Branching is represented by multiple Next Step IDs in 1 cell, comma-separated, no spaces. • No blank rows in the TSV file (tab-separated values) or the import can fail. • Standardize lane names early (Ops vs Operations vs Ops Team becomes 3 lanes). Why this matters commercially and operationally: If the process is data, the organization can finally answer questions like: • how many handoffs exist? • how many approvals exist? • where are the loops and rework paths? • what steps are waiting vs active? • what could be automated safely? And those answers stop being “opinions”. They become counts, pivots, and filters. A simple 20-step proof method: 1. Convert 20 steps from an existing diagram into rows. 2. Import into the cross-functional Data Visualizer template. 3. Fix formatting issues until it imports cleanly. 4. Change 1 row (move a step to a different lane) and re-import. If the diagram updates correctly, the process is now maintainable. This is Part 1 of a 3-part series: Part 1 – turn a diagram into data. Part 2 – apply the value stream lens (VA/BVA/NVA and Active/Waiting/Rework). Part 3 – generate many views from 1 model without redraw work. If only 1 thing happens this week: prove the round-trip. Import from data, make a change in the table, refresh the diagram. That is the moment a static map becomes a maintained model. Once that works, “1 process, many views” stops being a slogan. It becomes a repeatable workflow. Most teams never go back to redraw-based mapping after they see it. visio, swimlane, process mapping, operations, business analysis, workflow, data visualizer, automation, risk

Diagram to data-driven insights in visio Most “process improvement” efforts start with a diagram. And then they stall for the same reason: a diagram is a picture, not a model. A picture can look correct and still be useless for decision-making. Here is a fast test: If a leader asked these questions, could your process map answer them without a workshop? • How many handoffs exist end-to-end? • How many approvals exist? • How many rework loops exist? • Where does work wait the longest? • What % of steps are value-added vs waste? If the answer is “not sure”, the problem is not effort. The problem is the medium. The unlock is simple: Model = the dataset (table). View = the Visio diagram generated from that dataset. When a process exists as data, “analysis” becomes normal: • Excel can count, filter, and pivot. • Visio can regenerate diagrams consistently. • Artificial intelligence tools can work from structured input instead of screenshots. And the best part: 1 process model can produce many views without redraw work. Operational swimlanes: Function = who does the work. Value stream lens: Function = VA/BVA/NVA and Phase = Active/Waiting/Rework. Governance lens: Function = Accountable role (RACI – Responsible, Accountable, Consulted, Informed). Systems lens: Function = system of record used at each step. Nothing about the process steps has to change. Only the viewpoint changes. This is why “diagram to dataset” is not a formatting project. It is an operating model change. Once the dataset exists, teams stop arguing about whose Visio file is correct. They update 1 table, then regenerate the views everyone needs. A few practical wins show up immediately: • A single source of truth that does not drift across 10 diagram variants. • Faster updates (row edits vs diagram surgery). • A clean audit trail (what changed, when, and why). • Better AI outcomes (structured tables beat messy screenshots). If you want to validate this quickly, do it in 20 steps: 1. Capture 20 steps as rows with stable Step IDs. 2. Add Next Step IDs to define connections (branching is comma-separated). 3. Import into Visio Data Visualizer. 4. Change 1 lane assignment in Excel and re-import. If the diagram refreshes cleanly, you have a maintainable model. Common import killers to avoid: • Blank rows in TSV (tab-separated values). • Duplicate Step IDs. • Next Step IDs that point to steps that do not exist. • Lane names that drift (Ops vs Operations). After that, the diagram stops being shelfware. It becomes a system of record you can govern, audit, and reuse. Question: What would happen to your improvement backlog if every process map had a dataset behind it? If this feels familiar, start with 1 workflow and prove the round-trip. Everything else gets easier after that. visio, swimlane, process mapping, operations, business analysis, workflow, data visualizer, raci, value stream, automation, risk