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Consulting · Data & Decision Making
Define what the business must know to make better decisions.
We design KPI frameworks, metric definitions, reporting structures and decision rhythms that connect business questions to trusted data and accountable action.
- Diagnostic
- Fixed scope
- A roadmap you own
Trusted by established businesses
The Constraint
Start with the symptom you recognise.
Most businesses that arrive here have plenty of reporting, and very little confidence in it. Open any symptom for the decision underneath it.
What's included
What we cover
KPI design
KPIs and metric definitions
We define the metrics that matter, with a single agreed definition for each, tied to commercial outcomes rather than activity.
Reporting logic
Reporting structure and standards
We design how reports are structured, sourced and produced, replacing slow, error-prone manual assembly with consistent, documented reporting logic.
Dashboard strategy
The dashboards worth building
We plan which executive, department and operational dashboards the business actually needs, and the specific decisions each one exists to support.
Decision rhythm
Decision visibility and cadence
We establish the reporting rhythm and decision process, so numbers are reviewed on schedule and acted on, not just produced.
Data foundations
Data source mapping and tracking review
We map where data lives across your tools and identify the tracking, analytics and data quality gaps that undermine trust.
Decisions
Start with the decisions, not the dashboards
Every function makes different decisions on a different rhythm. Each needs its own metric, cadence and owner. The reporting system is designed backwards from decisions like these.
Leadership
Where does next quarter's investment go?
Outcome and driver metrics reviewed monthly and quarterly: performance, capacity and pipeline in one trusted view.
Marketing
Which channels earn more budget?
Acquisition cost and channel contribution reviewed weekly, with attribution rules agreed before the debate starts.
Sales
Which deals need intervention this week?
Pipeline movement and conversion leading indicators on a weekly cadence, owned by the person who can act on them.
Operations
Where will capacity break first?
Operational and guardrail measures on a daily and weekly rhythm, with thresholds that trigger action before the breach.
Customer experience
Which customers are at risk?
Diagnostic and retention indicators reviewed monthly, connected to the follow-up they should trigger.
Deliverables
What you walk away with
- ✓Data and decision-making audit report
- ✓Current-state reporting assessment
- ✓Data source map
- ✓KPI framework
- ✓Data dictionary and metric definitions
- ✓Decision-making framework
- ✓Measurement strategy
- ✓Reporting gap analysis
- ✓Data quality findings
- ✓Tracking and analytics recommendations
- ✓Dashboard strategy and wireframes
- ✓Executive, department and operational dashboard requirements
- ✓Reporting standards document
- ✓Prioritised improvement roadmap and implementation plan
How it runs
How we think it through
Diagnostic before prescriptive: the decisions define the metrics, the metrics define the reporting, and only then does anything get built.
- 01
Decision discovery
We begin by listing the decisions leadership, marketing, sales, operations and customer teams actually need to make. A report that doesn't support a decision is noise, so the decisions define everything that follows.
- 02
Evidence and report review
We review your existing reports, dashboards, spreadsheets and analytics: where the numbers come from, why teams disagree on them, and where trust broke down. Data quality findings surface here, not after a build.
- 03
KPI design
We design the KPI architecture (outcome, driver, leading indicator, guardrail, diagnostic and operational measures) with a single documented definition, data source and owner for each metric.
- 04
Stakeholder alignment
Definitions only hold if the people who report against them agree. We work the framework through the metric owners across functions, so it survives contact with the first month-end.
- 05
Future-state reporting design
We map the data sources, set the dashboard strategy and requirements, and define the decision rhythm: who reviews what, how often, what threshold matters and what action follows. Where Data Engineering or tracking work is needed first, we flag it here.
- 06
Handover
You receive a prioritised implementation roadmap for the dashboards, tracking fixes, integrations or data infrastructure that follow, scoped so any build, internal or with our engineering team, starts from defined logic.
Outcomes
What typically improves
The core deliverable is a trusted decision framework: what to measure, how to define it, where the data comes from, and how leaders use it.
Confidence
Numbers leadership can stand behind
Better confidence in key business metrics, with fewer reporting errors and fewer disputes over whose figures are right.
Speed
Faster leadership decisions
When definitions are agreed and reporting is reliable, decisions typically move faster and lean less on instinct.
Efficiency
Reduced manual reporting effort
Less time assembling reports by hand each period, and fewer errors introduced along the way.
Visibility
A clearer view across the business
Clearer visibility across marketing, sales, operations, finance and customer performance, structured around the decisions that matter, not activity.
Prioritisation
Better-evidenced investment decisions
Stronger evidence for budget allocation, and better prioritisation of growth, software, AI, operations and marketing investments.
Foundations
A base for what comes next
Clearer foundations for dashboards, automation, AI and custom software, built on metrics the business already trusts.
