Analytics that answers the question asked, not the report available
Eighty-seven percent of organisations have low BI and analytics maturity, not because they lack tools but because analytics is built on data foundations that were never governed. BCS builds analytics and BI capabilities on deKorvai-governed data foundations, so business decisions are made on current, validated data.
of organisations have low business intelligence and analytics maturity
of organisations run four or more BI tools simultaneously
of finance teams still rely on spreadsheets as primary planning and reporting tools
Three things BCS does before every other consultancy starts building
Most delivery programmes begin at the solution layer. BCS begins at the evidence layer, measuring what exists before proposing what to build. That sequence is what separates recommendations with a measurable outcome from plans that look credible at presentation and fail in execution.
Measure before designing
deKorvai quality baseline established before any architecture decision is made. Every recommendation is grounded in measured evidence from the current data estate, not assumptions from stakeholder interviews.
Automate from the first sprint
Symphony automation scope identified and embedded in the delivery roadmap during the engagement itself, not proposed as a separate follow-on programme after delivery concludes.
Govern from day one
Anugal access governance and data classification policies are designed as part of the solution architecture and active from the first production dataset, not retrofitted after the platform is in use.
4 reasons analytics investment does not change how the business decides
87% of organisations have low BI maturity, not because they lack BI tools but because analytics is built on data foundations that were never governed. The reports exist; the decisions do not follow.
Reports disagree because there is no single data model
When sales, finance, and operations each query the same underlying data through different BI tools with different transformation logic, reports produce different numbers for the same business question. Business users stop trusting analytics and maintain their own spreadsheets to produce numbers they can defend. The BI investment runs in parallel with the spreadsheet estate, not instead of it.
Analytics is built on data that was never quality-validated
BI development begins against source data extracts without profiling or validation. Reports appear to run correctly but produce results that diverge from source system totals in ways discovered months later during an audit or budget review. deKorvai quality validation on the data layer is the prerequisite for analytics the business can act on, not an optional enhancement.
Self-service BI becomes a shadow IT problem without governance
BI platforms marketed as self-service create shadow analytics: individual analysts build personal workbooks and publish reports without review. Definitions diverge, metrics are calculated inconsistently, and the organisation accumulates a library of conflicting reports that cannot be reconciled. Self-service requires a governed semantic layer and a defined metric catalogue to produce consistent results at scale.
AI models trained on ungoverned data produce unreliable predictions
Organisations investing in AI-augmented analytics expect predictive models to improve decisions. Models trained on data that was never profiled, validated, or governed reproduce the biases and quality failures of the training data in every prediction. The governance framework deKorvai enforces on the analytics data foundation is the prerequisite for reliable AI, not a separate data quality initiative.
What the analytics and BI engagement delivers
One version of every business metric, across every tool
A governed semantic layer ensures finance, sales, and operations query identical metric definitions through any BI tool, retiring the spreadsheet estate built to reconcile conflicting reports.
Business users answer their own questions without IT queues
Analysts build reports within governed guardrails on the certified data model, answering new business questions in hours rather than waiting on the next sprint cycle.
Decisions made on current data, not last week's report
deKorvai detects pipeline freshness failures and triggers resolution before the business encounters stale figures in reports or board presentations.
AI models trained on enterprise data that is certified clean
Models trained on the deKorvai-governed foundation produce reliable predictions because the training data has been validated against defined quality rules, not accepted as-is from ungoverned sources.
SAP Analytics Cloud and cloud BI from a single delivery team
One BCS team covers SAP and cloud analytics layers with consistent metric definitions and governance, removing the coordination cost of managing separate delivery streams.
BI estate rationalised from multiple tools to a governed platform
A rationalisation assessment and migration path to a governed analytics platform reduces licence cost, maintenance overhead, and report proliferation simultaneously.
How BCS builds analytics the business relies on
BCS analytics engagements begin with the data foundation, not the dashboard. Governance, quality validation, and a governed semantic layer are established before any reporting is built, so analytics produces consistent results from day one.
Foundation Assessment
Map business analytics requirements against the existing data foundation. Identify which questions are unanswerable, which reports conflict, and what data quality gaps prevent reliable analytics. deKorvai profiles the analytics data sources before design begins.
Semantic Layer Design
Define metric calculations, dimension hierarchies, and business rules in a governed semantic layer agreed with the business. The metric catalogue documents how every key measure is calculated, which source fields contribute, and which business rules apply.
Pipeline Build with Quality Gates
Build analytics data pipelines with deKorvai quality validation at each transformation step. Freshness monitoring confirms analytics refreshes on the agreed cadence. Quality failures are detected and resolved before business users encounter stale or incorrect reports.
BI Development
Dashboards, reports, and self-service workspaces built on the governed semantic layer using Power BI, Tableau, SAP Analytics Cloud, or Looker. Self-service workspaces configured with governance guardrails preventing the shadow analytics problem.
AI and Advanced Analytics
Where scope includes predictive analytics, models are built on the deKorvai-governed data foundation. AI outputs are surfaced in BI dashboards with confidence indicators and data lineage visible to business users.
What BCS delivers across analytics and BI programmes
BCS analytics and BI capabilities span governed data modelling through to AI-augmented analytics, covering SAP Analytics Cloud and hyperscaler BI platforms from a single engagement team.
