SAP Analytics Implementation Playbook
Execute successful SAP analytics implementations using the SAP Activate methodology. Follow its lifecycle from Discover to Run: governance setup, data integration, storytelling, and deployment strategies that drive measurable business outcomes at enterprise scale. 2026 best practices include zero-copy data sharing via BDC Connect, AI-powered process discovery (Signavio), and Joule natural language interfaces for executive adoption.
The SAP Activate Methodology: Six Phases, Four at the Core
SAP Activate is SAP's implementation methodology, and this playbook applies it to SAP Analytics Cloud (SAC), SAP Datasphere, and SAP Business Technology Platform deployments; SAP Activate even has a dedicated analytics workstream. It spans six phases: Discover, Prepare, Explore, Realize, Deploy, and Run. SAP calls Prepare through Deploy the four core phases of project delivery, while Discover (research before the project commits) and Run (support of the live solution) are additional phases. The diagram covers Discover through Deploy, and the post-go-live section below covers Run. Each phase has specific deliverables; this playbook adds governance gates and risk management practices for analytics.
Phase 1: Discover — Laying the Foundation
SAP describes Discover as a non-committal phase before the project starts: you research the offerings and produce an innovation strategy and roadmap, a value and impact analysis, an implementation strategy, and technical architecture and infrastructure requirements. For analytics, this is where you establish scope, identify business requirements, and align stakeholders around implementation objectives. Success here prevents costly scope creep and rework later.
Scope Definition
Define analytics use cases, identify business processes supported, and map data sources. Establish clear boundaries: what is and is not in scope for the initial implementation. Document business KPIs, required dimensions, and reporting cadences.
Fit-to-Standard Groundwork
SAP Activate emphasizes fit-to-standard: adopting standard functionality and processes rather than heavy customization. The fit-to-standard workshops themselves run in Explore; in Discover, use SAP's trial systems (as of November 2026, a 60-day SAP Analytics Cloud trial and 30-day SAP Datasphere and SAP Business Data Cloud trials) to check early that SAP Analytics Cloud, Datasphere, and BTP can meet requirements with minimal customization.
Architecture Decisions
Define the data architecture: data sources, integration strategy (batch vs. real-time), data governance model, and security framework. Decide how content will be consumed (curated stories for explanation versus self-service exploration), SAC vs. BW/Datasphere separation of concerns, and master data integration approach.
Risk Assessment
Identify common implementation risks: data quality issues, resource constraints, organizational change readiness, and technical complexity. Develop mitigation strategies early. Establish success metrics: adoption rates, query performance, data freshness SLAs.
Key Discovery Insight: Data quality work is easy to underestimate. Assess source data quality during Discover, then size the Realize-phase effort for data cleansing, enrichment, and validation from what you find; analytics cannot deliver trusted insights before that work is done.
Phase 2: Prepare — Environment and Team Setup
The Prepare phase starts the project: in SAP Activate it defines the scope of work and project governance and prepares the sandbox or starter systems. For analytics, it provisions infrastructure, configures identity and access management, and onboards the project team. Parallel streams run in this phase: technical environment setup and organizational readiness activities.
BTP Cockpit Setup
Configure SAP Business Technology Platform: subaccount structure, entitlements for the BTP services you use (such as SAP Cloud Transport Management, the Connectivity service, and SAP Integration Suite), and SAP Cloud Transport Management for promotion across DEV/QA/PROD environments; it transports SAC content network packages and, since SAP Datasphere release 2024.14, Datasphere content. Since January 1, 2026, SAC and Datasphere can no longer be bought with BTP credits (BTPEA, CPEA, or pay-as-you-go) for new subscriptions: SAP's pricing pages license both through SAP Business Data Cloud capacity units, and existing tenants were moved under BDC without a technical migration.
SAC Tenant Provisioning
Request and configure the SAP Analytics Cloud tenant. Set up the folder and sharing structure (grouping related content), model data access controls, and the license allocation (Business Intelligence, Planning Standard, or Planning Professional). Configure story settings: mobile optimization, responsive behavior, and integration with other SAP tools.
