Start a conversation

Business Intelligence Consulting & Analytics

Trust the Numbers. Understand What Matters.

Bring metrics, reporting, and analytics into one trusted BI environment. We help teams see performance clearly, explore what changed and make decisions with confidence.

Improve your BI environment

Start with the symptom

What Is Reporting Costing You?

Pick the one that sounds most like your week. We’ll show what we would change and where it sits in the range below.

Pick what reporting is costing you

The symptom

We Report Plenty and Decide Slowly

There is no shortage of reporting, but little of it maps to a decision anyone actually makes, so meetings start by working out what the numbers mean.

What we would change
What you could end up with
  • A KPI set chosen around the decisions each role has to make
  • Reporting designed per audience, not one dashboard for everyone
  • A BI roadmap that sequences the work instead of listing it
What it works with
  • The decisions each team takes weekly
  • The reports in use today, shadow spreadsheets included
  • Who reads what, and what they do next
Best fitBI Strategy, KPI & Reporting Design
Connected engineering
Send this brief to our BI team Talk to our BI team instead
The symptom

Two Reports, Two Answers, Same Question

Revenue, margin, active customers or conversion are each calculated slightly differently depending on which report you open, and both sides can defend their number.

What we would change
What you could end up with
  • One definition per metric, applied everywhere it is reported
  • A shared business layer that dashboards and AI both read from
  • Governance over who can change a definition, and how
What it works with
  • How each contested metric is currently calculated
  • The data models underneath your reporting
  • Who owns each business definition
Best fitSemantic Models & Governed Metrics
Connected engineering
Send this brief to our BI team Talk to our BI team instead
The symptom

Power BI Is In, and Nobody Is Confident In It

The licences are bought and the reports exist, but the model, the DAX and the refresh behaviour are understood by one person and trusted by few.

What we would change
What you could end up with
  • A Power BI architecture and data model built to be handed over
  • DAX and Power Query that someone other than its author can maintain
  • Workspace, dataset and access governance that holds as it grows
What it works with
  • Your existing Power BI estate, however it got there
  • The Microsoft platform around it
  • The people expected to own it afterwards
Best fitPower BI Consulting & Implementation
Connected engineering
Send this brief to our BI team Talk to our BI team instead
The symptom

The Dashboard Is Busy and Says Nothing

Everything available has been put on the screen, so trends, exceptions and the one number that should interrupt someone all read at the same weight.

What we would change
What you could end up with
  • Executive and operational views built around what each has to notice
  • Drill-down from the headline to the record behind it
  • Scorecards where the exception is the thing you see first
What it works with
  • The questions each view has to answer
  • The BI tool you have, or an open choice of one
  • What people currently export to a spreadsheet
Best fitDashboards & Data Visualization
Connected engineering
Send this brief to our BI team Talk to our BI team instead
The symptom

Every Question Becomes a Ticket

Teams cannot answer the next question themselves, so analysis queues behind a small central group and the answer arrives after the decision.

What we would change
What you could end up with
  • Self-service exploration inside guardrails, not raw table access
  • Analytics embedded in the product or the internal tool people already use
  • Role-based views, so what someone can see is decided once
What it works with
  • Who needs to explore and who needs an answer
  • The application analytics should live inside
  • Your access and tenancy rules
Best fitSelf-Service & Embedded Analytics
Connected engineering
Send this brief to our BI team Talk to our BI team instead
The symptom

There Are Four Hundred Reports and Nobody Knows Which Matter

Reports accumulated faster than they were retired, refreshes are slow, and no one can say which of them are actually opened.

What we would change
What you could end up with
  • Report rationalization based on what usage data actually shows
  • Performance work on the models and refreshes that cost the most
  • Access governance and BI standards that keep the next hundred in order
What it works with
  • Your current report inventory and usage logs
  • Refresh times and where they hurt
  • Who is allowed to publish, and where
Best fitBI Governance, Adoption & Optimization
Connected engineering
Send this brief to our BI team Talk to our BI team instead

Not sure where to start? Talk to our BI team

What we build

Analytics & BI Capabilities

When teams rely on different reports and different definitions of the same metric, decisions slow down. We create a clearer BI environment with shared metrics, practical reporting, and analytics people can actually use.

01

BI Strategy, KPI & Reporting Design

Define what matters, how it should be measured, and which reports different teams need.

