Analytics and Business Intelligence

Turn Analytics and Business Intelligence Into Trusted, Decision-Ready Information

DataConsultant helps leadership, finance, operations, commercial and technology teams replace fragmented reporting with governed KPIs, reusable semantic models, role-relevant dashboards, self-service analytics and an operating model that keeps information reliable after launch.

Business questions translated into owned KPI definitions

Semantic models designed for reuse, control and consistency

Dashboards and reporting aligned to roles, actions and workflows

Testing, access, adoption and operating ownership built into scope

Discuss Your Analytics Requirement Review Service Scope

Final scope, timeline and commercial terms are confirmed after reviewing the decisions to support, source systems, metric complexity, user groups, platform landscape, controls and implementation responsibilities.

Decision Intelligence Workspace Illustrative model

Governed inputs

Finance and ERP data

CRM and commercial data

Operations and service data

Digital and external sources

Trusted meaning

Business entities and dimensions

Owned KPI and metric definitions

Quality and reconciliation rules

Security and access policies

Decision support

Executive scorecards

Operational dashboards

Alerts and exception analysis

Governed self-service

Metric ownershipNamed and documented

Release controlTested and traceable

AdoptionMeasured by role and use

Decision-led

Start with the decisions, actions and users the analytics capability must support.

Metric-governed

Define reusable business meaning, ownership and reconciliation before scale creates conflict.

Control-aware

Design access, privacy, quality, release and audit considerations into the BI lifecycle.

Adoption-focused

Connect dashboards and self-service to role needs, training, support and measurable usage.

When Reporting Exists but Decision Confidence Does Not

Analytics programmes often accumulate dashboards, extracts, calculations and tools faster than shared business meaning. The service is designed to identify where reporting friction comes from and what must change across metrics, data, experience, platform, governance and ownership.

Conflicting KPIs

Teams calculate revenue, margin, pipeline, service or operational measures differently.

Manual reporting

Analysts repeatedly extract, combine and validate data before every management cycle.

Dashboard sprawl

Duplicate reports and uncontrolled workspaces increase support cost and user confusion.

Low trust

Users challenge numbers because source lineage, definitions and reconciliation are unclear.

Weak adoption

Dashboards are technically available but do not match roles, workflows or decision habits.

Access complexity

Sharing, workspace and data-access decisions are difficult to govern consistently.

Semantic duplication

Measures and business logic are rebuilt across teams instead of reused from governed models.

Unclear ownership

No single route exists for metric approval, data issues, report changes or lifecycle decisions.

Current state

Reports built from local extracts and duplicated logic
KPI definitions vary by team, region or platform
Dashboard demand is prioritised by urgency rather than value
Quality issues are discovered after publication
Self-service expands without clear publishing boundaries
Support, change and retirement responsibilities are fragmented

Target state

Priority decisions mapped to owned measures and user journeys
Reusable semantic models provide consistent business meaning
Certified datasets and controlled self-service reduce duplicate work
Quality, security and reconciliation are part of acceptance
Report portfolios are rationalised around practical use
Ownership, release, support and improvement processes are explicit

Align the Questions Before Redesigning the Dashboards

Share the decisions your leaders and teams need to make, where reporting currently breaks down and which metrics cause the most disagreement. We can help define a practical starting scope.

Discuss Decision Priorities See the KPI Workflow

What the Analytics and Business Intelligence Service Actually Covers

The engagement connects business decision design with data meaning, BI architecture, dashboards, controls and adoption. It can be advisory-only, implementation-led or structured to transition into ongoing operations.

Business intelligence as an operating capability, not a dashboard request queue

DataConsultant structures BI around the chain from business question to metric definition, governed data, semantic model, user experience, acceptance evidence and ongoing ownership. This makes it possible to distinguish a reporting symptom from the underlying issue in data quality, modelling, governance, platform design or process.

Primary purposeTrusted decision support

Core design objectGoverned business meaning

Operational outcomeUsable, supportable analytics

Questions and Decisions the Engagement Can Support

A useful BI programme identifies the management decisions that matter before choosing charts. These examples show the type of decision context that can shape requirements and acceptance criteria.

Executive performance

Are we on plan, and where does leadership need to intervene?

Connect financial, customer, operational, workforce, risk and delivery measures to accountable actions.

Typical output: executive KPI framework

Finance

What is driving variance, margin and cash performance?

Structure consistent dimensions, allocation logic, time intelligence and drill paths across planning and actuals.

Typical output: finance semantic model

Commercial

Which channels, customers and opportunities need attention?

Align pipeline, conversion, acquisition, retention, pricing and revenue measures with commercial workflows.

