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Decision Control for the Enterprise

Bring the rigor of financial modeling to business operations.

Data Sifter takes performance management from monthly and quarterly reviews into everyday decisions.

  • Connect Model to Data: A formula-based metric tree mapped to your data warehouse or lake.
  • Explain Variances: Automatically identify the drivers behind performance gaps so teams know where to focus.
  • Align Every Team: Each team sees the drivers it controls and how they roll up to company outcomes.
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Let's explore how it could work for your business.

For an online ticket marketplace like StubHub:

FROM YOUR MODEL:

Revenue = Events × Tickets per Event × Sold by Listed Tickets × Avg Ticket Price × Commission Rate

Revenue

$4.0M

Target $5M

Attainment: 80%

Why did revenue miss?

Share of revenue shortfall

55% → Fewer events
30% → Lower ticket sell-through rate
15% → Other
Metric Contribution

Why did events miss?

Share of events shortfall

80% → Fewer Pop events in CA, OH
20% → Other
Dimensional Contribution

One model. Same interpretation. Across every team.

The problem

Same data. Fragmented interpretation.

Teams see different pictures of performance. Those differences rarely get reconciled, so priorities diverge, decisions become disconnected, and the company loses focus on what actually drives results.

  • Every team rebuilds the logic in its own dashboard, spreadsheet or AI agent.
  • The same terms mean different things in Sales, Marketing, Finance and Product.
  • When a number moves, everyone has an explanation. Teams move on without a shared understanding.

The solution

Your financial model, made operational.

01

Start from your model

The metrics, drivers and targets your finance team already trusts become the foundation.

02

Connect the model to live data

We map each metric to the data warehouse to automatically explain performance and variances.

03

Put the model in everyone's hands

Every team sees the drivers it controls and how they roll up to company outcomes.

For an online ticket marketplace like StubHub

Metric Tree of Formulas

Revenue=Events×Tickets per Event×Sold by Listed Tickets×Avg Ticket Price×Commission Rate
Events=New Partner Events+Existing Partner Events
New Partner Events=Partner Leads×Contact Rate×Meeting Rate×Close Rate×Avg Events per Partner

Every metric is a formula of the drivers below it - so any variance traces back to the operating lever behind it.

Key entities

Slice any metric by

  • Buyer
  • Seller
  • Listings
  • Sales Transactions
  • Events
  • Date
  • Regional Manager

From company-wide performance to the operating drivers behind every result.

Data Sifter contribution analysis decomposing the 22% revenue decline from 2025 Q3 to Q4 with a waterfall of listed events, tickets per event, sell-through, average ticket price and commission rate

For every function

Your Metrics & Driver Formulas. One Connected Business.

Teams agree on how performance is measured, how their metrics fit together, and who owns each driver. Data Sifter turns these agreements into a shared operating model, giving every function clear accountability and the freedom to execute.

Finance

Connect financial plans, budgets, and forecasts to actual performance. Understand variances and the operating drivers behind them.

Key entities

  • Cost Center
  • GL Account
  • Business Unit
  • Product
  • Period

Illustrative formulas

  • Gross Profit= Revenue − COGS
  • EBITDA Margin= EBITDA / Revenue
  • Budget Variance= Actual − Budget
  • Free Cash Flow= Operating Cash Flow − CapEx

Powerful for each function. More valuable together.

Each function can use Data Sifter independently, with its own metrics, formulas, and dashboards. As functions come together, their models connect to create a shared understanding of what drives company performance.

Start seeing value in 1–2 weeks. Build from there.

Start with your most important metrics. Expand to more detail, teams, and capabilities without rebuilding or disrupting what's already working.

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Built on your existing data warehouse, with your data team owning the model.