Data, Reporting & Analytics

Service introduction

SAP Data and Analytics Services for Trusted Reporting and Enterprise Planning

Reporting problems are almost never dashboard problems. Two teams quote different revenue figures because the definition of revenue was never agreed and nobody owns it; the close slips because the numbers arrive in spreadsheets; a forecast is treated as a guess because the data behind it cannot be traced. VISCAP works below the dashboard layer — settling KPI definitions and ownership, connecting SAP and non-SAP data into governed models, and building the reporting, planning and consolidation on top of them. Across SAP Analytics Cloud, SAP Datasphere, SAP Business Data Cloud, SAP S/4HANA embedded analytics and Group Reporting, and existing SAP BW estates, what you should get back is a number the business trusts, a plan it can act on, and a data foundation your next AI use case can stand on.

What VISCAP delivers

What VISCAP delivers through SAP Data, Reporting & Analytics

Data and analytics strategy assessment

We start from the decisions the business is trying to make, then work backwards: the reports and dashboards actually in use, the spreadsheets quietly holding the rest together, the sources behind them, and the KPI definitions nobody has reconciled. What comes out is a target architecture and a roadmap ordered by priority rather than by tool.

Content scope
  • Business reporting requirements
  • Executive and operational reporting needs
  • Current report inventory
  • Current data landscape
  • SAP and non-SAP data sources
  • KPI definitions
  • Reporting ownership
  • Planning requirements
  • Financial consolidation requirements
  • Data-quality issues
  • Manual reporting processes
  • Spreadsheet dependency
  • Existing SAP BW landscape
  • Existing analytics tools
  • Data-access requirements
  • Security and authorization requirements
  • Target analytics architecture
  • Implementation roadmap
Connected solutions

SAP Solutions Supporting Data, Reporting, Planning and Analytics

SAP Business Data Cloud

A governed data and analytics foundation that connects SAP and third-party data while keeping its business context — the basis for data products, and for analytics and AI use cases that need to be trusted.

Data productsBusiness contextGoverned foundationAI use cases

SAP Analytics Cloud

Business intelligence, dashboards, enterprise planning, forecasting and predictive analytics in one place — so the plan and the report it is judged against are never built on different numbers.

Dashboards and storiesIntegrated planningPredictive analyticsCollaboration

SAP Datasphere

Data integration, semantic modelling, sharing and governance across SAP and third-party landscapes — the layer that turns raw tables into business data anyone in the organization can query without a translator.

Data integrationSemantic modelsData sharingGovernance

SAP S/4HANA Finance for Group Reporting

Financial consolidation on the same data the core already holds — group close, currency translation, intercompany processing, consolidation entries and consolidated statements, with the audit trail intact.

Group closeCurrency translationIntercompany processingConsolidated statements

SAP S/4HANA Embedded Analytics

Operational reporting where the work happens — analytical queries, SAP Fiori analytical apps, KPIs and multidimensional analysis inside S/4HANA, on live transactional data rather than yesterday’s extract.

Analytical queriesFiori analytical appsKPI tilesMultidimensional analysis

SAP BW and SAP BW/4HANA

Enterprise data warehousing, governed historical reporting and the complex models a business has built up over years — kept running while they are modernized, rather than switched off and rebuilt from memory.

Data warehousingHistorical reportingData-model simplificationProgressive modernization
Delivery approach

A Structured Path From Reporting Requirements to Trusted Business Insights

5 stages, each with a defined exit and an outcome the next depends on.

Scroll for all 5 stages

01 / 05

Assess

Identify the decisions the business needs to make, review the reports, dashboards, planning models and spreadsheets in use, and assess SAP and non-SAP sources, KPI definitions, data quality, manual reconciliation and access — then name the critical gaps.

  • Identify business and executive decisions
  • Review reports, dashboards, planning models, and spreadsheets
  • Assess SAP and non-SAP data sources
  • Review KPI definitions
  • Identify manual activities and reconciliation
  • Assess data quality
  • Review security and access requirements
  • Review the existing analytics architecture
  • Identify critical gaps and priorities
02 / 05

Design

Define reporting personas and use cases, agree KPI definitions and data models, choose the SAP analytics capabilities and connection type per case, then establish planning, consolidation, security, governance and ownership behind confirmed releases.

  • Define reporting personas and use cases
  • Establish KPI definitions
  • Define data models and semantic requirements
  • Select appropriate SAP analytics capabilities
  • Define live, replicated, or imported connection requirements
  • Establish planning and consolidation architecture
  • Define security and access
  • Establish governance and ownership
  • Confirm implementation releases
03 / 05

Build

Configure the data connections, build or extend the models, apply transformations and business rules, then create the dashboards, planning models, group reporting and embedded analytics — with security roles and ownership documented.

  • Configure data connections
  • Build or extend data models
  • Apply transformations and business rules
  • Create dashboards and stories
  • Build planning models
  • Configure workflows and actions
  • Configure group reporting requirements
  • Develop embedded analytics
  • Prepare security roles
  • Document models, reports, and ownership
04 / 05

Validate and Adopt

Reconcile source against target, validate KPI calculations, planning logic and consolidation rules, test reports, performance and role-based access, then take users through acceptance testing and train consumers, planners and administrators.

