Decision Control1 / 12

Decision Control for the Enterprise

Bring the rigor of financial modeling to business operations.

datasifter.io

The problem

Same data. Fragmented interpretation.

Your data

Data warehouse / lake

Dashboards

Reports

Spreadsheets

Custom queries

AI agents

Disconnected metrics. Conflicting interpretations. Eroding trust.

Executives

Roll-up reportsCompany performance

Finance

SpreadsheetsNet bookings

Sales

CRM / reportsInvoiced sales

Marketing

DashboardsAttributed pipeline

Product

Custom queriesActive usage

Operations

ERP queriesFulfilled orders

6 teams. 6 views of performance. No shared operating model.

Our approach

Run the Business from a Single Model

Your financial model

Metrics, formulas, drivers & targets

Static · Finance / leaders only

Your data

Data warehouse / lake

Data Sifter

Live, company-wide operating model

Your financial model, connected to live business data

Executives

Finance

Sales

Marketing

Product

Operations

Every team manages its drivers within one connected operating model.

Example · online ticket marketplace like StubHub

One Connected Model of Business Performance.

Metric Tree of Formulas

Revenue=Events×Tickets per Event×Ticket Sell-Through Rate×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

Metrics connect through formulas, so performance changes can be traced to the underlying operating levers.

ENTITIES

Explore performance across

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

Example · online ticket marketplace like StubHub

From fragmented reporting to shared interpretation.

Status quo

Revenue: $4M

Target: $5M|Attainment: 80%

Eight different
explanations of why.

With Data Sifter

Revenue: $4M|Target: $5M|Shortfall: $1M|Attainment: 80%

Why did revenue miss?

Share of revenue shortfall

Fewer events55%

Lower ticket sell-through30%

Other15%

Metric Contribution

Why did events miss?

Share of events shortfall

Fewer Pop events in CA, OH80%

Other20%

Dimensional Contribution

Operating focus: Investigate Pop event supply in CA and OH.

The product

Intelligence built on one model.

Contribution analysis decomposing a revenue decline into its drivers

How to get started

Start using Data Sifter in 1–2 weeks.

01 · Assess

Model & dataset assessment

Walk us through your model, key metrics and drivers.
Show us the available datasets.

02 · Develop

Build & hand off

We build the initial model with your team and connect it to your data.
Your data team owns and maintains it.

03 · Deploy

Start simple, scale up

Go live with a simple, aggregated dataset.
Add granularity and capabilities without disrupting users.

Next step

Schedule a working session with your Finance and Data teams to begin the assessment.

Appendix

datasifter.io

Analytics Infrastructure

How you access data

Reports

Area specific reporting

Follow-ups require analysts

LLM on Database

Natural-language questions directly on data

Inconsistent metric definitions

Visualization

Interactive dashboards and charts

Limited flexibility, conflicting metrics

Semantic Layer

Metrics mapped to data: entities & relationships

Models metrics, not how they fit together

LLM on Semantic Layer

AI answers using governed metrics & relationships

Consistent metrics, disconnected explanations

Business Outcomes

How you run the business

Decision Control

Brings the rigor of financial modeling to business intelligence

One model connecting drivers to outcomes
Consistent explanations of performance
Shared across every team

How the Shared Model is Built and Evolved

Sales

Product

Finance

Marketing

Analytics

Shared Model

Validation Engine

Standardized Data

Data Engineering

Propose changesReview & approveServes as dataset requirementsValidation resultsBuild & maintain dataset

Distributed ownership with governance and continuous validation

Architected with Data Security in Mind

Client Users

AWS Virtual Private Cloud

Application

Query Engine
Access Control
Analysis Engine

LLM

Amazon Bedrock

No database access
No SQL generation
No training on client data

Client Single Sign-On

Client Data Warehouse

Private read-only

Security Controls

Data remains in client data warehouse
No raw customer data is persistently stored
Encryption at rest and in transit
Access controlled by client SSO provider or Data Sifter
Dedicated AWS account per client
A single database service user with read-only access to a non-PII schema

Business Specs Sample

Metric Tree

metrics:
  - name: listed_events
    type: count_distinct
    table: listing
    field: event_id
    targets:
      - monthly: monthly_target_listed_events
    format: number

  - name: listed_tickets
    type: sum
    table: listing
    field: number_of_tickets_listed
    format: number
    metric_decomposition_formula: listed_events * listed_tickets_per_event

  - name: listed_tickets_per_event
    type: calculation
    definition: listed_tickets/listed_events
    format: number(2)

  - name: revenue
    type: sum
    table: sales
    field: commission_amount
    targets:
      - monthly: monthly_target_revenue
    default_table_roles:
      - date: sale_date
    format: currency
    metric_decomposition_formula: listed_events * listed_tickets_per_event
      * sold_by_listed_tickets * avg_ticket_price * commission_rate

Entity Relationships

tables:
  - name: sales
    source_table: dw_sales
    primary_key:
      - sale_id
    fields:
      - name: sale_id
        type: number
      - name: event_id
        type: number
      - name: sale_date
        type: date
      - name: transaction_posted_date
        type: date
      - name: seller_id
        type: number
      - name: buyer_id
        type: number
      - name: sold_tickets
        type: number
      - name: sales_amount
        type: number
        format: currency(2)
      - name: commission_amount
        type: number
        format: currency(2)
      - name: days_from_list_to_sale_date
        type: number
      - name: days_from_sale_to_event_date
        type: number
      - name: is_first_time_buyer_sale
        type: boolean
      - name: is_first_time_seller_sale
        type: boolean
    joins:
      - table: event
        join_type: left
        join_condition: event_id = event.event_id
      - table: date
        join_type: left
        table_role: sale_date
        join_condition: sale_date = date.date
      - table: date
        join_type: left
        table_role: transaction_posted_date
        join_condition: transaction_posted_date
          = transaction_posted.date
      - table: seller
        join_type: left
        join_condition: seller_id = seller.seller_id
      - table: buyer
        join_type: left
        join_condition: buyer_id = buyer.buyer_id

Decision Control for the Enterprise

Bring the rigor of financial modeling to business operations.

datasifter.io

Decision Control for the Enterprise