Semantic Layer and Metric Catalogue
Governed semantic layer design with a defined metric catalogue covering KPIs, financial measures, operational metrics, and customer analytics. Consistent metric definitions across all BI tools eliminate report conflicts and provide the single version of truth that self-service BI requires.
Power BI and Tableau Development
Enterprise-grade Power BI and Tableau implementations with governed data models, row-level security, deployment pipelines, and self-service workspace configuration. Report development follows semantic layer definitions, ensuring consistent metrics regardless of which analyst built the report or which workspace it is published to.
SAP Analytics Cloud Implementation
SAP Analytics Cloud story design, planning model configuration, and live connection setup for SAP BW, S/4HANA, and SAP Datasphere. SAP AC planning, consolidation, and predictive capabilities configured alongside hyperscaler BI tools, with consistent metric definitions applied across both environments.
deKorvai Analytics Pipeline Monitoring
Continuous deKorvai quality monitoring on every analytics data pipeline. Freshness failures, metric calculation anomalies, volume drops, and referential integrity breaks are detected before business users encounter incorrect dashboards. Analytics quality is an operational discipline, not a periodic manual check.
Self-Service BI Governance and Enablement
Self-service capability design with governance guardrails: workspace certification policies, dataset endorsement processes, usage monitoring, and analyst training programmes. Business users build their own analyses without creating the shadow analytics estate that ungoverned self-service generates.
Financial Analytics and FP&A Modernisation
Finance analytics migration from spreadsheet-based FP&A to governed planning models on SAP Analytics Cloud or cloud BI platforms. Budget, forecast, and variance analysis built on validated financial data with role-based access controls separating planning inputs from published results.
Machine Learning and Predictive Analytics
Predictive model development on deKorvai-governed enterprise data using Azure ML, AWS SageMaker, GCP Vertex AI, or Databricks MLflow. Models are built on validated training data, monitored for data drift and performance degradation, and surfaced in BI dashboards with lineage and confidence indicators visible to business consumers.
BI Estate Rationalisation
Assessment of the existing BI tool estate and report inventory, with a rationalisation roadmap consolidating multiple platforms onto a governed analytics layer. Report migration, decommission planning, and transition management for organisations carrying the maintenance overhead of four or more parallel BI platforms.
Data Lineage and Analytics Auditability
End-to-end data lineage from source system through transformation pipeline to dashboard metric, with deKorvai lineage tracking confirming which source records contribute to which published figures. Finance directors and auditors can trace any reported number back to its source data, with transformation rules and quality validation steps documented in the lineage record.
The platforms embedded in every analytics and BI programme BCS delivers
Agentic Pipeline Orchestration
Symphony
Symphony automates analytics pipeline scheduling, report distribution, and dashboard refresh cycles that currently run on manual triggers. Data products reach business users on schedule without operations teams managing the delivery workflow.
- Automated analytics pipeline scheduling eliminating manual trigger dependencies
- Report distribution workflow execution across business unit delivery schedules
- Dashboard refresh orchestration with dependency-aware sequencing
- Alert-triggered pipeline remediation for failed or stale datasets
AI Decision Intelligence
deKorvai
deKorvai surfaces predictive analytics, demand signals, and decision intelligence directly inside the BI layer. Business users access forward-looking recommendations alongside historical reporting without switching to separate intelligence tools.
- Predictive analytics embedded within existing BI tools and dashboard layers
- Demand and risk signal generation from operational and transactional data
- Decision intelligence surfaced at the point of reporting consumption
- AI-ready data scoring to identify datasets with predictive capability
Data Access Governance
Anugal
Anugal governs access to analytics environments, datasets, and BI tools at the user and role level. Sensitive data is masked or restricted based on classification, not left to manual access request processes.
- Role-scoped access control for BI tools, datasets, and analytics environments
- Sensitive data masking and restriction enforcement at the consumption layer
- Data classification-driven access policy enforcement without manual approvals
- Audit trail generation for every dataset access and report export action
What makes BCS different from every other analytics consultancy
BCS has delivered analytics and BI programmes for enterprise clients across manufacturing, financial services, healthcare, and retail for over ten years. Every engagement begins with the data foundation and deKorvai quality validation, not with the dashboard, which is why analytics built by BCS stays trusted after delivery.
Data foundation established before dashboard development begins
deKorvai quality baseline is set, the semantic layer is defined, and the metric catalogue is agreed with the business before a single report is built. Dashboards delivered at the end of the programme run on validated foundations.
One metric definition, consistent across every BI tool
The governed semantic layer contains the single definition of every business metric, referenced by all BI tools in the estate. Metric consistency is structural, not a process that needs enforcing through report review cycles.
SAP Analytics Cloud and hyperscaler BI from a single team
Organisations running SAP alongside Azure, AWS, or GCP receive consistent analytics delivery from one BCS team with the same metric catalogue and deKorvai monitoring applied across both environments.
Self-service analytics that stays governed at scale
BCS self-service BI design includes certification policies, dataset endorsement workflows, and analyst enablement within governed boundaries. Organisations accumulate a governed report estate, not a library of uncertified workbooks.
Analytics capability transferred to the business
BCS analytics programmes transfer capability to the client organisation: analyst enablement, semantic layer documentation, and pipeline runbooks are handed over as part of the engagement.
Ready to build analytics the business trusts?
BCS analytics engagements begin with a deKorvai data foundation assessment before any dashboard development starts. Book an initial analytics assessment to understand the current state of the data and reporting estate.