Datasphere Spaces
Design Datasphere spaces to organize data models by business area (Sales, Finance, Supply Chain). Configure data integration from sources (SAP, third-party, cloud systems). Set up fact and dimension tables, establish data flow automation, and define time-dependent data patterns.
Identity and Access Management
Configure Identity Authentication (IAS), part of SAP Cloud Identity Services, for single sign-on. Map organizational roles (story viewers, dashboard editors, data modelers, and administrators) to SAC roles, which SAP recommends assigning through teams, and to Datasphere scoped roles, which combine a role template with spaces and users. Establish user provisioning (SAP Identity Provisioning feeds SAC through its SCIM-based API, with Microsoft Entra ID among the supported sources) and agree the row-level security approach: Datasphere data access controls and SAC model data access control.
Prepare Phase Checklist
- BTP subaccount structure documented and approved
- SAC and Datasphere tenants live and accessible to team
- IAS configured with SSO and role mappings
- Dev/QA/Prod environments established and isolated
- Cloud Transport Management configured for content promotion
- Team access provisioned; training materials prepared
- Data source connectivity tested (SAP ERP, cloud systems, third-party)
Phase 3: Explore — Prototyping and Validation
The Explore phase runs the fit-to-standard workshops: SAP Best Practices content and standard processes are demonstrated in sandbox or starter systems, and the delta requirements, gaps, and configuration values go into the backlog for Realize. For analytics, build prototypes that business users validate early, when rework is least costly; formal user acceptance testing comes at the end of Realize.
Prototype Key Stories
Build 2-3 representative SAC stories that demonstrate core use cases: executive dashboard, operational report, drill-down analysis. Include responsive design for mobile and desktop. Test interactivity: filters, bookmarks, drill-down navigation. Validate that story complexity is manageable and performance meets expectations.
Data Model and Connection Validation
Validate that Datasphere models properly represent business entities. Test data freshness from source systems. Verify fact and dimension relationships, hierarchies, and measure calculations. Run sample queries to confirm query performance under expected data volumes.
Business Validation
Business users validate that prototypes answer their analytics questions accurately and intuitively. Run validation sessions against realistic data subsets. Document gaps: missing dimensions, incorrect calculations, UI improvements. Prioritize gaps for the Realize phase, which closes with the full integration test and UAT.
Governance Rules Definition
Define how analytics will be governed: data access rules (who sees what data), story approval workflows, data model change control, and naming conventions. Test row-level security (data access controls) in Datasphere. Document master data stewardship responsibilities.
Common Pitfall: Skipping proper governance setup in Explore. By the time Realize begins, it becomes difficult to retrofit governance. Without clear data access rules, governance workflows, and naming conventions, analytics deployments become chaotic and hard to maintain.
Phase 4: Realize — Build and Harden
The Realize phase builds the production-ready analytics platform. SAP Activate works through the backlog in agile sprints and closes the phase with a full integration test and user acceptance test before Deploy. Data models are hardened, stories are polished, and all integration, performance, and security controls are validated.
Build Full Analytic Stories
Develop all planned SAC stories for the initial release. Implement responsive design (mobile-first approach). Add rich interactivity: filtering, conditional formatting, drill-down navigation, and bookmarks for saved views. Optimize visualizations for clarity: chart types, color coding, trend indicators.
Data Models and Integration Flows
Complete Datasphere data models for all business areas. Build data flows for scheduled data pulls from source systems (SAP ERP, cloud applications, third-party data). Test data quality validation: completeness, accuracy, timeliness. Implement error handling and retry logic for failed data loads.
Integration and Orchestration
Where users need near-real-time data, use live connections (SAC live to SAP S/4HANA, SAP BW, or Datasphere) or Datasphere remote tables rather than scheduled imports, which run at most hourly. Use SAP Integration Suite (its Cloud Integration capability) to bring data from applications without a suitable connection type into Datasphere; SAC itself does not subscribe to event streams. Implement SAP Cloud Transport Management for content promotion from DEV → QA → PROD. Use scheduled publications to deliver stories by email on a recurring schedule (for example, as PDF or PowerPoint files).
