Enterprise Business Analytics &
Decision Intelligence Services
Mobiloitte designs business intelligence and analytics solutions that connect enterprise data with governed KPIs, interactive dashboards, self-service analytics, predictive models and decision-support workflows. From executive performance dashboards to operational analytics and AI-assisted exploration, we help organizations move beyond reporting what happened toward understanding why it happened, what may happen next and which action deserves attention.
What Is Business Analytics?
Business analytics is the use of trusted data, business metrics, statistical analysis, visualization and predictive techniques to understand performance and support better decisions.
Business intelligence typically helps organizations monitor what happened and what is happening through reports, dashboards and KPIs.
Advanced analytics can help explain why something happened and estimate what may happen next.
Decision intelligence goes one step further by connecting data, analytics, business context and decision workflows so teams can evaluate options and act with greater confidence.
Trusted for Enterprise Digital Engineering
Mobiloitte works with organizations building and modernizing digital products, data platforms and enterprise software across diverse business environments.
Enterprise Business Analytics &
Decision Intelligence Capabilities
Analytics Strategy & KPI Framework
Before building dashboards, define what the organization actually needs to measure. A dashboard should begin with the decision—not the visualization.
- Business objectives
- Decision areas
- Critical KPIs
- Metric definitions
- Data sources
- Reporting requirements
- Analytics maturity
- Data requirements
Executive Dashboards & Business Intelligence
Create executive and management experiences that provide a consistent view of business performance. Design should prioritize clarity → context → exceptions → trends → action.
- Executive dashboards
- Financial dashboards
- Operational dashboards
- Performance scorecards
- Management reporting
- Board reporting
- Drill-down analysis
Governed Metrics & Semantic Layer
Create one meaning for every important business metric. Analytics fails when different departments calculate the same KPI differently. A semantic layer creates a shared business language across dashboards, APIs, analytics and AI.
- Business terminology
- Metric formulas
- Dimensions
- Relationships
- Access rules
- Time logic
- Reusable business definitions
Self-Service Analytics
Give authorized business users the ability to explore trusted data without creating uncontrolled spreadsheet copies. Self-service does not mean ungoverned access.
- Interactive filtering
- Ad hoc analysis
- Governed datasets
- Reusable metrics
- Saved views
- Role-based dashboards
- Natural-language exploration
Diagnostic Analytics
Move beyond knowing that a KPI changed. Investigate why revenue declined, which segment drove churn, or where operating costs increased.
- Segmentation
- Cohort analysis
- Trend comparison
- Variance analysis
- Correlation
- Root-cause exploration
- Funnel analysis
Predictive Analytics & Forecasting
Use historical and contextual information to estimate possible future outcomes. Model quality should be validated against actual outcomes over time.
- Sales forecasting
- Demand forecasting
- Churn prediction
- Customer propensity
- Risk indicators
- Capacity forecasting
- Workforce forecasting
Decision Intelligence & Scenario Analytics
Move from insight to decision support. Connect Data + Analytics + Business Rules + Context + Alternatives + Human Judgment to evaluate possible actions.
- Scenario comparison
- What-if analysis
- Decision rules
- Optimization
- Recommendation support
- Simulation
- Decision workflows
Real-Time & Operational Analytics
Deliver information closer to the moment decisions need to be made. Real-time analytics should only be used where lower latency creates genuine operational value.
- Operations monitoring
- Transaction monitoring
- Logistics events
- Customer behavior
- IoT telemetry
- Fraud signals
- Incident monitoring
Embedded Analytics
Bring insights directly into the applications where users already work, reducing the need for users to leave their operational workflow.
- Customer portals
- CRM systems
- Employee applications
- SaaS platforms
- Operational systems
- Mobile applications
- Partner portals
AI-Assisted & Natural-Language Analytics
Allow appropriate users to explore governed business information using natural-language questions grounded in trusted business definitions.
- Semantic layers
- Business metadata
- Query generation
- Analytics APIs
- AI models
- Visualization
- Audit logs
Analytics Governance & Adoption
An analytics platform creates little value if people do not trust or use it. Analytics transformation is partly a technology problem and partly an adoption problem.
- KPI ownership
- Metric definitions
- Data access
- Dashboard ownership
- Change management
- Training
- Data literacy
From Reporting to
Decision Intelligence
Organizations can evolve their analytics capabilities progressively.
Level 1 — Reporting
What happened?
Level 2 — Business Intelligence
What is happening?
Level 3 — Diagnostic Analytics
Why did it happen?
Level 4 — Predictive Analytics
What may happen next?
Level 5 — Decision Intelligence
What should we consider doing?
Level 6 — Augmented Decision Support
How can AI help users explore and act faster?
Not every organization needs to begin at Level 6.
The right analytics maturity depends on the data, decisions, users and business value involved.
Architecture of a Modern
Business Analytics Platform
Business Systems
Trusted Data Foundation
Semantic & Metrics Layer
Analytics & Intelligence
Experience Layer
Decision & Action Layer
Across Every Layer
Analytics Built Around the Decisions
Different Teams Make
Executive & Board
- Revenue
- Profitability
- Strategic KPIs
- Growth
- Risk
- Portfolio performance
Finance Analytics
- Budget vs actual
- Cash flow
- Revenue analysis
- Cost variance
- Margin analysis
- Forecasting
Sales Analytics
- Pipeline
- Win rate
- Sales velocity
- Revenue forecasting
- Account performance
- Territory performance
Marketing Analytics
- Acquisition
- Conversion
- Campaign performance
- Customer acquisition cost
- Channel performance
- Marketing ROI
Customer Analytics
- Retention
- Churn
- Customer lifetime value
- Segmentation
- Service performance
- Journey analytics
Operations Analytics
- Productivity
- Cycle time
- Capacity
- SLA performance
- Cost-to-serve
- Operational exceptions
Supply Chain Analytics
- Demand
- Inventory
- Supplier performance
- Order fulfillment
- Logistics
- Forecast accuracy
Workforce Analytics
- Headcount
- Attrition
- Hiring
- Capacity
- Workforce cost
- Skills
Business Analytics Delivery Process
From Business Question to Governed Decision Support
Define the Decisions
Identify: Business questions, Decision owners, Users, KPIs, Current reporting gaps, Desired outcomes
Map Data & Metrics
Identify: Data sources, Existing reports, Metric definitions, Business logic, Data quality, Ownership, Access requirements
Design the Architecture
Define: Analytics model, Semantic layer, Dashboard architecture, BI platform, Security, Refresh requirements, AI requirements
Prototype the Experience
Validate: KPIs, Visual hierarchy, User journeys, Drill-down behavior, Filters, Mobile requirements, Decision context before scaling.