Scope
What this is, and what it isn't
What's included
- Decision discovery across leadership, marketing, sales, operations and customer experience
- Current-state review of reports, dashboards, spreadsheets and analytics
- KPI architecture: outcome, driver, leading indicator, guardrail, diagnostic and operational measures
- A single agreed definition and owner for every metric
- Reporting and decision rhythm: who reviews what, how often, and what action follows
- Data source mapping, with feasibility flagged where engineering or tracking work is needed first
- Dashboard requirements and wireframes for the views worth building
- Reporting standards and a prioritised implementation roadmap
What this is not
- Generic spreadsheet cleanup or basic data entry
- One-off report formatting with no decision purpose
- A full data engineering or data warehouse build, which belongs to Data Engineering
- Dashboard builds where the KPI logic has not yet been defined
- Paid media management or ongoing campaign reporting operations
- Accounting, tax, legal or compliance advice
- Data collection for the sake of collecting data
Who it's for
A decision system, not another dashboard
Data & Decision Making is a consulting service for businesses that need to turn scattered information into trusted, decision-ready insight. It focuses on the questions the business needs to answer, the metrics that matter, the reporting structure required, and the decision rhythm needed to act, because dashboards are only useful when they support real decisions and the underlying metrics are trusted.
Discuss your decision system- Data spread across systems, no source of truth
- Reliant on manual, disconnected reporting
- Don't trust your dashboards or reports
- KPIs unclear or defined inconsistently
- Need better visibility across the business
- Deciding on assumptions, not evidence
- Want reliable reporting before scaling
- Operations-heavy and needing evidence
Probably not the right fit
- −You only want cosmetic dashboard formatting, or existing reports made to look nicer
- −You need a large historical data cleanup with no business question behind it
- −You want reports that aren't tied to any decision
- −You want a dashboard built quickly, without defining what it should measure or why
- −Your business is still early-stage: this work typically suits established businesses, usually $2M+ in revenue, with real reporting complexity
Where this fits
The decision layer between strategy and build.
Data & Decision Making defines the decisions. Related services implement the pipelines, dashboards, AI and ongoing support. Select a pathway to see where it picks up.
Data & Decision MakingThe hub: owns the decision system
Owns: KPI logic · metric definitions · reporting structure · dashboard strategy · decision rhythm.
Data & Decision MakingData Engineering
When it comes into play
The numbers live in disconnected systems and reporting can't be automated by hand.
What it owns
Pipelines, warehouses, integrations, reporting-ready datasets.
Relationship to Data & Decision Making
We define what should be measured; Data Engineering builds the technical foundation that moves, cleans and prepares the data.
Proof
Selected work.
Payment Processing Cost Reduction. An ecommerce retailer was losing a significant percentage of revenue to payment processing and invoice platform fees. Web Lifter redesigned the entire sales and payment workflow, replacing Stripe and Paycove with a direct Westpac PayWay integration and a custom-built invoicing platform. The new architecture reduced transaction costs, streamlined operations, and delivered immediate profit improvements without requiring any increase in sales volume.
Read the caseWhat clients say
“We can't recommend Web Lifter highly enough … a digital partner who could understand our operations, connect the dots between marketing and backend systems, and deliver real results.”
Questions
Common questions
Fit
No. This service defines the KPI logic, reporting structure, metric definitions and dashboard requirements first. The dashboard build itself may follow through Kepler, Data Engineering or another implementation pathway.
Established businesses with useful data spread across multiple tools, reports, teams or spreadsheets that need clearer visibility for leadership, marketing, sales, operations, finance, customer experience or service delivery.
No. This is not cosmetic dashboard design. It is for businesses that need reporting to support better decisions. The visual layer comes later, once the logic is right.
It depends on the number of data sources, departments and decisions in scope. This is fixed-scope consulting work, and pricing is confirmed on a scoping call once we understand your current reporting and the decisions the work needs to support.
Dashboards
This service is often most useful exactly then, when dashboards already exist but leadership doesn't trust them, use them, or make decisions from them. We fix the logic underneath.
Yes, depending on access and scope. The focus is not the platform itself, but the business decisions those sources need to support and how their metrics are defined.
Data quality
The service identifies data quality issues, source gaps, tracking problems and reporting risks. Technical cleanup, pipelines, warehouses and integrations then move into Data Engineering.
Yes. AI reporting, summaries, recommendations and automation are only useful when the underlying data, definitions and decision logic are reliable. Otherwise AI produces confident, wrong answers faster.
Deliverables
Typically a reporting assessment, data source map, KPI framework, data dictionary, dashboard strategy, reporting gap analysis, a decision framework and a prioritised implementation roadmap.
A trusted decision framework showing what to measure, how to define it, where the data should come from, and how leaders should review and act on it.
Boundaries & next steps
Data & Decision Making owns KPI design, reporting logic, dashboards and decision rhythm. Business Economics owns pricing, margins, profitability and cost-to-serve. They pair well: one defines the commercial metrics, the other turns them into a reporting system.
This service defines what should be measured and why. Data Engineering builds the technical foundations that move, clean, integrate and prepare the data behind those definitions.
Not the ongoing operation of it. This service defines how marketing performance fits into the wider decision system (the KPIs, definitions and reporting structure) so campaign reporting connects to commercial outcomes rather than activity.
Common next steps include Data Engineering to build the pipelines and datasets, Kepler for governed dashboards, AI Engineering for AI-assisted analysis, Software Development for custom tooling, or Technology Managed Services for ongoing support.
Before the dashboard