  • Reconcile source and target data
  • Validate KPI calculations
  • Test reports and dashboards
  • Validate planning logic
  • Test consolidation rules
  • Conduct performance testing where required
  • Validate role-based access
  • Support user acceptance testing
  • Train report consumers, planners, and administrators
  • Prepare production deployment
05 / 05

Govern and Improve

Monitor data loads and connections, review usage and retire redundant reports, manage KPI changes and data quality, maintain planning models, support close cycles and tune performance — keeping the foundation ready for SAP Business AI.

  • Monitor data loads and connections
  • Review report usage
  • Retire redundant reports
  • Manage KPI changes
  • Review data quality
  • Maintain planning models
  • Support close and consolidation cycles
  • Optimize dashboard performance
  • Govern enhancement demand
  • Prioritize additional use cases
  • Transition into AMS where required
  • Align analytics with SAP business AI readiness

Industries

Manufacturing

Analytics across production, procurement, inventory, quality, maintenance, plant performance, cost and management reporting

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Consumer Products and FMCG

Reporting and planning across demand, sales, distribution, pricing, inventory, channels and profitability

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Pharmaceuticals and Life Sciences

Governed analytics across manufacturing, quality, batch traceability, compliance-sensitive reporting and supply-chain performance

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Chemicals

Analytics across process manufacturing, materials, quality, yield, plant operations, cost and environmental information

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Professional Services

Reporting and planning across project performance, utilization, resources, time, billing and profitability

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Engineering, Construction and Projects

Analytics across project cost, revenue, procurement, materials, subcontractors, progress, cash flow and portfolio performance

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Case study

Five modules restructured for Cloud4C Services

A landscape revamp with IAS/IPS, single sign-on and Story Reports — 88% HR process automation across the environment.
Case study

From 5 days to 3 hours for Cadila Pharmaceuticals

Payroll processing cut from five days to about three hours, with four SuccessFactors capability areas added along the way.
Insight

Why two dashboards disagree on the same KPI

Reconciling numbers is rarely a data problem. It is usually two teams using the same word for different measures, and nobody owning which one is right.
Insight

SAP Business Data Cloud, Datasphere and Analytics Cloud: what does what

Three products, overlapping names, and a lot of confusion about which one owns the semantic layer. A plain read of where each fits.

Frequently Asked Questions

Everything between a source system and a decision: data and analytics strategy assessment, data integration, modelling and governance, business intelligence, dashboards and enterprise reporting, enterprise planning, budgeting and forecasting, financial consolidation and group reporting, and the modernization of an existing SAP BW or analytics landscape — along with the KPI definitions, security model and ownership that keep the output trustworthy afterwards.

SAP’s cloud analytics application, combining business intelligence, enterprise planning and predictive analytics in a single tool. In practice that means dashboards and stories, driver-based budgeting and forecasting with versions, workflows and approvals, predictive forecasting, and collaborative decision support — with planning and reporting sitting on the same models, so a forecast and the actuals it is compared against cannot drift apart.

SAP’s governed data and analytics foundation. It brings SAP and third-party data together while preserving the business context — the semantics, hierarchies and relationships that get lost when tables are copied into a generic lake — and exposes it as managed data products. That context is what makes analytics defensible and what SAP Business AI use cases depend on.

They sit at different layers. SAP Datasphere is where data is integrated, modelled semantically, governed and shared. SAP Business Data Cloud is the wider governed foundation that connects SAP and non-SAP data with its business context and turns it into data products for analytics and AI. SAP Analytics Cloud is the consumption layer — dashboards, reporting, planning and prediction. Most landscapes use them together; the mistake is buying the consumption layer and expecting it to solve a modelling problem.

Yes — almost every reporting requirement crosses that line. We assess each source and choose live access, replication or federation per case rather than copying everything by default, align master data so entities match across systems, and apply transformations, business rules and data-quality controls in the governed layer. Where the connectivity itself is the obstacle, it is handled as integration work rather than worked around inside a report.

Yes. That covers financial planning and analysis, budgeting, rolling and driver-based forecasts, and revenue, cost, workforce, headcount, sales, supply-chain, capex and project planning — built with scenario modelling, version management and what-if analysis, and operationalized through planning calendars, workflows, data entry, allocations, data actions, multi actions and approvals, with plan-versus-actual reporting and predictive forecasting on top.

Yes, and it is often the better first engagement. We review what is actually used against what exists, rationalize and retire redundant reports, fix KPI definitions and ownership, tune model and dashboard performance, close data-quality and reconciliation gaps, and correct role-based access. A large share of “we need a new analytics platform” turns out to be an unowned KPI and a slow model.

An inventory of the SAP BW and SAP BW/4HANA estate — objects, data flows, source dependencies, and real query and report usage — then an object-by-object decision to keep, simplify, consolidate or retire. What remains is modernized toward SAP Datasphere and SAP Business Data Cloud, typically through a hybrid architecture and BW data-product generation rather than a single cutover, with reconciliation testing, user migration, legacy report retirement and an analytics operating model to run it afterwards.

Yes. Analytics can move into VISCAP AMS or stay with your team under a governance model we set up: monitoring data loads and connections, reviewing report usage and retiring what is redundant, managing KPI changes and data quality, maintaining planning models, supporting close and consolidation cycles, tuning performance, and prioritizing new use cases — including which are ready for SAP Business AI and which need governance first.

Contact Us

Speak with VISCAP’s data and analytics team about the reporting you rely on today, the planning and close cycles behind it, and the data foundation needed to make both trusted.

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