Security and Governance Controls
Implement row-level security in Datasphere with data access controls, which filter rows by organizational hierarchy or region for every user who reads the object, including through SAC. Configure story-level security in SAC through sharing settings (View, Edit, Full Control, or Custom): who can view, edit, and publish stories. Set up data sensitivity classifications and audit logging (the SAC activities log; Datasphere space audit logs for read and change operations).
Performance Tuning
Optimize data model aggregation levels to balance storage and query performance. Test stories under realistic data volumes. Identify slow queries and optimize Datasphere models or SAC story logic. Apply SAP's SAC performance guidance (avoid charts with more than 500 data points, use Top N, pick a loading optimization setting, and review Performance Insights statistics and recommendations), and agree response-time targets with the business before go-live.
Hypercare Planning
Plan go-live support: assign escalation contacts, document known issues, prepare runbooks for common troubleshooting scenarios. Schedule post-go-live monitoring of query performance, data freshness, and user adoption metrics. Plan phase-2 enhancements based on feedback.
Testing Strategy
Unit Testing: Validate individual data models, measure calculations, and story logic. Test edge cases: zero values, NULL handling, period-over-period comparisons.
Integration Testing: Test end-to-end data flows from source systems through Datasphere to SAC. Verify that data transformations are correct and consistent across systems.
User Acceptance Testing (UAT): Business users validate against production data volumes and realistic scenarios. Measure performance, confirm accuracy of key metrics, and validate business logic alignment.
Stress Testing: Simulate peak usage: your expected number of concurrent users querying dashboards, plus headroom. Identify bottlenecks in Datasphere queries or SAC story rendering.
Critical Success Factor: Do not underestimate data quality work. Allocate explicit Realize-phase effort to data cleansing, validation, and reconciliation, sized from the data quality assessment. "Garbage in, garbage out" — analytics are only as good as the underlying data.
Phase 5: Deploy — Go-Live and Adoption
The Deploy phase sets up production, confirms readiness for business operations, executes go-live, and runs hypercare; it also transfers knowledge to operations teams and monitors adoption. Go-live is not a one-time event but the beginning of ongoing platform support and enhancement, which SAP Activate treats as the Run phase.
Go-Live Execution
Execute planned go-live: activate production analytics, notify users, and monitor system health. Promote content from QA to PROD using Cloud Transport Management. Validate data loads are running on schedule. Monitor analytics query performance and user login volume during initial hours.
Content Promotion Strategy
Use Cloud Transport Management to safely promote stories, data models, and governance rules from development to production environments. Version control analytics content: maintain release notes, rollback plans, and approval workflows. Track changes to data models and story designs.
Knowledge Transfer
Transfer story ownership to business teams. Train operations teams on data load monitoring, error handling, and troubleshooting. Document data model lineage: where data comes from, how it's transformed, and where it's consumed. Establish centers of excellence for analytics development and governance.
Hypercare Support
SAP Activate places sustainment and hypercare in Deploy: the project team stays for a predetermined period, usually set in the contract or in the plan for the transition to operations. Agree that window and its support hours up front. Maintain on-call escalation contacts. Address data issues, performance bottlenecks, and user questions in near real-time. Document issues and create a phase-2 backlog for improvements.
Adoption Monitoring
Track adoption metrics: story view counts, active users per week, query response times, data refresh success rates. Measure business impact: adoption of insights into decision-making, cycle time improvements, cost savings. Use adoption metrics to inform phase-2 prioritization.
Change Management
Execute change management: training programs for all user types, communication plans for stakeholder engagement, champion networks to encourage peer-to-peer adoption. Address organizational resistance to analytics-driven decision-making. Build feedback loops to continuously improve the platform.
Implementation Essentials: Data, Governance, and Testing
Data Integration Strategy
Data integration is the foundation of analytics. Plan your approach carefully:
- Source Identification: Map all systems that feed analytics: SAP ERP, CRM, HR, third-party data, cloud SaaS platforms. Prioritize by business value and integration complexity.
