Financial Analytics for trusted finance decisions, not just more reports

Financial analytics is the structured use of governed finance and operational data to measure performance, explain variances, support forecasting, analyse profitability, monitor cash and working capital, and improve management decisions. The work should connect business questions to agreed metrics, traceable data sources, analytical models and decision workflows rather than treating dashboards as an end in themselves.

Scope can include stakeholder discovery, reporting and KPI assessment, metric-definition workshops, source and reconciliation review, semantic-model design, management-reporting and dashboard blueprints, profitability and variance analysis, forecasting requirements, data-quality checks, governance, testing, implementation support, adoption guidance and a prioritised improvement backlog. Final deliverables are agreed during scoping.

Typical sponsors include CFOs, finance directors, FP&A leaders, controllers, heads of management reporting, business-unit finance leaders, CIOs, CDOs and analytics leaders. Effective delivery also needs participation from metric owners, finance subject-matter experts, data and BI teams, source-system owners and relevant risk or control stakeholders.

Common decision areas include budget versus actual performance, forecast changes, revenue and margin movements, product or customer profitability, cost-to-serve, working capital, cash visibility, operating expense, capital allocation, business-unit performance and management reporting. The exact decision set should be prioritised according to business value, data readiness and ownership.

Yes. An engagement can assess existing reports and dashboards, identify duplicated or conflicting measures, review data quality and refresh dependencies, rationalise content, redesign decision journeys, improve semantic models and define governance for ongoing change. A full replacement is not required when targeted remediation is the better option.

The engagement can document business meaning, calculation logic, grain, dimensions, source, owner, approval status and permitted variations for priority measures. Conflicts are surfaced for accountable business decisions rather than silently resolved in code. Approved definitions can then be represented in a metric catalogue or governed semantic model.

Yes, subject to access and data readiness. The service can map information from general-ledger and ERP systems, planning tools, procurement, CRM, payroll, operational systems, databases and controlled spreadsheets. Source reconciliation, data transformation and ownership requirements are confirmed before implementation.

Work can be structured around an existing technology estate and may involve Power BI, Tableau, Looker, Qlik, Excel, Python or R, together with cloud and data platforms such as Microsoft Fabric, Snowflake, Databricks, BigQuery, Redshift and services across Azure, AWS or Google Cloud. Recommendations remain requirements-led unless a specific product evaluation or implementation is in scope.

The engagement can define critical data elements, control totals, tolerances, source-to-report checks, period and entity rules, exception ownership, issue triage and acceptance evidence for analytical outputs. Where a broader validation programme is required, a dedicated data validation workstream may be more appropriate.

The timeline is confirmed after scoping. It depends on the number of decisions and KPIs, source systems, data quality, stakeholder availability, reconciliation effort, semantic-model complexity, dashboard or implementation scope, review cycles, testing needs, access controls, documentation and adoption support.

DataConsultant does not publish a fixed fee for this service on the current service pages reviewed for this scope. A proposal is based on the decisions to support, number of business units and data sources, reporting complexity, metric-definition effort, data remediation, semantic modelling, dashboard implementation, testing, documentation, onsite needs and support required. Public market examples on this page are budgeting context only and are not DataConsultant prices.

Unless explicitly scoped, the service does not automatically include a full data-platform rebuild, ERP remediation, statutory audit, legal or tax advice, formal regulatory assurance, penetration testing, permanent operational support, or unlimited dashboard development. Dependencies and responsibility boundaries should be documented during scoping.

Useful inputs include management packs, KPI lists, chart-of-accounts and entity structures, sample reports, planning and forecast processes, source-system inventories, data models, known reconciliation issues, data-quality findings, access requirements, user groups, governance policies and the decisions leadership wants to improve. Missing evidence should be recorded as a limitation rather than assumed.

Yes. The engagement can work alongside finance, FP&A, accounting, business-unit leaders, internal analytics and engineering teams, platform vendors and systems integrators. Roles, access, dependencies, review points, decision rights and acceptance responsibilities should be agreed during mobilisation.