According to the BARC Data, BI and Analytics Trend Monitor 2026 published by BARC GmbH, organizations are placing greater emphasis on the foundations of effective analytics: data quality, security, governance, and the ability to access data in a consistent business context. These foundations are essential for making effective use of AI in data analysis and business decision-making.

These changes are reflected not only in technology vendors' strategies, but also in the way organizations are designing their data architectures.

Modern Business Analytics: Data Platforms Are Replacing Disconnected Tools

In many organizations, analytics environments have evolved organically over the years. Separate tools have been used for data integration, reporting, planning, and predictive analytics. Increasingly, organizations are moving towards a platform-based architecture in which data management, analytics, and planning operate within a single environment.

SAP Business Data Cloud is an example of this approach. Solutions such as SAP Datasphere and SAP Analytics Cloud are being developed as components of a unified data platform.

This shift also changes how analytics solutions are deployed. New projects are increasingly built around a single platform that brings together data management, reporting, planning, and AI capabilities.

For organizations, this means a simpler analytics architecture and easier integration of data from different business systems.

The growing adoption of data platforms is driven by a fundamental problem: analytics environments have become too complex. In many companies, the number of tools used to work with data has grown faster than the organization's ability to manage them effectively.

The new SAP commercial model also changes the way organizations plan and manage the cost of analytics solutions. With SAP Business Data Cloud, consumption-based pricing based on Capacity Units is becoming increasingly important. These units can be used across different components of the platform, including SAP Datasphere and SAP Analytics Cloud.

To help organizations estimate their resource requirements, SAP has also introduced the Capacity Unit Estimator. The tool helps estimate computing capacity requirements based on factors such as the number of users, analytical scenarios, and the volume of data being processed.

In practice, this represents a shift away from static licensing models towards an approach closer to cloud consumption models, where the actual use of data platform resources plays a central role.

As a result, enterprise analytics architectures are increasingly evolving into data platforms rather than collections of separate tools for reporting, integration, and planning.

Data Fabric: Connecting Data Across ERP and Other Business Systems

One of the biggest challenges in modern analytics is bringing together data from multiple systems, including ERP environments and data platforms used across different areas of the organization.

An approach known as data fabric is gaining traction. It enables organizations to work with data from multiple sources without continuously copying and replicating it across systems.

Solutions such as SAP Datasphere are designed to support this approach by providing a common semantic layer for business data and enabling it to be integrated with information from other data platforms.

This architecture can reduce the cost and complexity associated with maintaining multiple copies of the same data, simplify integration processes, and shorten the time required to deliver business insights.

SAP Analytics Cloud: Modern Financial and Operational Planning

Another major trend in analytics platforms is the closer integration of data analysis with business planning.

For years, financial, operational, and sales planning in many organizations was carried out in separate tools, often outside the company's core data architecture.

Today, the xP&A (Extended Planning and Analysis) approach is gaining momentum. It brings financial and operational planning closer to enterprise analytics by using the same underlying data models for planning, reporting, and analysis.

Within the SAP ecosystem, SAP Analytics Cloud (SAC) serves this role by combining reporting, analytics, and planning capabilities within a single environment.

Connecting planning processes with operational data — for example, from SAP S/4HANA — enables organizations to respond more quickly to changes in their operations and build more realistic business scenarios.

For Finance and Controlling teams, this means moving away from planning based on static spreadsheets towards planning models directly connected to the organization's operational data.

AI and the Future of Business Data Analytics

The increasing use of artificial intelligence is another major direction in the development of analytics platforms.

New solutions from technology vendors are making it possible to automate parts of the analytical process — from identifying patterns and relationships in data to generating forecasts and business scenarios.

Within the SAP ecosystem, this includes generative AI capabilities such as Joule, SAP's AI assistant. Joule allows users to interact with business information using natural language, making data and analytical capabilities more accessible to a wider range of business users.

As these technologies evolve, the importance of a coherent data architecture is increasing. AI needs access to reliable, well-governed data and a consistent business context if it is to deliver meaningful results across different areas of an organization.

In practice, the value of AI depends to a large extent on the quality of the data architecture on which it is built.

SAP Data and Analytics: Sources and Expert Support

The trends discussed above point to a broader shift in enterprise analytics: organizations are moving from fragmented collections of analytical tools towards integrated data platforms that combine data management, analytics, planning, and AI.

For organizations looking to assess how these developments could apply to their own data and analytics environment, our consultants can help.

We can support you in using the SAP Capacity Unit Estimator, assess the potential of SAP Business Data Cloud, or discuss how SAP Datasphere and SAP Analytics Cloud could fit into your existing data and analytics architecture.