- Batch vs. Real-Time: Batch loads (nightly or hourly) cover many analytics needs; scheduled SAC imports run at most hourly. Reserve real-time integration for operational dashboards with strict SLAs (e.g., order processing status). Real-time adds complexity and cost.
- Master Data Management: Establish a single source of truth for customer, product, employee, and supplier master data. Govern master data at the source or in SAP Master Data Governance, then harmonize it in Datasphere (whose view data validations check keys and hierarchies) before analytics consumption.
- Data Quality Frameworks: Implement validation rules for each data source: required fields, format validation, range checks, and referential integrity. Log and alert on data quality failures before analytics are impacted.
- SAP Integration Suite: Use the Cloud Integration capability of SAP Integration Suite to orchestrate data flows, handle transformations, and manage API integrations with cloud platforms. Implement retry logic and error handling for resilience.
- BDC Connect for Zero-Copy Analytics (2026): BDC Connect, part of SAP Business Data Cloud, shares governed SAP data products zero-copy into a partner's own catalog (e.g., Snowflake catalog-linked databases or Databricks Unity Catalog via Delta Sharing) rather than copying SAP data out. Databricks reached general availability in October 2025, Snowflake on May 4, 2026, and Google BigQuery in July 2026; Microsoft Fabric was still in public preview in November 2026, with GA planned for the second half of 2026, and Amazon Athena (plans announced May 12, 2026) is planned for the first half of 2027. It avoids duplication and keeps data fresh; verify each partner's status before committing.
Zero-Copy & Federated Access Implementation Steps
When to use this: Your organization has analytical data in Snowflake, Databricks, or BigQuery and you want live, governed access without copying data to SAP infrastructure. Two complementary patterns apply — BDC Connect shares governed SAP data products zero-copy into the partner's own catalog, while SAP Datasphere can also federate to those platforms through remote tables: the Google BigQuery connection type supports them directly, and Snowflake and Databricks are reached live through the Generic JDBC connection (the native Snowflake connection type serves replication flows only, and there is no Databricks connection type). The steps below cover the Datasphere federation setup.
- External data platform (Snowflake, Databricks, BigQuery) deployed and operational.
- Analytics schemas and tables documented; data quality baseline established.
- Network path in place: for Generic JDBC, a Data Provisioning Agent (with its CamelJdbcAdapter) runs on a host that can reach the external platform, and firewall rules allow the traffic.
- A technical (service) user with read-only access provisioned in the external platform.
- In Datasphere, create the connection: Google BigQuery, or Generic JDBC for Snowflake and Databricks.
- Provide connection details: hostname, credentials, database/schema names.
- Test connection: verify Datasphere can query external platform.
- Check pushdown: remote tables federate queries to the source at runtime, so confirm in the external platform's query history that SAC filters reach it (less data transfer, better performance).
- Import the external tables as remote tables, then build views with the semantic usage Fact on top (e.g., Snowflake table "orders" becomes the fact view Fact_Orders in Datasphere).
- Create dimension views on external master data (e.g., Snowflake "customer_master" becomes Dimension_Customer), and combine facts and dimensions in an analytic model for SAC.
- Define measures: aggregation logic (sum revenue, count orders, average margin).
- Establish governance: who owns semantic model? Who can request changes? What's the approval process?
- External platform should have clustering/partitioning on common query dimensions (time, customer, product).
- Measure query latency against the targets agreed with the business. If slower, investigate: Is clustering or partitioning missing? Are queries complex? Should the remote table be persisted in Datasphere as a snapshot instead?
- For high-frequency queries, consider materializing aggregates in external platform (e.g., daily sales by customer) and querying aggregates via live connection.
- Row-level security: Datasphere data access controls filter rows for every user who reads the view directly or through dependent objects, including SAC users, so they see only data they're authorized for. Example: Sales user sees only their territory's orders (even though full dataset exists in Snowflake).
