This year, Gartner’s Data & Analytics Summit is focused on a pivotal theme: “From Inspiring Exploration to Leading Execution.” As AI, data, and analytics strategies continue to evolve, enterprise data leaders must move beyond experimentation to operational excellence, balancing opportunity and risk while delivering tangible value to their organizations.

At Cube, we understand that AI-readiness isn’t only about cutting-edge models and on-demand insights; it’s about having the right data foundation to drive meaningful organizational impact. Cube’s universal semantic layer unifies, governs, optimizes, and integrates enterprise data for both AI and analytics, ensuring a smooth transition from exploration to execution with reusable data models and metrics across all data consumers.

Aligning AI, Data, and Analytics Strategies with Business Goals

Data leaders are well-aware of the challenges of adopting and scaling BI across their organizations. Now, they face increasing pressure to turn AI potential into business value, ensuring alignment with strategic goals while managing stakeholder expectations. However, many enterprises struggle to bridge the gap between strategy and execution, often hindered by siloed, inconsistent data across their organization.

With Cube Cloud, data leaders can confidently drive AI adoption while maintaining alignment with organizational goals and delivering measurable business impact. Cube’s universal semantic layer unifies siloed data and ensures business users, analysts, and AI applications work from a single source of truth. As a result, your data teams will realize faster time-to-value, and every level of the organization will have access to consistent, governed data for informed decision-making.

Making Data AI-Ready for Your Organization

Enterprises are quickly learning that most AI initiatives fail to scale due to data readiness issues rather than AI model limitations, as many struggle to shift from testing to production. Context is key to AI success. Without structured, contextualized, and governed data, AI outputs are prone to inaccuracies, bias, and operational inefficiencies.

Cube Cloud ensures your AI initiatives can move beyond proof-of-concept and scale confidently across the enterprise, providing reliable and trustworthy results. With AI-ready data, you can ensure AI can interpret and act on business data accurately. Data can be aggregated and optimized for real-time AI inference, improving efficiency and reducing operational costs. With Cube’s commitment to interoperability, integration with AI frameworks and other platforms powers AI-driven analytics and automation.

Delivering Trusted Insights Across the Enterprise

Enterprises must navigate an increasingly complex AI and analytics landscape, ensuring data is not only available but also accurate, consistent, and actionable. You must be able to share and reuse the same data models and metrics across data consumers. Without a unified semantic layer, inconsistent metrics and siloed data sources lead to unreliable AI outputs.

Cube empowers enterprises to deliver trusted insights at scale, driving informed decision-making across all business functions. Cube Cloud provides centralized metrics management, which allows you to define KPIs once and ensure consistency across data consumers. Additionally, API-driven data access delivers data to any application, from cutting-edge AI to modern OLAP in spreadsheets. Finally, broad integration capabilities enable seamless analytics experiences across platforms like Power BI, Tableau, and custom embedded analytics applications.

Unifying and Optimizing Enterprise Data

Effective data management is critical for scaling analytics and AI initiatives. However, enterprises often grapple with fragmented data definitions and performance issues across multiple cloud data platforms and data consumers. These discrepancies shift attention away from improving metrics and outcomes to explaining how the metric was calculated, while slow query responses degrade user experience.

Cube Cloud acts as the bridge that connects raw cloud data to meaningful business insights that accelerate data-driven innovation. Cube Cloud consolidates fragmented data definitions into a centralized, governed layer accessible by all data consumers. It also implements intelligent caching and pre-aggregation strategies to enhance query performance and reduce cloud costs. Cube Cloud empowers data teams with simplified access to governed, fast, AI-ready data.

Ensuring Security, Governance, and Compliance

As enterprises scale their AI and analytics efforts, ensuring data governance, security, and compliance is a top priority. Inconsistent governance policies can lead to regulatory risks, data breaches, and reduced trust in AI-driven decisions, which is not easily recovered once it is lost.

Cube provides enterprises with the flexible governance they need to scale AI and analytics with confidence and compliance. Using robust security controls, Cube Cloud implements role-based data access policies, user-dependent query rewrites, and data masking to ensure sensitive data is protected. For auditability and compliance, it provides automated data lineage from source to consumer. Cube Cloud enforces governance rules to ensure every AI and BI tool accesses the same, trusted data.

Meet Cube at Gartner’s Data & Analytics Summit

As enterprises transition from AI exploration to execution, Cube Cloud stands ready to deliver unified, governed, AI-ready data. By solving for key barriers for scaling AI initiatives, you can also solve for the key barriers for scaling BI initiatives too, all from one trusted place.

Let Cube help you turn your AI, data, and analytics strategies into tangible business outcomes. See you at the Gartner Data & Analytics Summit!