Analytics

SAP Analytics

This skill enforces correct analytics architecture decisions, ensuring that CDS-based analytics are used for S/4HANA embedded reporting, standard Fiori analytical apps are evaluated before building custom reports, and authorization relevance is never an afterthought in CDS view design.

Content Routing

Topic	Section
SAP Analytics Cloud (SAC)	SAP Analytics Cloud
CDS-based embedded analytics	Embedded Analytics in S/4HANA
BW/4HANA and data warehousing	BW/4HANA
SAP Datasphere	SAP Datasphere
Fiori analytical apps	Fiori Analytical Apps
Planning models	SAC Planning
Live connection vs import	Connection Types

Iron Laws

1. ALWAYS USE CDS-BASED ANALYTICS FOR S/4HANA EMBEDDED REPORTING. Classic SE38/SQ01 reports and BW extractors are technical debt in S/4HANA. CDS views with analytical annotations are the strategic reporting foundation. Every new analytical requirement starts with CDS.
2. NEVER BUILD CUSTOM REPORTS WHEN STANDARD FIORI ANALYTICAL APPS EXIST. SAP delivers 500+ analytical Fiori apps. Before writing a single CDS view, search the Fiori Apps Library (fioriappslibrary.hana.ondemand.com) for existing coverage. Custom reports have maintenance costs; standard apps are upgraded by SAP.
3. ALWAYS CONSIDER AUTHORIZATION RELEVANCE IN CDS VIEWS. A CDS view without @AccessControl.authorizationCheck annotation defaults to NOT_ALLOWED in strict mode. Every analytical CDS view must have an explicit authorization check or a documented justification for NOT_REQUIRED. Data leaks from missing auth checks are audit findings.
4. NEVER MIX LIVE AND IMPORT MODELS WITHOUT ARCHITECTURE JUSTIFICATION. SAC live connections (real-time to HANA/BW) and import models (scheduled data copy) serve different purposes. Mixing them in the same story without documented rationale creates user confusion about data freshness.
5. ALWAYS VALIDATE KPI DEFINITIONS WITH BUSINESS OWNERS. A technically correct KPI with a business-incorrect definition is worse than no KPI. Revenue recognition timing, cost allocation methods, and headcount definitions MUST be confirmed with finance/HR before building dashboards.

Embedded Analytics in S/4HANA

CDS Analytical Annotations

The foundation of S/4HANA embedded analytics. Key annotations:

Annotation	Purpose
@Analytics.dataCategory: #CUBE	Marks view as analytical cube (fact data)
@Analytics.dataCategory: #DIMENSION	Marks view as dimension (master data)
@AnalyticsDetails.query.axis: #ROWS / #COLUMNS	Default query layout
@Semantics.amount.currencyCode	Currency field association
@Semantics.quantity.unitOfMeasure	Unit of measure association
@Consumption.derivation	Default filter value derivation
@AccessControl.authorizationCheck: #CHECK	Authorization enforcement
@ObjectModel.representativeKey	Key field for Fiori display

CDS View Stack (Recommended Pattern)

1. Interface View (I_): Stable API, reusable across applications. SAP-delivered.
2. Consumption View (C_): Built on interface views, adds analytical annotations. SAP or custom.
3. Extension View (Z_): Custom extensions via CDS view extension or wrapper views.

Virtual Data Model (VDM)

S/4HANA VDM organizes CDS views into layers:

• Basic (private): Direct table access — not for consumption
• Composite (restricted): Join multiple basic views — internal use
• Consumption (public): Exposed to Fiori, OData, SAC — the API layer

Key Analytical CDS Views (Examples)

CDS View	Domain
C_TRIALBALANCE	Financial — Trial balance
C_JOURNALENTRYITEMQ	Financial — Journal entries
C_GLLINEITEMQ	Financial — GL line items
C_PROFITCENTERACTUALQ	CO — Profit center actuals
C_PURCHASEORDERITEMQ	MM — Purchase order analytics
C_SALESORDERITEMQ	SD — Sales order analytics
C_PRODUCTIONORDERQ	PP — Production order analytics

SAP Analytics Cloud

SAC Capabilities

• Business Intelligence: Stories, dashboards, smart insights, smart predict
• Planning: Financial and operational planning with versioning
• Predictive: Time series forecasting, classification, regression
• Application Design: Custom analytical applications with scripting

Connection Types

Type	Data Freshness	Use Case	Limitations
Live (HANA)	Real-time	Operational dashboards	No blending, limited data wrangling
Live (BW)	Real-time	BW query consumption	No blending across connections
Import (scheduled)	Scheduled refresh	Cross-source blending, planning	Data latency, storage limits
Import (file)	Manual upload	Ad-hoc analysis	No automation

Story Design Best Practices

1. One story, one purpose — do not combine operational monitoring with strategic analysis
2. Use responsive pages — design for desktop and mobile from the start
3. Limit widgets per page to 8-10 — performance degrades with widget count
4. Use input controls over page filters for user-facing filtering
5. Set default filters to avoid full-data-set initial load
6. Use calculated measures sparingly — complex calculations degrade live connection performance