- Audit logging: enable the space's audit logs for read (SELECT) and change operations, set the retention period (7 to 10,000 days, default 30) with the storage it consumes in mind, and monitor for unusual access patterns.
- Credential rotation: rotate the technical user's credentials on the schedule your security policy sets, and document the process.
- Custom BDC data products are built from local tables in a file space (the object store), not from federated remote tables or analytic models. To offer this data as a product, persist it into a file space first, then publish it through the Data Sharing Cockpit. SAP's announced Data Product Studio had no confirmed general availability as of November 2026.
- Other teams then find the product in the catalog and install it into their own Datasphere spaces, or share it to SAP Databricks, SAP Snowflake, or BDC Connect partners, without building their own copies.
Project Phase Gates and Governance Checkpoints
Implement governance gates at the end of each SAP Activate phase to prevent ungoverned analytics from reaching production. Durations depend on scope, so tie each gate to the phase exit rather than to a fixed week:
- Strategy Checkpoint: Implementation strategy, roadmap, and value case agreed with the sponsors. Open governance questions (Who owns analytics? Who approves data models? What are naming standards?) are logged for Prepare, where SAP Activate sets project governance.
- Architecture Review: Data integration architecture approved. Source systems identified. BDC Connect strategy (if applicable) approved. Datasphere vs. SAC modeling decisions validated.
- Success Criteria: Project can proceed to Prepare phase only if strategy and architecture decisions are locked.
- Environment Readiness: SAC tenant, Datasphere spaces, data integrations (SAP Integration Suite, BDC Connect) all configured and tested.
- Governance and Access Setup: Governance framework and ownership approved by the steering committee. Roles and team access provisioned; the row-level security approach agreed, so users can access only approved data.
- Baseline Data Quality: Data quality assessments completed for all source systems. Quality rules drafted (completeness, referential integrity, format validation).
- Success Criteria: Team can build models and test data flows without manual workarounds. Proceed to Explore phase.
- Prototype Validation: Pilot data models and stories built and validated with business stakeholders. Do data and metrics match business expectations?
- Performance Baseline: Model sizes measured. Query performance benchmarked. Performance targets set and committed.
- Design Documentation: Data dictionary, source-to-target mapping, and governance procedures documented.
- Success Criteria: Stakeholders confident in approach. Architecture and design approved by governance council. Proceed to Realize phase.
- UAT Sign-Off: Business users validated all stories and data models. Metrics reconciled to source systems. All defects fixed (only minor issues remain).
- Performance Production-Ready: Performance tested with realistic data volumes and user concurrency. All queries meet SLA targets.
- Security Audit: Row-level security verified. Access controls tested. Audit logging enabled and monitored.
- Data Quality Report: Data quality rules deployed. Baseline metrics captured. Stewards trained on monitoring and escalation.
- Success Criteria: Formal production go-live approval from steering committee. Proceed to Deploy phase.
- Go-Live Readiness: Production environments promoted. Refresh schedules active. Monitoring dashboards operational.
- Adoption Metrics: User adoption tracked weekly. High usage indicates successful adoption. Low usage triggers intervention (more training, simpler stories).
- Support Readiness: Support team trained and on-call. Escalation procedures active. SLAs for incident response documented.
- Success Criteria: Monitor adoption through the agreed hypercare period. If adoption targets are met, hand over to steady-state operations (Run).
Governance and Security
Analytics governance ensures data is trustworthy, access is controlled, and usage is audited:
- Data Access Rules: Implement row-level security with Datasphere data access controls to limit data visibility by user role, region, or business unit. Example: sales representatives see only their territory data.
- Story Approval Workflows: Require approval before publishing production stories. Implement version control: since the 2026 QRC1 release, SAC file versions for optimized stories record who saved each version and when and let you restore an earlier one (referenced objects such as models are not versioned). Establish naming conventions for consistency.
- Naming Standards: Define clear naming conventions for data models (DM_SALES_ORDERS), stories (Sales Dashboard - Monthly), and fields (Revenue_USD). Consistency makes content discoverable and maintainable.
