January 2026 Summaries

Sigma's Input Tables, now generally available for Google BigQuery customers, enable secure and dynamic write-back capabilities, allowing users to build AI applications, operational workflows, and scenario models directly on governed warehouse data. This feature addresses common challenges such as data governance failure, productivity loss, and costly maintenance by facilitating a controlled, bi-directional flow of data within the Sigma interface, eliminating the need for complex ETL jobs and external databases. By integrating seamlessly with BigQuery, Sigma Input Tables allow for rapid prototyping, advanced scenario planning, and the blending of external data with live warehouse information, ensuring informed, real-time decision-making for operational applications like financial forecasting and sales planning. This integration reduces total cost of ownership and engineering overhead while maintaining unified control and governance, empowering business teams to create bespoke solutions without compromising data integrity. The introduction of Sigma Input Tables represents a significant step in enhancing data agility and operational efficiency, backed by the scalability and speed of Google BigQuery.

On March 5, 2026, Sigma will host its inaugural user conference, Workflow, at Convene in San Francisco, focusing on building, scaling, and operationalizing AI applications within Sigma's platform. This one-day, in-person event is designed for app builders, analytics engineers, operations leaders, and data leaders to explore how Sigma's AI apps and live cloud analytics enable teams to automate decisions and streamline workflows directly on their data. Attendees will have the opportunity to learn directly from Sigma's product leaders, engage in hands-on training sessions tailored to all skill levels, witness live app-building competitions, and explore real customer use cases demonstrating successful transitions from traditional spreadsheets to Sigma's integrated system. The conference aims to foster a community of like-minded professionals, offering a platform for networking and inspiration, with limited spots available for those eager to enhance their workflow capabilities using Sigma.

Unstructured data, which comprises about 80%-90% of the data organizations collect, includes diverse formats like PDFs, images, and audio that contain valuable business insights but are often underutilized due to their absence from data warehouses. Sigma and Snowflake address this challenge by enabling businesses to explore unstructured data within their existing governed environments, using the same permissions and security models as structured data. This integration allows for the analysis of unstructured data at scale, facilitating insights that connect back to structured warehouse data. By leveraging AI functionalities, users can ask questions across multiple data types, gaining insights that were previously difficult to obtain. This approach redefines the analytics landscape by incorporating unstructured AI into business intelligence workflows without compromising governance or security, allowing for repeatable and scalable analysis that informs business decisions.

Sigma's mission to empower enterprises with secure, scalable access to live data has evolved significantly by 2025, focusing on flexibility in organization setup and management to accommodate the growing integration of AI into business-critical workflows. The company has addressed key challenges in enterprise-scale data platforms, such as flexible security and governance, end-to-end data security as AI becomes central to decision-making, and balancing ease of use for business users with control for developers. Innovations like Sigma Tenants, customer-managed keys, and expanded identity and authentication options have been introduced to enhance operational efficiency, security, and user management. These advancements allow enterprises to manage complex environments with centralized visibility while maintaining strict access controls and compliance across regions. Sigma's platform facilitates the embedding of AI capabilities into workflows, ensuring that data-driven applications operate within existing governance structures. As Sigma continues to expand its enterprise offerings, it aims to extend robust control and operational rigor to AI applications, integrating them into broader software development lifecycles without compromising security or adding complexity.

Generative AI is becoming essential in analytics, leading teams to expect AI agents to handle complex queries, automate analyses, and act on results, yet the challenge remains in transitioning from prototypes to production while providing large language models (LLMs) with the necessary context. Sigma focuses on curating context for AI, emphasizing the importance of three context pillars: data warehouse and transformation metadata, semantic layer integration, and user input, to ensure accurate and relevant AI outputs. This curated context helps AI models understand unique business definitions and logic, enabling more precise and aligned outputs. By integrating these elements, platforms like Sigma enhance AI's capacity to collaborate in analytical workflows, transforming them into interactive, data-driven applications that align with business needs.

MAMBA, developed by Hakkoda, an IBM company, is an innovative asset management analytics app built using Sigma's AI capabilities and direct connection to Snowflake. It aims to resolve the inefficiencies in traditional asset management by providing a unified, real-time view of portfolio and market data, allowing for seamless decision-making and reducing the need for ad hoc report generation. MAMBA focuses on delivering a single version of truth, enabling real-time analytics tailored for various user personas like portfolio managers and risk teams while maintaining data security and scalability with role-based access and reusable components. The app's design process emphasized user involvement, starting with prototyping in Sigma's environment and iterating based on feedback to ensure an intuitive interface that integrates analytics into the workflow rather than as a separate step. The app's dynamic features allow for interactive data exploration, making it a powerful tool for enhancing speed and accuracy in decision-making. The future of MAMBA includes leveraging AI and predictive models to further enhance its capabilities, with plans to integrate features like Snowflake Cortex for advanced analytics. The app is positioned as a tailored solution for the asset management industry, distinguishing itself from generic business intelligence tools.