Tableau Solve
Tableau Solve

Tableau Solve Hands On

Explore every possibility. Decide with confidence.
A guided, hands-on walkthrough — about 15 minutes.
Tableau Solve · Innovation & Build Team
Built by the people making Solve real.
Product
Esther Schenau
Director, Product Growth
UX
Michael Hill
Principal Product Designer
Bailiang Zhou
Principal Product Designer
Architecture
James Diefenderfer
Principal Architect
Engineering
Ryan DiCenzo
Software Engineering, SMTS
Monil Ghandi
Software Engineering, PMTS
Parth Shah
Software Engineering, LMTS
Marian Simo
Software Engineering, MTS
Adrian Sufaru
Software Engineering, PMTS
TPM
Billy Lewis II
Technical Program Manager
Innovation Advisor
Matthew Miller
VP, Product

Tableau Solve

The flexibility of a spreadsheet, the power of data science, paired with the capability of an agent — inside the governed Tableau platform.

Bottlenecks exist when processes are inflexible and too rigid

  • Data is read-only
  • Insights are not immediately relevant
  • Users can't pressure-test the data
  • Analysts are drowning in tasks

Empower the business to experiment in a trusted environment

  • What-if insights in the flow of work
  • Conversational UI & deterministic UI
  • Trusted data and semantics
  • Traceable history for brainstorming
How Solve delivers
Five things you can do — every one stays governed and audited
Stay
In the flow of work with saveable "what-if" scenarios — no exporting to a spreadsheet.
Optimize
Against real-world constraints — surface the best allocation given your rules.
Layer
Intelligent what-ifs at any level of your data — a single row to an aggregate.
Validate
Every decision with a full audit trail: what changed, who changed it, and why.
Enumerate
Multiple scenarios in seconds, compared side by side.
Who makes simulations
Three personas work together
Simulation capabilities depend on licensing. These roles often overlap — the same person can wear more than one hat.
Pre-existing to Tableau
Workbook Author

Scopes which fields are editable to simulation authors. Often the same group or technical level as simulation authors — they set the guardrails everyone else works within.

New · Tableau Solve
Simulation Author

Creates, modifies, and saves simulations. We expect most Solve users here — even dashboard viewers are potential simulation authors as we encourage experimenting with numbers in place.

Business user mode — what-if scenarios Advanced mode — deep scenario planning & optimization
New · Tableau Solve
Simulation Viewer

Opens public simulations and can play with overrides, but can't save. We expect only a small portion of users in this category.

Login instructions
Get into Tableau Solve — 4 steps
Sign in to the shared training environment, create your simulation from the golden template, then jump into any capability below.

Layer — Multi-level overrides

Conduct what-if analysis at any level of your data. Edit a leaf value directly, or edit an aggregate and choose how the change flows down.
Start here — what most teams live with today
Q1 2026 Commit Forecast — Sales Reporting 🔒 Read-only
🔒 This is a published dashboard — great for seeing the numbers, but you can't ask "what if AMER closes 15% more?" right here. Modeling that means taking the data somewhere else.
↓ With Tableau Solve

The same forecast becomes something you can experiment on.

  • Override any value — a single rep or a whole region rollup.
  • Watch totals and charts recalc instantly, in place.
  • Stay governed — no export, full audit trail of every change.
Step through — how an override works in Solve
Show · step 1 of 6
Q1 2026 Commit Forecast
Commit Forecast Amount ($) · by Region → User · Scenario: Expected
Commit Forecast by Region
Current
🕘 History
No changes yet.
Overrides are logged here — what changed, and by how much.
The math — how the numbers actually move
Direct override
Edit one value

Replace a single leaf value. The rollup above it is just the sum of its children, so it re-totals automatically.

rollup = Σ childᵢ AMER = 500,000 + 300,000 + 200,000 = 1,000,000
Aggregate override → distribute
Edit a total, push it down

Type a new total and Solve solves for the children. You choose how the delta (Δ = new − old) is spread:

Δ = new_total − old_total Proportional — a share of Δ by contribution: childᵢ = childᵢ + Δ × (childᵢ ÷ old_total) Even split — same dollars to each: childᵢ = childᵢ + (Δ ÷ n) Manual — you set each child yourself.
Worked example — proportional distribution

We raised the AMER total from 1,000,000 to 1,300,000 — a delta of +300,000. Proportional gives each rep a slice of that delta equal to their share of the total, so the Override column always sums back to +300,000.

RepBeforeShare of totalOverride = Δ × shareAfter
Sarah Chen500,00050%300,000 × 50% = +150,000650,000
Marcus Reid300,00030%300,000 × 30% = +90,000390,000
Priya Nair200,00020%300,000 × 20% = +60,000260,000
AMER total1,000,000100%+300,0001,300,000
Each rep's override = delta × their share of the total. Everyone keeps the same slice of the pie; only the size of the pie changes.
Even split would instead ignore share and add the same amount to each: 300,000 ÷ 3 = +100,000 per rep.
Now you do it
Your turn — in Tableau Solve

The sales-commit dataset is already loaded for you. You've seen it and you know the math — now push on real numbers. There’s more than one way to get there — ask Tableau Agent in plain language, or do it by hand in the UI (click a value to override, drag a bar). Every edit stays governed and lands in History, so you can see exactly what moved and by how much.