- Audit Logging: Log data access and modifications with the SAC activities log (permission changes, model changes, story views, logins) and Datasphere space audit logs (read and change operations). Monitor for unusual queries or access patterns. Implement data sensitivity classifications: public, internal, confidential. Restrict access to sensitive data.
- Centers of Excellence: Establish governance bodies to manage data model approvals, story releases, and analytics standards. Build analytics platform teams (architects, modelers, developers) to maintain quality.
Testing and Validation
Comprehensive testing prevents data errors, performance bottlenecks, and security vulnerabilities from reaching production:
- Data Model Testing: Validate fact tables contain correct granularity. Test hierarchies: time hierarchies (Year > Quarter > Month), organizational hierarchies (Company > Region > Territory). Validate measure calculations: sum, average, count logic.
- Query Performance Testing: Benchmark query response times under realistic data volumes. Identify slow queries and optimize data model aggregations. Test concurrent user loads at and above your expected peak.
- Story Accuracy Testing: Reconcile story metrics against source system reports. Validate drill-down navigation logic. Test filter combinations to ensure no data is lost or duplicated.
- Security Testing: Verify row-level security is enforced. Test that users cannot see data outside their approved permissions. Audit story access controls and data sensitivity classifications.
- Mobile and Responsive Design Testing: Test stories on mobile devices (iOS, Android), tablets, and desktops. Validate chart rendering, filter usability on small screens. Measure load times on slow connections (4G).
Common Implementation Pitfalls and How to Avoid Them
Pitfall 1: Skipping Governance Setup Early
Risk: Without governance frameworks in place, analytics sprawl: duplicate stories, inconsistent metrics, and confusion about data access rules.
Solution: Define governance in the Explore phase, before large-scale content creation. Establish naming standards, approval workflows, and data access rules early.
Pitfall 2: Underestimating Data Quality Work
Risk: Poor data quality in analytics erodes user trust and leads to incorrect decisions. Data cleansing effort is easy to underestimate.
Solution: Assess data quality early (Discover phase). Size Realize effort for data validation, cleansing, and enrichment from that assessment. Don't rush to analytics without clean data.
Pitfall 3: Over-Customizing Standard Functionality
Risk: Heavy customization of SAP Analytics Cloud or Datasphere violates SAP Activate fit-to-standard principles. It increases implementation time, cost, and maintenance burden.
Solution: Adopt SAP standard patterns first. Customize only when business value clearly justifies the cost. Document all customizations and plan maintenance.
Pitfall 4: Neglecting Change Management
Risk: Even excellent analytics fail if users aren't trained or motivated to adopt. Adoption failure means wasted investment and continued reliance on legacy reports.
Solution: Develop change management plans in Prepare phase. Train users extensively. Build champion networks. Measure adoption; reinforce successful adoption patterns.
Pitfall 5: Inadequate Performance Testing
Risk: Stories that perform well with test data slow down dramatically under production volumes. Users abandon slow analytics in favor of static reports.
Solution: Test early with realistic data volumes. Optimize data models and queries. Stress test at expected peak concurrency. Set and validate SLA targets before go-live.
Pitfall 6: Weak Master Data Governance
Risk: Without clear master data ownership and validation, data inconsistencies cascade through analytics. Sales sees different regional totals than Finance.
Solution: Establish master data stewardship early. Define single sources of truth for customer, product, and employee master data. Govern it at the source or in SAP Master Data Governance, and harmonize it in Datasphere before analytics consumption.
Post-Go-Live: Building a Sustainable Analytics Practice
After Deploy comes SAP Activate's Run phase: support and management of the productive solution, which SAP treats as a methodology of its own, with support models such as a Customer Center of Expertise (CCoE). Build an analytics practice that sustains and scales:
Operations and Monitoring
- Data Load Monitoring: Monitor Datasphere data refresh jobs (the System Monitor shows tasks and statement logs). Alert on failures. Validate data freshness against SLAs. Build dashboards for load job status and data lag metrics.
