Reporting & Business Intelligence Consulting

The two terms overlap heavily in practice but emphasise different parts of the same discipline. Business Intelligence consulting (sometimes business intelligence and analytics consulting) typically emphasises the operational layer: dashboards, scheduled reporting, KPI frameworks and the data pipeline that feeds them. Analytics consulting typically emphasises the analytical layer: deeper data science, predictive modelling, segmentation and causal inference. Most engagements in financial services need both. The reporting layer answers "what happened?" and "what's happening now?", while the analytics layer answers "why?" and "what's going to happen?". We deliver both as one engagement because separating them produces dashboards that describe symptoms without diagnosis, or analytics that nobody operationalises because the reporting infrastructure underneath cannot deliver it daily.

Business intelligence matters more in financial services than in most sectors because of three structural factors. First, the regulatory burden produces reporting requirements that are non-negotiable: FCA returns, prudential reporting, KYC and AML monitoring, Consumer Duty outcomes data, all need accurate underlying BI before they can be reported externally. Second, the operating model in financial services is data-heavy by design: every transaction, policy, mandate, application or position generates structured data, and the firms that turn that data into operational advantage compound faster than firms that don't. Third, the buying cycle and customer lifetime value are long enough that small attribution improvements compound into material revenue differences over multi-year horizons. Firms that under-invest in BI underperform in financial services in ways that don't show up immediately but are unrecoverable once the gap opens.

Yes. FinTech BI engagements have different priorities than incumbent financial services BI engagements. FinTechs typically need real-time operational dashboards (transaction monitoring, user activity, fraud signals), unit economics reporting that holds up to investor scrutiny (LTV, CAC, payback, cohort retention), product analytics integrated with revenue data (which features drive monetisation, which user behaviours predict churn), and growth attribution across the channels and partner integrations FinTechs typically use. We build FinTech BI in Looker, Power BI, Tableau, HubSpot Reporting, Hex, Sigma or whichever BI platform fits the company's data stack and engineering maturity. Most FinTech engagements include some data engineering work to unify the data layer before the BI layer can be built credibly on top.

A BI implementation runs through four sequenced phases. Phase one is discovery and audit: data source mapping, current-state assessment, decision-mapping workshops with each audience, KPI framework review and a prioritised roadmap of what to build, in what order, and against what success criteria. Phase two is data architecture: pipeline design, warehouse or lake structure, transformation logic, data modelling and integration design. Phase three is the BI layer: dashboard development, report templates, alerting logic and the audience-specific permissioning that determines who sees what. Phase four is rollout: phased deployment to each audience, training built into the rollout, governance framework documentation and the ongoing optimisation model. Most engagements run 12-20 weeks for a focused build, 20-32 weeks for a comprehensive multi-source implementation, and longer where data engineering work is needed to unify the source layer first.

AI in BI lands in three places on our engagements. First, in descriptive and diagnostic analytics, where AI surfaces patterns, anomalies and segmentation insights faster than a human analyst working through dashboards manually. Second, in predictive analytics, where machine learning models forecast pipeline, predict churn, score leads, identify customers most likely to upgrade or expand, and inform capital and operational planning. Third, in decision intelligence proper, where AI generates recommendations rather than just describing what happened. We scope AI use cases against feasibility, measurable impact and the regulatory perimeter — some AI features are appropriate to deploy immediately, some require additional governance, and some are not yet defensible to use in regulated decisioning contexts. The upstream readiness assessment work sits in our AI Strategy & Readiness page. The downstream build work where agentic systems get embedded into operational decisioning sits in our Agentic Systems & Automation page.

We are platform-agnostic and select the BI tool against the engagement, not against a partnership. The platforms we deliver on most often include Power BI for Microsoft-heavy environments and enterprise reporting, Looker for modern data stacks and SQL-first BI, Tableau where the user base is large and visualisation-heavy, HubSpot Reporting for marketing and sales reporting integrated with the HubSpot CRM, Sigma and Hex for analytical workflows and product analytics, Domo for executive dashboards in mid-market firms, and Metabase for open-source-first FinTech environments. We also work directly with the underlying data layer (Snowflake, BigQuery, Databricks, Postgres) where the data engineering work has to happen before the BI tool layer can be built credibly on top.

A focused engagement covering reporting design, dashboard build and KPI framework typically runs between 8 and 14 weeks. A comprehensive BI implementation covering data architecture, multi-source integration, dashboard development and rollout typically runs between 14 and 24 weeks. Engagements with significant data engineering work on the source layer (legacy data cleanup, multi-system unification) typically run 24-40 weeks. Pricing depends on data complexity, source system count, sub-sector regulatory requirements and the breadth of audiences served, so we confirm pricing after the discovery workshop rather than publishing rate cards. Most engagements include the first 30 days of post-launch optimisation and the governance documentation as part of the original scope.