Typical output: commercial performance dashboard

Operations

Where are capacity, throughput, quality or service exceptions emerging?

Design monitoring around thresholds, bottlenecks, root-cause paths and ownership rather than static status reporting.

Typical output: operations control view

Customer

Where are customer journeys, service outcomes or retention changing?

Combine customer, transaction, digital and service signals with governed definitions and privacy-aware access.

Typical output: customer analytics model

Analytics operations

Which reports, models and licences should we improve, consolidate or retire?

Use inventory, usage, performance, ownership and dependency evidence to control BI estate growth.

Typical output: rationalisation backlog

Service Scope Across Strategy, Semantic Design, Delivery and Adoption

Scope is assembled around the business need rather than a predetermined tool. The four workstreams below can be combined or separated depending on maturity and implementation responsibilities.

Strategy & governance

Define priorities, ownership and the controls that keep reporting coherent.

Analytics and BI strategy
Decision and KPI framework
Metric ownership
Demand prioritisation
Report lifecycle
Operating model
Centre-of-excellence design
Adoption measures

Information & experience

Translate business questions into understandable information products.

Metric catalogue
Semantic-model design
Dashboard information architecture
Executive scorecards
Role-based user journeys
Alerts and exception views
Self-service patterns
Accessibility considerations

Engineering & platform

Build the technical components needed for reliable analytical delivery.

Data marts and transformations
Semantic model implementation
Refresh orchestration
Security configuration
Performance optimisation
Deployment pipelines
Environment design
Usage telemetry

Assurance & adoption

Validate results, prepare users and establish a sustainable support model.

Data reconciliation
Functional and performance testing
Release assurance
Training and enablement
Usage monitoring
Support transition
Enhancement governance
Continuous improvement

Define the Semantic Layer Before Self-Service Multiplies the Same Metric

A governed meaning layer can reduce repeated logic across reports while making ownership, access, quality and change easier to manage.

Scope Metric Governance Review Semantic Design

From Business Question to Governed Dashboard

This workflow keeps requirements connected to business meaning and acceptance evidence instead of starting with a visualisation tool.

Decision

Identify who needs to decide, act or monitor.

Question

Define the question and the decision threshold.

KPI

Agree formula, dimensions, owner and refresh need.

Semantic model

Create reusable entities, measures and access rules.

Experience

Design dashboard, drill path, alert or self-service route.

Evidence

Reconcile, test, approve, publish and monitor use.

Semantic and Metric Governance for Consistent Business Meaning

A semantic layer is useful only when technical modelling and business accountability reinforce each other. The design can cover reusable measures, shared dimensions, quality expectations, security and controlled change.

ERP / finance

CRM / customer

Operations

Governed semantic and metric layer

Business entitiesMeasuresDimensionsCalculation logicOwnershipAccessQuality rulesLineageChange control

Executive BI

Operational BI

Self-service

Metric ownership and approval

Assign accountable business owners, approval routes and documented definitions for material KPIs.

Lineage and reconciliation

Connect measures to source logic, transformations, exceptions and acceptance evidence.

Access and publishing controls

Define roles, workspace responsibilities, sensitive-data boundaries and controlled publishing paths.

Self-service with support boundaries

Separate reusable certified assets from exploratory analysis and define escalation when business risk is higher.

Common Analytics and BI Use Cases

The exact use case determines the right combination of business definition, data engineering, semantic modelling, visual design, controls and adoption support.

Leadership

Executive performance management

Create a concise view of financial, customer, operational, workforce, risk and transformation performance.

Trigger: Board reporting conflict
Output: Executive scorecard

Finance

Planning and profitability analytics

Unify budget, actual, margin, cost and cash measures with consistent dimensions and drill-down logic.

Trigger: Manual month-end reporting
Output: Finance semantic model

Commercial

Sales and marketing intelligence

Connect pipeline, conversion, acquisition, campaign, customer and revenue-performance measures.

Trigger: Fragmented funnel metrics
Output: Commercial analytics suite

Operations

Operational exception monitoring

Expose capacity, throughput, inventory, quality, fulfilment, service and productivity exceptions.

Trigger: Slow issue detection
Output: Operational control dashboard

Modernisation

BI migration and rationalisation

Reduce duplicate reports, map dependencies, redesign semantic assets and plan controlled decommissioning.

Trigger: Legacy BI transition
Output: Migration backlog

Self-service

Governed analytics enablement

Provide certified data assets, reusable models, role-based publishing rules, training and support pathways.