- Query Performance Monitoring: Monitor SAC story response times with Performance Insights and the performance statistics in the System Overview (formerly the Administration Cockpit). Track slowdowns. Identify and optimize slow queries. Maintain the response-time targets agreed with the business.
- User Adoption Metrics: Track story views, active users, and query volumes weekly (System Overview usage statistics; the activities log records story views). Identify underutilized stories and optimize or retire them. Track adoption trends over time.
- Issue Management: Maintain a prioritized backlog of reported issues and enhancement requests. Triage by business impact. Plan phase-2 enhancements based on adoption patterns and feedback.
Continuous Improvement
- Phase-2 Roadmap: Plan analytics enhancements for quarters 2 and 3. Prioritize by business value, adoption metrics, and technical feasibility. Examples: add new data sources, develop new stories, implement planning applications.
- Analytics Center of Excellence (CoE): Establish a center of excellence to drive analytics best practices, provide technical support, and govern the analytics platform. Build a self-service analytics model where business teams develop their own content with CoE guidance.
- Capability Development: Invest in training: Datasphere data modeling, SAC story development, scripting in optimized stories (analytic applications became inaccessible when the classic experience was retired at the end of Q2 2026). Build internal subject matter experts to reduce consulting dependence.
- Technology Upgrades: Monitor the SAP product roadmap and release notes. SAC and Datasphere are cloud services that SAP updates on its own release schedule, so plan regression checks and the adoption of new Datasphere, SAC, and AI capabilities rather than upgrade projects.
Key Takeaways for Implementation Success
The Five Implementation Fundamentals
- Master the SAP Activate methodology: Six phases, with Prepare through Deploy at the core, provide a proven roadmap. Don't skip phases or rush the timeline. Each phase builds on the previous one.
- Prioritize governance early: Define data access rules, naming standards, and story approval workflows in Explore. Governance retrofits are expensive and difficult.
- Invest in data quality: Budget explicit Realize effort for data cleansing and validation, sized from your data quality assessment. Analytics are only as good as the underlying data.
- Plan for change management: Training, communication, and adoption support are critical success factors. Strong analytics fail without user adoption.
- Test comprehensively: Unit test data models. Integration test data flows. UAT test with business users. Stress test with realistic data volumes.
Implementation Checklist
Pre-Implementation: Discover Phase
- ☐ Business requirements documented and prioritized
- ☐ Analytics use cases defined and validated
- ☐ Data sources identified and assessed for quality
- ☐ Implementation strategy and roadmap agreed
- ☐ Success metrics and KPIs defined
- ☐ Value and impact analysis completed
- ☐ Risk register created and mitigation plans documented
Preparation: Prepare Phase
- ☐ BTP cockpit configured with subaccount structure
- ☐ SAC and Datasphere tenants provisioned
- ☐ IAS configured for SSO and role management
- ☐ Dev/QA/PROD environments set up
- ☐ Cloud Transport Management configured
- ☐ Data source connectivity validated
- ☐ Training and onboarding program launched
Prototyping: Explore Phase
- ☐ 2-3 representative stories built and tested
- ☐ Data model prototypes created and validated
- ☐ Fit-to-standard workshops completed with business users
- ☐ Governance rules and naming standards defined
- ☐ Data access controls designed and tested
- ☐ Gap analysis documented for Realize phase
- ☐ Mobile responsiveness validated
Build: Realize Phase
- ☐ All planned stories built; integration test and UAT passed
- ☐ Data models complete and optimized
- ☐ Data flows automated and validated
- ☐ Security and row-level access controls implemented
- ☐ Performance tested under realistic data volumes
- ☐ Stress testing completed at expected peak concurrency
- ☐ Cloud Transport Management promotion tested
- ☐ Hypercare support plan documented
Go-Live: Deploy Phase
- ☐ Production content promoted and validated
- ☐ User access provisioned and verified
- ☐ Data loads running on schedule
- ☐ Hypercare support team activated
- ☐ Adoption metrics baseline established
- ☐ Issue tracking process activated
- ☐ Phase-2 enhancements planned