BI RoadmapsKPI FrameworksReporting StrategyMetrics DesignAnalytics Requirements

02

Semantic Models & Governed Metrics

Create a shared business layer, so important measures are calculated the same way across dashboards and reports.

Semantic ModellingMetrics LayersBusiness DefinitionsData ModelsKPI Governance

03

Power BI Consulting & Implementation

Plan, implement and improve Power BI for reliable reporting, analysis, and wider team adoption.

Power BI ArchitectureData ModellingDAXPower QueryDashboard DevelopmentPower BI Governance

04

Dashboards & Data Visualization

Present complex information in clear views that make trends, performance, and exceptions easier to understand.

Executive DashboardsOperational ReportingInteractive VisualizationKPI ScorecardsDrill-Down Analytics

05

Self-Service & Embedded Analytics

Give teams controlled ways to explore data themselves or access analytics directly inside the tools they already use.

Self-Service BIEmbedded AnalyticsRole-Based ViewsAd Hoc AnalysisAnalytics Portals

06

BI Governance, Adoption & Optimization

Keep reporting manageable as users, dashboards, and data sources grow.

Access GovernanceReport RationalizationPerformance OptimizationUsage MonitoringAdoptionBI Standards

AI-powered analytics

From Dashboards to Conversations

Users should not have to open report after report to understand what has changed. AI can help them ask questions, compare performance and surface what needs attention, while answers remain grounded in approved business metrics.

01

Ask Your Data

Let users ask questions in natural language and receive answers based on governed metrics and BI models.

Example“Why did loan approval rates decline this month, and which customer segments were most affected?”

02

AI-Generated Insight Summaries

Surface important movements and KPI changes directly alongside dashboards.

ExampleHighlight which departments or service areas contribute most to longer patient wait times.

03

Anomaly & Change Detection

Spot unusual movements early and show users where the change began.

ExampleFlag a sudden increase in production defects and identify when the pattern started.

04

Conversational BI & Copilots

Let users explore reports, compare periods, and navigate metrics through conversation.

ExampleAsk a Power BI Copilot to compare payment failures across channels and highlight the biggest differences.

05

AI-Assisted Report Creation

Help analysts create calculations, visualizations and report narratives faster while staying within governed definitions.

ExampleGenerate an initial production dashboard using approved output, downtime, and quality metrics.

Explore AI & Machine Learning

Industry context

Analytics & BI Across Industries

A useful dashboard in lending looks very different from one on a factory floor. We shape BI around the decisions, metrics and operating realities of each industry.

01 / 09

FinTech

01Portfolio Performance
02Loan Funnels
03Payment Analytics
04Delinquency Trends
05Risk & Revenue Reporting

Our work

Data & Analytics in practice.

Engagements from the wider Data & Analytics practice this service sits in — 3 of them written up in full.

View all case studies
The transaction monitoring dashboard on a laptop against a lilac backdrop, showing an alert queue with risk scores.FinTech

Transaction Intelligence & Behavioral Analytics

A scalable and configurable transaction monitoring solution engineered to detect suspicious activity in real time using rule engines, behavioral profiling, and hybrid risk logic, enabling financial institutions to strengthen AML compliance while optimizing operational efficiency.

45%Faster suspicious activity detection35%Improvement in monitoring workflow efficiency
The SWIFT data orchestration console on a laptop, showing a validated message table with status columns.FinTech

SWIFT Data Orchestration & Automation

A centralised data automation platform engineered to replace high-risk manual spreadsheet processes with secure ingestion, task-based validation, and SWIFT-compliant standardisation workflows, ensuring data integrity, operational scalability, and audit-ready transparency for insurance operations.

60%Reduction in manual spreadsheet50%Faster data validation
The watchlist governance console on a tablet, showing a consolidated list view with status columns.FinTech

Watchlist Governance & Compliance Intelligence Solution

Unified data governance and watchlist orchestration solution that consolidates, cleanses, and enriches regulatory, commercial, and internal lists, empowering compliance teams with real-time updates, audit-ready transparency, and exceptionally accurate screening data for enterprise AML operations.

50%Faster watchlist updates40%Reduction in duplicate or inconsistent list data

How we work

How a BI Environment Is Built

We agree what each number means before anyone designs a screen for it, then keep the environment governed as reports and teams multiply around it.

01

Frame the Decisions

Establish which decisions the reporting has to support, who makes each one, and what they currently do when the dashboard does not answer it.