Trigger: Uncontrolled extracts
Output: Self-service operating model

Deliverables Designed for Acceptance, Reuse and Ongoing Ownership

Deliverables are tailored to the scope. A useful output should state assumptions, dependencies, owners, controls and acceptance criteria clearly enough for internal teams to act on it.

DELIVERABLE 01

Current-state assessment

Evidence-based view of reporting, metrics, data, platform, governance, performance, adoption and ownership gaps.

DELIVERABLE 02

Decision & KPI framework

Priority questions, measures, formulas, dimensions, owners, thresholds and refresh expectations.

DELIVERABLE 03

Semantic-model blueprint

Reusable entities, measures, dimensions, access, lineage, quality and lifecycle responsibilities.

DELIVERABLE 04

Dashboard portfolio

Prioritised role-based dashboards, wireframes, navigation, alerts, drill paths and publishing requirements.

DELIVERABLE 05

Target BI architecture

Source-to-consumption flow, semantic layer, environments, security, deployment, monitoring and dependencies.

DELIVERABLE 06

Quality & test evidence

Reconciliation rules, functional checks, performance criteria, issue records, acceptance and known limitations.

DELIVERABLE 07

Governance & operating model

Roles, publishing controls, access review, support routes, change process, escalation and lifecycle decisions.

DELIVERABLE 08

Roadmap & backlog

Sequenced work packages, priorities, dependencies, risks, resource needs and mobilisation actions.

Define BI Deliverables That Can Be Reconciled, Accepted and Operated

Clarify what must be designed, what must be built, what your team will approve and what ownership is required after release.

Request a Scope Review See Delivery Stages

A Delivery Path From Discovery to Sustainable Analytics Use

The sequence is adapted to the engagement, but the work should maintain traceability from business need through design, implementation, testing and operating ownership.

Stage 1

Discover

Confirm decisions, users, priorities, pain points, constraints and success measures.

Stage 2

Assess

Review reports, metrics, data, platforms, controls, performance, cost and adoption evidence.

Stage 3

Design

Define KPIs, semantic meaning, user journeys, architecture, security and acceptance criteria.

Stage 4

Build

Implement agreed models, data products, dashboards, controls and deployment components.

Stage 5

Validate

Reconcile data, test functions and performance, resolve findings and record limitations.

Stage 6

Adopt & operate

Train users, transition support, monitor use and quality, and govern improvements.

What We Need From Your Team to Make BI Decisions Defensible

Good analytics delivery depends on access to the people who own business meaning and the evidence needed to validate data, platform and control assumptions.

Business prioritiesDecision questions, reporting obligations, pain points and target outcomes.

Metric ownersPeople who can approve KPI formulas, dimensions, thresholds and interpretation.

Report inventoryCurrent dashboards, extracts, usage data, schedules and known duplication.

Source systemsERP, CRM, data platforms, files, APIs, history, latency and known data issues.

Architecture contextData flows, environments, integrations, gateways, modelling and deployment practices.

Controls & policiesSecurity, privacy, access, retention, audit, residency and release requirements.

User groupsLeadership, analysts, operational users, creators and support stakeholders.

ConstraintsTarget dates, licences, skills, vendor contracts, budget, dependencies and change windows.

Platform-Aware Delivery Without Making the Tool the Strategy

Analytics and BI can span reporting products, cloud data platforms and governance controls. Recommendations should reflect existing investments, user needs, security, scale, skills, cost and operating responsibilities.

BI & reporting

Dashboard, reporting and analytical-consumption platforms.

Microsoft Power BITableauQlikLooker

Data foundations

Warehouses, lakehouses, relational data and governed analytical stores.

Microsoft FabricCloud warehousesLakehousesData marts

Semantic & quality controls

Reusable models, metric ownership, reconciliation and lineage practices.

Semantic modelsMetric cataloguesData qualityLineage

Lifecycle & access

Controlled publishing, deployment, security, monitoring and change.

Role-based accessRelease controlsUsage telemetrySupport workflows

Turn BI Release Into a Governed Operating Model

Dashboards become business-critical assets. Define ownership, access, quality, release, support and retirement responsibilities before the estate becomes harder to control.

Discuss BI Governance Review Operating Measures

Measure Analytics Health With Evidence, Not Dashboard Counts

The right scorecard depends on the operating model. The example below shows dimensions that can be baselined during discovery and tracked after implementation without assuming a guaranteed improvement.