SAC Planning

Planning Model Design

• Accounts dimension: Mandatory — represents the measure structure (revenue, cost, headcount)
• Time dimension: Mandatory — fiscal or calendar periods
• Organization dimension: Company code, cost center, profit center
• Category dimension: Plan, actual, forecast, budget versions
• Custom dimensions: Product, region, project as needed

Key Planning Features

Feature	Description
Data Actions	Automated allocation, copy, distribution operations
Multi Actions	Chain multiple data actions with parameters
Predictive Forecasting	Time-series-based forecast generation
Value Driver Trees	Top-down planning with driver decomposition
Calendar Tasks	Workflow for planning cycles with assignments and deadlines
Version Management	Plan, budget, forecast version comparisons

Integration with S/4HANA

• Embedded Planning: SAC planning models connected live to S/4HANA ACDOCA
• Writeback: Plan data written back to S/4HANA for integrated financial reporting
• Data Actions for Actuals: Pull actuals from S/4HANA into planning models for comparison

Fiori Analytical Apps

App Types

Type	Technology	Example
Overview Page (OVP)	Smart annotations, CDS	Financial Overview, Procurement Overview
Analytical List Page (ALP)	CDS + SmartTable	Journal Entry Items, Sales Orders
KPI Workspace	KPI modeler	Custom KPI tiles on Fiori Launchpad
SAC Embedded	iFrame/mashup	Embedded SAC story in Fiori

Key Standard Analytical Apps

• F0996A: Financial Statement (actual/plan comparison)
• F1531: Display Journal Entries
• F2077: Manage Cost Centers and Budgets
• F0713: Sales Order Fulfillment Analysis
• F0842: Purchase Order Tracking
• F2439: Inventory Analysis

BW/4HANA

When to Use BW/4HANA

• Cross-system reporting (multiple S/4HANA instances, non-SAP sources)
• Historical data retention beyond S/4HANA operational data lifetime
• Complex transformation and data modeling beyond CDS view capabilities
• Near-line storage for large data volumes

Key Objects

Object	Purpose
ADSO (Advanced DataStore Object)	Primary data persistence
CompositeProvider	Virtual data integration layer
Open ODS View	External data consumption without persistence
BW Query	Analytical query for consumption by SAC or Fiori

CDS Extraction

S/4HANA -> BW/4HANA extraction uses CDS-based extractors (replacing classic datasources). Annotation: @Analytics.dataExtraction.enabled: true.

SAP Datasphere

Positioning

SAP Datasphere (formerly Data Warehouse Cloud) is the cloud data integration and modeling layer.

Key Capabilities

• Spaces: Isolated workspaces for data governance
• Data Builder: Graphical data flow and view modeling
• Business Layer: Business-friendly semantic model on technical data
• Data Marketplace: Third-party and SAP content packages
• Open SQL Schema: Direct HANA SQL access for advanced scenarios

Integration with SAC

Datasphere serves as the semantic layer between raw data and SAC stories. Pattern: Source systems -> Datasphere (model/govern) -> SAC (visualize/plan).

Best Practices

1. Start with SAP standard content — check the SAC content library and Fiori apps library before building custom
2. Design CDS views for reuse — build interface views that multiple consumption views can reference
3. Implement row-level security via DCL — CDS data control language for authorization, not application-level filtering
4. Use bookmarks in SAC stories for personalized views instead of duplicating stories
5. Monitor CDS view performance — use SQL explain plan and HANA monitoring to detect expensive views

Anti-Patterns

• Building ABAP ALV reports for analytics in S/4HANA (bypasses the VDM and lacks CDS benefits)
• Creating one massive CDS view with 50+ joins instead of layered VDM approach (performance disaster)
• Using SAC import mode for data that changes hourly (live connection is appropriate)
• Skipping @AccessControl annotations — data exposed to all users without explicit authorization
• Hardcoding currency conversion in CDS views instead of using standard conversion functions

Verification

This skill is complete ONLY when ALL of the following are true:

• Correct analytics technology selected for the scenario (CDS, SAC, BW/4HANA, Datasphere)
• Authorization approach defined (DCL for CDS, SAC security for stories)
• Connection type justified (live vs. import with rationale)
• Standard content checked before proposing custom development
• KPI definitions validated against business requirements, not just technical correctness
• Performance implications considered (view complexity, data volume, refresh frequency)

Evidence required: Specific CDS view names, SAC model design decisions, connection type justification, and standard content references — not generic analytics advice.

Next Skill

After completing this skill, invoke:

• fi — When financial reporting CDS views or GL analytics are the focus
• btp — When custom analytical applications or Datasphere configuration is needed
• abap-cloud — When custom CDS view development is required

Cross-References

• fi — Financial reporting, trial balance, journal entry analytics
• co — Cost center and profit center analytical views
• abap-cloud — CDS view development, ABAP SQL, RAP-based analytical services
• btp — SAP Datasphere administration, SAC tenant management
• sf — Workforce analytics integration with SuccessFactors data