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Data Analytics

Understand what the numbers show and what needs attention.

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A good fit if

  • Leadership waiting weeks for financial reports
  • Numbers that don't match between systems and spreadsheets
  • No way to compare performance across units, locations, or time
  • Private equity groups that need comparable measures across their companies

What we do

  • Agree the metrics

    Agree the key performance indicators (KPIs) for margin, cash, and growth, and write down what each one means.

  • Consolidate the data

    A data warehouse fed by your ERP, CRM, operational systems, and spreadsheets, with validation rules.

  • Build useful reports

    Strategic KPIs and trends for executives; job- and unit-level detail for operators.

  • Keep the numbers reliable

    Documented sources, clear ownership, and a defined process for exceptions and corrections.

Common uses

  • Job profitability dashboards
  • Executive KPI scorecards
  • Operational efficiency tracking
  • Cash flow forecasting
  • Resource utilization analysis
  • Sales pipeline reporting
Illustrative example

Every number has a definition

Before a metric goes on a report, we agree what it means, where it comes from, how often it refreshes, and who owns it.

  • Job gross margin

    Definition
    Contract revenue minus job cost to date, by job
    Source
    ERP job cost
    Refresh
    Daily
    Owner
    Controller

  • Backlog

    Definition
    Signed contract value not yet billed
    Source
    ERP and CRM
    Refresh
    Weekly
    Owner
    CFO

  • Days sales outstanding

    Definition
    Average days from invoice to payment, excluding retainage
    Source
    Accounting
    Refresh
    Weekly
    Owner
    AR manager

  • Service calls completed

    Definition
    Completed work orders as a share of scheduled, by branch
    Source
    Field service
    Refresh
    Daily
    Owner
    Service manager
Sample definitions for illustration. Your metrics, sources, and refresh cadence are agreed with your team.

How it works

  1. Agree the questions

    Start with the decisions leadership needs to make.

  2. Define and source

    Set the definition, source, and refresh cadence for each metric.

  3. Build and validate

    Build the warehouse and reports, and check numbers against source systems.

  4. Train and hand over

    Train the people using the reports and confirm who owns each metric.

Not sure where this would start for you? Walk us through how the work moves today.

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Client results

Data Analytics in practice

See all client work

What you keep

  • Written definitions for every reported metric
  • Documented data sources and owners
  • Reports built for executives and for operators
  • Self-service access and training for your team

What to plan for

  • Reports refresh on an agreed schedule. How current they are depends on each source system.
  • Validation rules flag bad or missing data. Someone on your team owns the correction at the source.
  • A metric goes on a report once its definition is agreed, not before.

Questions

Which systems can you report from?

ERP, CRM, operational systems, and spreadsheets. We confirm what each source can provide before we build.

How current will the numbers be?

Each metric gets an agreed refresh cadence based on its source and how often the decision it supports is made.

Is this just dashboards?

No. Reports sit on agreed definitions, a data warehouse with validation rules, and clear ownership that keeps the numbers reliable over time.

Who maintains it after launch?

Your team owns the metrics and sources. We document how everything works and train the people using it.

Related reading

Related services

Talk through your operations

Tell us which numbers leadership waits on today. We'll suggest where to start.

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