Dimension	Question to answer	Evidence	Possible action
Metric consistency	Do material reports use approved definitions?	Definition register, reconciliation exceptions, certified-model coverage	Standardise and assign ownership
Data reliability	Are refreshes and quality exceptions visible?	Refresh logs, quality checks, incidents, lineage	Improve monitoring and escalation
Performance	Do reports respond within usable operational expectations?	Query duration, model size, capacity and usage evidence	Tune models and workload design
Adoption	Are target roles using the intended information?	Active usage, repeat use, task completion, feedback	Redesign journeys or enable users
Governance	Are owners, workspaces and releases controlled?	Ownership register, access reviews, release records	Sustain controlled operating cadence
Portfolio value	Which assets should be improved, consolidated or retired?	Usage, duplication, support demand, business criticality	Prioritise rationalisation backlog

Illustrative assessment dimensions only. Final measures, thresholds and actions depend on the client’s business context, platform and agreed service scope.

Custom Analytics and BI Pricing Based on the Work You Actually Need

DataConsultant does not publish a fixed fee for this service. Public BI consulting offers in India vary too widely in scope to state a reliable enterprise market range for this page, so pricing is handled through a scoped proposal rather than an unsupported numeric estimate.

Focused review

Analytics & BI Assessment

For organisations that need an evidence-based view of reporting, metric, platform, governance or adoption gaps before committing to change.

Commercial modelRequest a Quote

Defined assessment questions
Evidence request and stakeholder review
Prioritised findings
Recommended next-step backlog

Scope an Assessment

Defined delivery

BI Design & Implementation

For a bounded programme with agreed KPI, semantic, dashboard, migration, testing or governance deliverables.

Commercial modelRequest a Quote

Defined deliverables and acceptance
Design and implementation responsibilities
Testing and release evidence
Documentation and handover

Request a Scoped Proposal

Embedded capacity

Specialist BI Support

For internal teams that need targeted architecture, semantic modelling, analytics engineering, dashboard, QA or governance capability.

Commercial modelRequest a Quote

Role and responsibility definition
Client-led or shared backlog
Governed delivery standards
Knowledge transfer

Discuss Specialist Support

Operational transition

Managed BI Preparation

For teams preparing dashboards, models and reporting operations for ongoing monitoring, controlled change and support.

Commercial modelRequest a Quote

Service boundary and asset inventory
Runbooks and support ownership
Monitoring and change controls
Improvement backlog

Plan the Transition

What affects cost and timeline: business domains, stakeholders, source systems, data quality, metric complexity, report and dashboard volume, semantic-model depth, migration requirements, user roles, security and privacy controls, platform environments, testing, documentation, training, onsite needs and ongoing support. Third-party software, cloud and licence charges are separate unless the proposal explicitly includes them. Timeline is confirmed after scoping.

Choose the Right Starting Point for Your Reporting Problem

Not every reporting issue needs a full analytics and BI programme. The first discussion should determine whether the requirement is primarily about decision design, data quality, architecture, engineering, platform operations or another specialist service.

Good fit for this service

Leadership lacks a consistent view of performance across functions or business units.
Metric definitions conflict across reports, teams or platforms.
Dashboard and report portfolios need rationalisation or redesign.
A semantic layer or governed self-service model is required.
BI migration must preserve business meaning and acceptance evidence.
Analytics adoption, support and lifecycle ownership are unclear.

A different or adjacent service may be better

A single source-system defect needs direct data-quality remediation.
The primary requirement is enterprise data-platform architecture or engineering.
The need is predictive modelling, machine learning or advanced data science rather than BI.
Only ongoing monitoring and support is required for an already stable BI estate.
The request is for legal advice, statutory audit, certification or penetration testing.
The scope is temporary staffing without an analytics consulting or delivery objective.

Choose the BI Engagement Around the Decision, Not a Prebuilt Package

Share your users, current tools, priority KPIs, source systems and target outcomes. We can separate advisory, implementation, migration, assurance and managed-support responsibilities before pricing.

Request Analytics & BI Pricing Explore Related Services

Why DataConsultant Approaches BI as a Connected Business Capability

The service sits across business priorities, data, architecture, governance, analytics delivery and operating support, helping teams address the reason reporting fails rather than only redesigning the front end.

Business questions first

Requirements start with decisions, users and actions so metrics and dashboards have a defined purpose.

Governed meaning before scale

Metric definitions, semantic models and ownership are treated as reusable assets rather than report-specific logic.

Controls within delivery

Quality, access, release, privacy and support considerations are connected to design and acceptance.

Knowledge transfer and operation

Documentation, training and ownership are included where needed so the capability can be sustained after implementation.

Analytics and Business Intelligence Questions From Enterprise Buyers

These answers cover scope, platforms, governance, deliverables, client inputs, timeline, commercial treatment and ongoing support.

