The Top 5 Data and Analytics Themes for SAPinsiders in 2026

Data and analytics have moved from back-office support to a front-line driver of how digital businesses operate, compete, and prepare for AI adoption. For SAPinsiders, 2026 will be about turning growing data investments into better decisions, stronger governance, and a real foundation for AI rather than more isolated reports and dashboards.

Data Maturity Becomes a Business Imperative

Only a small minority of organizations we surveyed for our recent Enterprise Data and Analytics in the Era of AI research report have truly transformational data and analytics capabilities, with a data-driven culture and AI-enabled insights embedded into business processes. Most organizations sit in the middle of the maturity curve as Data Adopters, with enterprise-wide reporting and some real-time capabilities, while many are still foundational or ad hoc, relying on basic dashboards or even spreadsheets.

Even early investments are paying off: organizations at the Data Beginner stage are already seeing improvements in customer and user experience and better governance from modest upgrades to their data landscape. The largest gains such as improved forecast accuracy, more agile operations, and establishing a foundation for AI adoption show up most for Data Leaders that have built integrated, governed data capabilities and embedded analytics into decision-making.

Governance and Data Quality Move Center Stage

Data governance is becoming a core pillar of modern data strategies in the era of AI and rising regulatory scrutiny. A growing share of organizations now have enterprise-wide governance practices, and a smaller but important segment has embedded governance across the data lifecycle with automation and business accountability, while only a minority have no formal governance at all.

As organizations mature, they move from basic data ownership and policies to robust data quality standards, stewardship roles, and automated governance over metadata, lineage, and access. Data Leaders are much more likely to credit improved governance and quality with driving outcomes and to report better compliance and risk mitigation, underscoring governance as a differentiator in data and analytics success.

Centralized, Connected Data Across SAP and Beyond

Digitalization is creating an exponential increase in data volumes that span SAP and non-SAP systems, making integration a critical issue. Many organizations say their data is only partially integrated across SAP and non-SAP environments—far more than those that report seamless, intelligent data availability—while only a smaller portion have unified and governed access across systems.
To respond, creating centralized data repositories like data warehouses, data lakes, and lakehouses is one of the top actions organizations are taking, closely followed by coordinating IT and lines of business on data and analytics. Data Leaders are moving toward hybrid landscapes that blend SAP tools such as SAP BW/4HANA, SAP Datasphere, SAP Business Data Cloud, and SAP BTP with non-SAP platforms like Snowflake, Databricks, AWS, and Microsoft Azure to support unified analytics.

Cloud, Self-Service, and Emerging Tech Redefine Analytics

The technology stack underpinning data and analytics is shifting toward cloud and self-service. While on-premise data warehouses remain common, a growing proportion of organizations already use or have used cloud data warehouses and lakes. Adoption of data integration and orchestration tools, self-service and low-code/no-code analytics, and real-time streaming continues to rise.

Data Leaders are out in front with emerging capabilities. They have significantly higher adoption rates for embedded analytics in business processes, natural language querying for BI, synthetic data and digital twin modeling, and agentic AI, while organizations in the Data Adopter segment are more likely to start with generative AI copilots and assistants. For SAPinsiders, this points to a future where intuitive, AI-augmented tools help business users explore data and drive decisions without relying solely on centralized teams.

Leadership, Culture, and Investment Drive Outcomes

Organizational models play a major role in unlocking value from data. Data Leaders are far more likely to have a dedicated data and analytics center of excellence or roles such as a Chief Data Officer or head of analytics driving strategy, while many Data Beginners have no clear owner for data strategy and often rely on general technology leadership instead.

As maturity rises, the main challenge shifts from technology to change management and user adoption. Leaders cite adoption as their top barrier, yet they also report much higher rates of high or very high end-user adoption than Beginners, and a large share expect to significantly increase data and analytics investment over the next 12 months, with many others planning a slight increase.

Organizations that keep investment flat or only move incrementally risk falling further behind as Leaders compound their advantages.

What This Means for SAPinsiders

For SAPinsiders shaping 2026 data and analytics roadmaps, three priorities stand out.

• Put data leadership at the center of your strategy. Establish a data-focused leader or center of excellence to own data and analytics strategy, governance, and roadmap, rather than treating data as a secondary responsibility for general IT roles.Empower this function to prioritize data quality, security, and access, and to coordinate IT and lines of business on how data supports processes and decisions.
• Build a unified, AI-ready data foundation on SAP and cloud platforms. Centralize and connect data from SAP and non-SAP sources using modern platforms like SAP Datasphere, SAP Business Data Cloud, SAP BTP, and complementary cloud data warehouses and lakehouses.Design for data quality, security, resiliency, and regulatory compliance from the start, and make presenting data from both SAP and non-SAP systems a core requirement.
• Design for adoption with self-service, embedded analytics, and emerging AI. Expand use of self-service analytics, BI, and visualization tools to give more users governed access to trusted data.Pilot and scale embedded analytics, natural language BI, predictive models, and generative or agentic AI on top of your data foundation to accelerate decision-making and lay the groundwork for broader AI initiatives.

To explore the full data behind these findings, benchmark your own maturity against Data Beginners, Adopters, and Leaders, and dive deeper into the technologies and actions that are driving results, download the SAPinsider benchmark report “Enterprise Data and Analytics in the Era of AI.”