Engineer & Integrate
Build: Metric models, Dashboards, Reports, Analytics services, Predictive models, Integrations, Embedded experiences
Validate
Test: Metric accuracy, Business definitions, Data freshness, Filters, Permissions, Performance, Forecast/model quality where applicable
Activate & Train
Provide: Role-based access, Training, Documentation, Analytics guidance, Adoption support
Govern & Improve
Monitor: Usage, Data quality, Dashboard performance, KPI changes, User feedback, Analytics value and continuously remove obsolete analytics.
Business Intelligence & Analytics
Technology Ecosystem
Technology selection should follow the organization's data environment, business requirements, governance model and existing investments.
We select analytics platforms around the users, metrics and existing data ecosystem—not around dashboard-tool preference alone.
Business Intelligence & Visualization
Analytics Engineering
Semantic & Metrics Layer
Statistical & Predictive Analytics
Data Platforms
Cloud
Application Integration
Why Business Analytics Programs Fail
and How We Engineer Around It
Conflicting KPIs
Dashboard Overload
Low Data Trust
Slow Time to Insight
Insights Without Action
Low Adoption
AI Without Business Context
Measure Analytics by Decisions Improved
not Dashboards Delivered
Decision Speed
- Time from question to insight
- Time from exception to decision
- Manual analysis time
Metric Trust
- KPI consistency
- Reconciliation issues
- Metric disputes
- Data-quality exceptions
Analytics Adoption
- Active users
- Dashboard usage
- Self-service rate
- Repeat usage
Reporting Efficiency
- Manual reporting hours
- Report preparation time
- Duplicate reports
- Scheduled-report automation
Predictive Performance
- Forecast error
- Model accuracy
- Prediction stability
- Business usefulness
Operational Value
- Exception resolution
- Cycle-time improvement
- Decision completion
- Workflow efficiency
Business Outcomes: Revenue, Margin, Retention, Cost, Service levels, Conversion where the analytics initiative can legitimately be linked to those outcomes.
Define Your Analytics Success MetricsAI-Assisted Analytics
With Business Context
Generative AI can reduce the technical barrier between business users and enterprise analytics.
But natural-language analytics must remain grounded in trusted business definitions. Potential capabilities include:
Ask Questions in Natural Language
Automated Insight Summaries
Assisted Root-Cause Analysis
Forecast Exploration
Dashboard Copilots
Narrative Reporting
AI Controls & Governance
Do not connect a general-purpose LLM directly to production databases and assume the answers will be correct. AI analytics should include appropriate:
Business Analytics in Practice
Confidential Retail Enterprise
Business Question
What decision or reporting problem existed? Inventory visibility across channels was fragmented, leading to stockouts and excess inventory costs.
Existing Environment
Data isolated across ERP, e-commerce, and point-of-sale systems with manual spreadsheet reconciliation.
Analytics Solution
Unified semantic model for inventory metrics, real-time executive supply chain dashboard, and predictive demand forecasting models.
Governance & Adoption
Standardized metric definitions across all departments with role-based access for regional managers. Supported by analytics training programs.
Measured Outcome
- Reporting cycle reduced from weekly to near real-time.
- Manual reports eliminated.
- Decision cycle for inventory rebalancing reduced significantly.
Choose the Right
Analytics Starting Point
Enterprise buyers may be at very different levels of analytics maturity.
Analytics & BI Assessment
For organizations with fragmented reporting or unclear analytics priorities. Outcome: Analytics maturity assessment, KPI review, Architecture recommendations, Prioritized roadmap.
Executive Dashboard Program
For leadership teams requiring a governed view of organizational performance. Outcome: KPI framework, Semantic definitions, Executive dashboard, Governance model.
Self-Service BI Enablement
For organizations seeking to give business users more analytical independence. Outcome: Governed datasets, Reusable metrics, Role-based analytics, Training and adoption plan.
Predictive Analytics Pilot
For organizations ready to validate forecasting or predictive use cases. Outcome: Validated use case, Model evaluation, Business workflow recommendation.
Decision Intelligence Program
For organizations that need analytics integrated more directly with recurring business decisions. Outcome: Decision map, Analytics models, Scenario framework, Decision-support workflows.
Analytics Modernization
For organizations migrating legacy BI/reporting environments or rationalizing large report estates.
Embedded Analytics Engineering
For software and SaaS businesses adding analytics directly into customer or employee applications.
Managed Analytics Operations
For organizations requiring continuing dashboard, metric, model, adoption and analytics-platform support.
Frequently Asked Questions
Turn Business Data Into Decisions You Can Defend
Move beyond disconnected reports and dashboards. Mobiloitte can help you establish trusted KPIs, governed analytics and decision-support experiences that give leaders and teams a clearer view of performance—and the context required to act. Start with the decisions that matter. Define the metrics. Connect trusted data. Then build analytics around how people actually work.