What is analytics and business intelligence consulting?

Analytics and business intelligence consulting helps an organisation turn business questions and operational data into governed measures, reusable semantic models, dashboards, reports, alerts and analysis that support real decisions. Scope can include assessment, KPI design, information modelling, BI architecture, dashboard design and build, testing, governance, self-service enablement, adoption and operating support.

What is included in DataConsultant’s Analytics and Business Intelligence service?

The service can include decision and stakeholder discovery, current-state reporting assessment, KPI and metric definition, source-data review, semantic-model design, dashboard and report portfolio planning, BI architecture, security and access design, data-quality checks, testing, release controls, training, adoption planning, rationalisation and managed-support transition. Final responsibilities are confirmed during scoping.

Who should sponsor an analytics and BI engagement?

Sponsors commonly include CFOs, COOs, CIOs, CTOs, chief data officers, analytics leaders, finance directors, operations leaders, commercial leaders and business-unit heads. Delivery normally also needs metric owners, subject-matter experts, data engineering, architecture, security, privacy, platform administration and representative end users.

Can you help when different dashboards show different KPI values?

Yes. The engagement can trace conflicting definitions to formulas, filters, hierarchies, time logic, source systems and transformation rules, then define owned metric specifications and reconciliation criteria. Resolving the issue may also require data-quality or engineering work when the source data itself is inconsistent.

Do you only work with Power BI?

No. The service is requirements-led and can consider environments that use Microsoft Power BI, Tableau, Qlik, Looker and other enterprise reporting tools, together with warehouses, lakehouses, databases and transformation platforms. Product selection, licensing and architecture are assessed against the client environment rather than assumed in advance.

Can the service include a semantic layer or governed metric model?

Yes. Scope can include business entities, dimensions, measures, calculation logic, ownership, certification, reuse, lineage, access, versioning and change controls. The objective is to reduce repeated metric logic across reports and provide a clearer governed meaning layer for analytics consumers.

Can DataConsultant support self-service analytics?

Yes. Self-service enablement can include governed datasets, reusable semantic models, workspace and publishing standards, templates, training, data literacy, access controls, support routes, usage monitoring and escalation for higher-risk or more complex analysis.

How are data quality, privacy and security handled?

The engagement can identify critical data dependencies, quality rules, reconciliation needs, access boundaries, sharing controls, sensitive-data handling, retention or residency constraints, audit requirements and ownership. The service supports appropriate design and readiness but does not imply legal advice, statutory audit, certification or penetration testing unless separately commissioned.

Can you migrate or rationalise legacy reports and dashboards?

Yes. Migration can include inventory, dependency and usage analysis, duplicate-report rationalisation, target definitions, redesign, conversion planning, parallel validation, cutover, training and controlled decommissioning. Complex legacy logic may need redesign rather than direct automated conversion.

What deliverables can we expect?

Typical outputs can include a current-state assessment, decision and KPI framework, metric catalogue, semantic-model blueprint, target BI architecture, dashboard and report portfolio, wireframes or implemented assets where build is in scope, quality and reconciliation rules, governance model, testing evidence, adoption plan, operating model and prioritised implementation backlog.

How long does an analytics and BI engagement take?

Timeline is confirmed after scoping. It depends on the number of business domains, users, source systems, reports, KPIs, data-quality issues, platform complexity, security requirements, implementation depth, testing cycles, migration needs, stakeholder availability and whether ongoing support is included.

How is Analytics and Business Intelligence pricing calculated?

DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and confirmed through a Request a Quote process after the required outcomes, data sources, business domains, KPI and dashboard volume, platform landscape, implementation responsibilities, testing, governance, training, migration and support requirements are understood. Third-party software, cloud and licence costs are treated separately unless explicitly included in the proposal.

What information should we prepare before the first discussion?

Useful inputs include priority business questions, current reports and dashboards, KPI definitions, source-system information, data-flow or architecture diagrams, known data-quality issues, user groups, platform and licence context, security or privacy constraints, adoption concerns, target dates and the people who can approve business definitions.

Can DataConsultant provide ongoing BI support after implementation?

Yes. Ongoing support can be scoped separately for monitoring, refresh failures, access requests, semantic-model maintenance, minor enhancements, release coordination, data-quality escalation, usage reporting, rationalisation and continuous improvement. Service boundaries and any service levels must be agreed before operations begin.

Analytics & BI Enquiry

Request an Analytics and BI Scope Review

Share your contact details and requirement. DataConsultant can review the likely workstreams, evidence, stakeholder involvement and appropriate commercial next step.