Enabling a Data Fabric that Links Strategy to Execution

⇨ Organization often have a gap between strategies that embrace the data story construct and execution of solutions.

⇨ SAP is aiming to maximize data fabric enablement with its new Datasphere

⇨ SAP Datasphere's multi-tier design can help overcome landscape complexity challenges that detract from business intelligence strategy realization.

An Opportunity to Close the Gap Between Data Strategy and Execution

The end-to-end data story has evolved over time with respect to strategy, requirements, and deployment. Evolution is necessary to address capability advancements and organizational needs. Despite best intentions, there is often a gap between strategies that embrace the data story construct and execution of solutions that deliver intended outcomes. In this sense, decision intelligence conceptual ideation is frequently ahead of execution.

Prior SAPinsider research, including our 2022 Analytics in the Cloud Benchmark Report, illustrated that organizational business intelligence drivers and strategies intended to address those drivers are well-founded, but face various challenges in strategy realization. From disparate landscapes and varied business user requirements to reactive approaches in addressing specific, high-value needs, fulfillment of business intelligence imperatives requires taking a step back to understand core organizational goals, how these goals can be enabled proactively, and how solution landscapes should be shaped to meet these objectives.

The Multi-Tier Approach to Business Intelligence Enablement

In recent years, the business intelligence market pivoted from on-premise solutions to managed cloud and Software-as-a-Service (SaaS) constructs. Amid this pivot are paradigm evolutions that enable a data-to-decision fabric intended to streamline and advance legacy business intelligence approaches.

Many well-known solution providers, including SAP, Google, Microsoft, Amazon, and others, are shifting to subscription-based offerings (spanning managed cloud and SaaS) that include:

• Infrastructure
• Data stores
• Technology platforms for data management
• Extraction, transformation, and load (ETL)
• Data assembly and staging
• Application integration
• Business user-centric, low-code/no-code data consumption

SAP Datasphere, announced in March 2023, represents the next step forward in SAP’s approach to data fabric enablement. From a productized perspective, SAP Datasphere is the next evolution of SAP Data Warehouse Cloud (SAP DWC), which is central to capabilities enabled by SAP Business Technology Platform (SAP BTP).

SAP Datasphere goes beyond what was enabled by SAP DWC with source data neutrality and data ecosystem partnerships in a three-tier construct that is similar to competitor offerings:

• A data consumption tier, or “front end” of the data story that enables business users to access, analyze, and visualize data;
• A platform tier, including SAP Datasphere running in SAP Business Technology Platform (BTP); and
• A data tier that enables SAP and non-SAP data from any source or location.

This construct is illustrated in Figure 1.

Figure 1: SAP Datasphere Functional Architecture

Source: SAP, April 2023

Moving Toward Data Democratization

Beginning at the data tier and working upward, SAP Datasphere, along with functionally similar offerings from large and niche players, seeks to democratize the data fabric by inserting data source and premise neutrality. Central to SAP’s pivot from SAP-centric sources, SAP Datasphere enables comingling of SAP and non-SAP data from any location to be managed staged at the platform tier.

SAPinsiders have historically reported disparate data sources as a challenge in executing on business intelligence strategy. With SAP Datasphere running in SAP BTP, organizations can now comingle SAP, non-SAP, structured, unstructured, on-premise, cloud, and multi-cloud data with greater ease.

Further, rather than creating new stores to ingest this data, SAP Datasphere ingests source metadata to apply governance and orchestration capabilities in a semantic layer presented to business users for consumption. Once sources are identified, ingested, and modeled in SAP Datasphere, non-technical business users can then apply front-end planning, analysis, reporting, predictive, artificial intelligence (AI), machine learning (ML), and other capabilities to drive decisions. This approach differs from other SAP solutions, like SAP Business Warehouse (SAP BW), that involve significant technical resource engagement to enable data consumption.

Data Consumption in a Multi-Tier Model

Our 2022 Analytics in the Cloud Benchmark Report indicates that SAP customers leverage a variety of data consumption solutions from multiple vendors. While SAP Analytics Cloud (SAC) is SAP’s go-forward direction for data consumption, application programming interfaces (APIs) available at the SAP BTP tier provide the opportunity for consumption via solution of choice. For example, Microsoft Power BI, Amazon QuickSight, Google Looker, and other consumption solutions can leverage APIs for SAP BTP to enable the business user experience.

This said, there are functional advantages to leveraging SAC, including enablement of planning and analytics capabilities. For example, as existing SAP Business Planning and Consolidation (SAP BPC) customers plan the next step in their journey, SAC offers out-of-the-box financial planning and analysis (FP&A) capabilities that accelerate transition from SAP BPC to SAC. Additionally, SAP Integrated Business Planning (SAP IBP) customers can expand upon extended planning and analysis (xP&A) capabilities with ready-to-implement SAC solutions.

What Does This Mean for SAPinsiders?

• Bridging the gap between business intelligence strategy and execution is critical to enabling cross-organizational objectives. Organizations should investigate challenges that contribute to this gap and embrace technologies, processes, and resources that link strategy to execution.
• Multi-tier, source neutral platforms such as SAP Datasphere can help overcome landscape complexity challenges that detract from business intelligence strategy realization. Platform- and consumption-tier self-service, vendor ecosystem cost efficiencies, time-to-value, and return on investment (ROI) should also be assessed in selecting and deploying a platform tier that enables organizational strategy.
• All stakeholders, from C-suite to shop floor, should participate in business intelligence strategy, requirements, and target outcome definition. Neither line-of-business (LoB) nor technology stakeholders can plan and deliver on a strategy that is not inclusive of all stakeholders. In turn, organizational change management practices should be embraced to drive effective business intelligence transformation.