Focus
DecisionsRolesKPIsGaps
02

Define the Metrics

Agree one calculation per measure and put it in a semantic layer, so dashboards, self-service and AI assistants all read the same definition.

Focus
Semantic layerDefinitionsModelsOwnership
03

Build for the Reader

Design each view around what its audience has to notice, with drill-down to the record behind the headline and embedded analytics where the work happens.

Focus
DashboardsDrill-downSelf-serviceEmbedding
04

Govern and Rationalize

Watch what is actually used, retire what is not, tune the refreshes that cost the most, and keep access and publishing standards in place as the estate grows.

Focus
UsageRationalizationPerformanceAccess
Frame01 Frame the DecisionsDefine02 Define the MetricsBuild03 Build for the ReaderGovern04 Govern and Rationalize

Insights

Thinking Behind Better Reporting.

Perspectives on why dashboards fail, what a semantic layer actually buys you, and how analytics earns its place in a decision rather than a meeting.

Feature image showing enterprise data literacy connecting BI dashboards to decision adoption through role-specific users and business actions
Data & Analytics

Why BI Dashboards Fail: Enterprise Data Literacy Playbook

· Kartik Gajjar · 7 min read

Feature image showing embedded analytics, standalone BI, and shared warehouse layers in SaaS reporting architecture
Data & Analytics

Embedded Analytics vs. Standalone BI: Which Reporting Architecture Fits Your SaaS Product?

· Jigar Vavadia · 8 min read

Feature image showing a transition from a dense legacy dashboard to a modern dashboard while preserving power-user workflows and phased migration
Experience Design

Redesigning a Legacy Dashboard Without Losing Power Users

· Rutvi Kothari · 8 min read

All Data & Analytics writing

FAQ

Frequently Asked Questions About Analytics & BI

Straight answers on what business intelligence services and BI consulting actually cover, why dashboards fail, what a semantic layer is, and how Power BI, Tableau and Looker differ.

01What does business intelligence consulting include?

Business intelligence consulting can cover BI strategy, KPI definition, semantic modelling, dashboard design, implementation, governance and adoption. The aim is to create a reporting environment where teams work from the same definitions and have a consistent view of business performance.

02Why do BI dashboards fail?

Dashboards often fail because metrics are unclear, data is inconsistent, or reports become too complicated. They can also fail when users cannot connect what they see to a decision. Strong BI starts with trusted data, shared definitions, and reporting designed around real business questions.

03What is a semantic layer in business intelligence?

A semantic layer translates underlying data into shared business terms such as revenue, margin, or conversion. It gives dashboards, self-service analytics and AI assistants the same foundation, reducing the risk of different teams calculating the same metric differently. Without one, the same measure ends up defined separately in every report that uses it.

04Power BI vs Tableau vs Looker: Which is better?

There is no single best BI platform. Power BI often suits organizations already using the Microsoft ecosystem. Tableau is strong for flexible visual exploration, while Looker works well for governed, model-driven analytics. The right choice depends on your systems, users, and reporting requirements.

05How can AI be used with business intelligence?

AI can support natural-language queries, conversational BI, automated summaries, anomaly detection, and assisted report creation. For reliable results, these capabilities should work with governed metrics and trusted BI models rather than interpreting raw business data without context. The governed metric layer is what keeps the answers trustworthy.

06How is Business Intelligence different from Data Science?

Business Intelligence focuses on metrics, reporting, and visibility into performance. Data Science goes deeper into statistical analysis, experimentation and causal investigation when teams need to understand why something happened or test what may happen next. BI tells you what the number is; data science tells you what is moving it.

Bring Clarity to Every Business Decision

Connect trusted metrics, clear dashboards and AI-assisted analytics so teams can understand performance and act with confidence.

Strengthen your BI environment Explore data & analytics

Ask an AI about this page

Before you choose a partner, ask your own AI

One click opens the assistant you already use with a question that points it at this page, so the answer comes from what we publish, not a guess.

The question it opens withRead https://digiwagon.com/analytics-bi-services and explain how DigiWagon runs a Business Intelligence Consulting & Analytics engagement, what I should expect in the first 90 days, and how to judge whether a partner like this fits a team of our size. Stick to what the page says and mark anything you are not sure about.

ChatGPTPerplexityClaudeGoogle AI ModeGrok