1 “What am I looking at?” 2 “Push AMER’s 5 largest in-quarter deals to next quarter — what’s the coverage impact?” 3 “Apply a 10% discount to every in-quarter AMER deal — net revenue vs. the committed number?”

Enumerate — Scenarios & comparison

Scenarios are a global control at the top of your simulation. Select one to plan in it, or layer several together to compare — Conservative, Expected, Aggressive — all on the same governed view.
Start here — what most teams live with today
Q1 2026 Commit Forecast — one plan 🔒 Read-only
🔒 There's exactly one number. To weigh a cautious case against an aggressive one, you'd copy the workbook, hand-edit each version, and try to line them up in a slide later.
↓ With Tableau Solve

One simulation, many scenarios — controlled globally.

  • Scenarios live at the top — the control applies to every chart in the view at once.
  • Select one to plan in it, or layer several to compare in any combination.
  • Stay governed — every scenario reads from the same trusted data.
Step through — how scenarios work in Solve
Step 1
◧ Scenarios
Sales Overview
Commit Forecast Amount ($) · by Region — the scenario control applies to this chart and every other on the view
Commit Forecast by Region
The idea — how the global control works
A global control
One control, whole view

Scenarios sit at the top of the simulation, not on any single chart. Whatever you select applies to every chart at once — so the entire view stays consistent as you move between plans.

Select one, or layer many
Layer to compare

Select a single scenario to plan inside it. Or turn on several at once and Solve layers them together — each becomes its own color-coded series, so you cross-compare in any combination on one view.

Conservative — cautious case Expected — the working plan Aggressive — upside case
Now you do it
Your turn — in Tableau Solve

You owe the VP a range, not one number. On the loaded dataset, build three commits on the same governed data and compare them — no divergent spreadsheets to reconcile. There’s more than one way to get there — ask Tableau Agent in your own words, or drive the scenario control by hand (+ to add, select to plan, layer to compare).

1 Create three commits — Conservative, Expected, Aggressive — on the same data 2 Compare them side by side — where do they diverge most? 3 Which number would you say out loud to the CRO?

Optimize — Constrained optimization

Tell Solve the goal and the rules; it finds the numbers. In today's streamlined mode, you don't touch a panel of dials — you just ask Tableau Agent in plain language, and it runs the solver for you.
In this experience, the solver is reached through Tableau Agent. The full manual Optimization panel (objective, grain, variables, convergence) lives in advanced authoring mode — streamlined mode keeps it conversational.
Start here — what most teams live with today
AMER Commit — 3 sales leaders 🔒 Read-only
🔒 The AMER region rolls up from its three sales leaders, at $1.0M today. Leadership wants that commit at $2.0M — but Priya's number is fixed by contract. Which leaders move, and by how much? By hand, that's a lot of trial and error.
↓ With Tableau Solve

Just ask — the Agent runs the solver.

  • Say the goal in plain language — a target to hit.
  • Name the rules — e.g. keep Priya locked.
  • Agent solves it — a governed result, right in the view.
Step through — asking the Agent to optimize
Step 1
AMER Commit Forecast
Commit Forecast Amount ($) · by sales leader — rolls up to the AMER total
Before vs. After solve
Before After
Tableau Agent
The math — what the solver actually does
Objective: minimize change
Reach the goal, move the least

Of all the ways to hit AMER = $2.0M, the solver prefers the one with the smallest total squared change across the reps — no wild swings unless the rules force them. Under a fixed total, that means an equal increase for each movable rep.

minimize Σ (newᵢ − oldᵢ)² subject to: Σ newᵢ = 2,000,000
Constraints bound the search
Rules the answer must obey

Priya is locked, so she stays at $200,000. That leaves $1,800,000 for Sarah + Marcus (currently $800,000) — the solver spreads the extra $1,000,000 across the two movable reps.

Priya = 200,000 (locked) Sarah + Marcus = 1,800,000
Worked example — minimize change to hit $2.0M

AMER is $1,000,000 today (Sarah 500k, Marcus 300k, Priya 200k). Target is $2,000,000. Priya is locked, so the two movable reps must together rise by $1,000,000. Because the solver minimizes the sum of squared changes, the smallest-change answer gives each movable rep the same absolute increase — not a proportional one.

RepBeforeRuleChangeAfter
Sarah Chen500,000movable+500,0001,000,000
Marcus Reid300,000movable+500,000800,000
Priya Nair200,000🔒 locked+0200,000
AMER total1,000,000target+1,000,0002,000,000
Minimizing Σ(change)² under a fixed total is smallest when the change is spread equally — so the $1,000,000 gap splits into +$500,000 each. (A proportional split would move Sarah more and raise the total squared change — a bigger overall change, which is exactly what "minimize change" avoids.) Unlock Priya or add a cap, and the solver re-solves.
Now you do it
Your turn — in Tableau Solve

State a goal, name your rules, and let the solver find the least-cost answer — then push it until it breaks. There’s more than one way to get there — ask Tableau Agent in plain language, or set it up by hand in the Optimization panel (objective, constraints, Run Solver). On the loaded dataset, try these — the last one can’t be solved: that’s the point.

1 “What’s the fewest deals I must pull into AMER to hit 2.7x, at the lowest discount cost?” 2 “Maximize net revenue while keeping AMER ≥ 2.7x and not dropping any protected deals.” 3 “Need EMEA at 3x coverage — what do we do to get there?”