The Importance of Developing a Data and Analytics Strategy

Developing a data and analytics strategy has become a board-level imperative for SAP-centric enterprises, reshaping how finance and technology leaders run their organizations day to day. With cloud-based tooling maturing, the question is no longer whether to invest in analytics, but how to industrialize data as an operational asset that reliably informs every decision.

Why Strategy, Not Tools, Now Determines Value

For SAP customers, the shift to S/4HANA, SAP Business Data Cloud and SAP Analytics Cloud (SAC) is exposing fragmented data models, manual reporting and brittle integrations that limit the impact of even advanced analytics initiatives. A formal data and analytics strategy aligns architecture, governance and operating models. This lets CFOs, finance directors and architects can trust and reuse data across planning, consolidation, and operational reporting.

The stakes are rising quickly. The global data analytics market is projected to grow from about $64 to $84 billion dollars in 2025 to hundreds of billions by the mid-2030s, with compound annual growth often estimated near or above 25% as organizations race to embed analytics into core processes. For SAP shops, this growth translates into increased pressure to modernize legacy SAP BW and BPC landscapes, rationalize overlapping data warehouses, and integrate SAP and non-SAP sources into governed, analytics-ready models.

SimpleFi Solutions is already seeing SAP finance teams treat data strategy as the foundation for modernization rather than an afterthought. In a recent program, SimpleFi used its PlaniFi accelerator and SAP Analytics Cloud to deliver a new consolidation and reporting solution for SumUp in just over two months, slightly under budget. The standardized, prebuilt content and governed data model allowed the finance team to shorten design and testing cycles while gaining a platform they can own and extend themselves, reducing dependence on IT for everyday reporting changes.

For technology executives, this changes daily work. Controllers gain consistent, drillable views of actuals and plans across entities; architects can standardize integration patterns instead of supporting one-off data feeds; and CIOs can reallocate resources from report building to higher-value data products. A documented strategy also clarifies which use cases belong in SAP-native versus external platforms, improving vendor negotiations and roadmap discipline.

Overcoming Adoption Challenges Inside SAP Landscapes

Common obstacles in building an SAP-centric data and analytics strategy include heavily customized ECC and BW systems, inconsistent master data, and fragmented reporting tools across regions and business units. Finance teams often rely on spreadsheets to reconcile conflicting numbers, undermining trust in official reports and slowing the close. Without a strategy, each new initiative adds another silo.

Recent transformation programs show these challenges are solvable when organizations treat data strategy as a cross-functional effort. In one banking example, a financial institution rationalized multiple SAP architectures into a single SAP Cloud ERP and integrated financial data platform, reducing data volume while improving regulatory reporting and financial transparency. The bank completed migration in a single weekend with no noticeable impact on operations, demonstrating how disciplined data planning can de-risk even complex SAP changes.

For SAP leaders, a few best practices are emerging such as:

• Standardize finance and operational data models early, including clear ownership between finance and IT.
• Favor cloud-based analytics and prebuilt frameworks to accelerate delivery and keep customizations in check.
• Embed training and knowledge transfer so business teams can manage reports and models, turning analytics from an IT bottleneck into a self-service capability.

As SAP shifts beyond S/4HANA migrations toward optimization, a coherent data and analytics strategy is becoming the primary lever for unlocking value from core ERP investments. That means the most important analytics decision in 2026 is organizational rather than technical.

What This Means for SAPinsiders

Strategic data ownership reshapes SAP finance. Treating data and analytics strategy as a first-class initiative will push SAP finance and IT leaders to formalize ownership, align data models across S/4HANA and analytics platforms and turn reporting from a reconciliation exercise into a reliable, reusable foundation for continuous planning and performance management.

Cloud-native analytics accelerators become competitive differentiators. The emergence of prebuilt SAP Analytics Cloud frameworks and governed data models means GSIs, SIs and boutique partners that bring accelerators will win, as customers increasingly expect under-budget, fast-cycle delivery that finance teams can run independently post go-live.

Post-migration value hinges on data strategy discipline. As SAP programs move beyond core S/4HANA migration, enterprises that invest in coherent data strategies will better exploit AI, ESG and real-time analytics use cases, while others risk replicating legacy fragmentation in modern cloud landscapes.