July 2026 Summaries

Sigma has introduced several innovative updates in July 2026, focusing on enhancing the capabilities of Sigma Agents, Sigma Assistant, and AI cost monitoring. The new Agent Builder Assistant streamlines the creation of agents from plain-language inputs, while governance has been improved to ensure agents move through the software development lifecycle effectively. Sigma Assistant now enables business users to build dashboards, apps, and forms using plain language, and the AI Columns feature allows data enrichment without coding. AI cost monitoring dashboards have been launched to provide transparency on AI-related expenses across platforms like Claude, ChatGPT, and Snowflake Cortex. Additionally, Sigma has been recognized by Databricks and Snowflake for its contributions to business intelligence and analytics, and it continues to offer features such as Workbooks as Code and real-time app building through partnerships with platforms like Databricks Lakehouse. These updates reflect Sigma's commitment to making data analytics more accessible, efficient, and cost-effective for its users.

Enterprise BI tool consolidation aims to reduce overlapping dashboards, reporting systems, spreadsheets, and embedded analytics products into a single governed presentation layer while retaining the existing cloud data warehouse and its security, lineage, and transformation infrastructure. The need commonly arises from decentralized SaaS purchases, limitations in legacy BI products, and inherited technology from mergers or reorganizations, creating redundant licenses, administrative work, inconsistent metric definitions, stale extracts, and time-consuming reconciliation across teams. A successful consolidation program should inventory tools and workflows, prioritize high-cost or high-friction use cases, verify metric and security parity before migration, roll out in phases, and retire legacy licenses around renewal dates. The proposed replacement platform should query warehouse data live, support dashboards, reports, spreadsheet-like analysis, embedded analytics, and governed AI capabilities, including permission-aware actions and writeback. The piece presents Sigma as an example of such a warehouse-native platform, claiming that its workbooks, reporting, input tables, embedded analytics, and AI agents can unify these functions while preserving warehouse governance.

Spreadsheet governance is presented as a risk-based framework for controlling business-critical end-user computing files through defined ownership, access permissions, version histories, formula protections, data-quality checks, and regulatory mapping. The proposed implementation process involves establishing scope, risk tiers, accountable owners, and industry-specific compliance requirements; inventorying spreadsheets across storage systems; ranking them by materiality, complexity, data sensitivity, and usage; enforcing least-privilege access, protected formulas, and authoritative versions; and sustaining the program through periodic reviews, retirement policies, training, and executive accountability. The discussion argues that governance has become more urgent because spreadsheets remain central to finance and operations while AI tools increase the volume of generated formulas and files. It also identifies structural limitations of file-based governance, including incomplete in-file audit trails, uncontrolled copies and version conflicts, and data that moves outside managed environments. Sigma is offered as an alternative platform that connects spreadsheet-like workbooks to live cloud warehouse data, inherits warehouse-level security, provides shared data sources and audit logs, and aims to make permissions, lineage, and change tracking platform-level controls rather than manually maintained file-based processes.

Evaluating embedded analytics platforms requires careful consideration of features beyond the standard promises of live dashboards and AI assistants, focusing on their performance in real-world scenarios with large customer bases and dynamic data models. Critical features include warehouse-native data access to ensure real-time accuracy, multitenant governance for secure tenant isolation, and embedding depth that allows for user interactivity without burdening engineering teams. Performance at scale, white-label branding for seamless integration, and grounded AI that respects data governance are also essential. Sigma's platform exemplifies these capabilities by allowing queries to execute directly in connected cloud data warehouses, thereby maintaining data integrity and security while offering robust user interaction and governance features. The platform's architecture supports scalability and adaptability, crucial for handling increasing data volumes and regulatory demands, making it a viable choice for long-term analytics integration.

Sigma's "Workflow on the Road: London" event highlighted the growing trend among finance, data, operations, and IT leaders of building custom applications within the Sigma platform to enhance efficiency and governance over traditional SaaS tools. Participants, including companies like Sagacity, Genius Sports, and Met Office, shared insights on how consolidating dashboards, reports, and single-purpose tools into fewer, more governed applications has led to significant cost reductions and more actionable data insights. The event emphasized the importance of grounding AI in well-modeled, governed data to improve business-specific applications, and showcased how features like writeback and agents can transform static reports into dynamic applications that allow real-time decision-making. Attendees also discussed the benefits of maintaining a governed backend while allowing flexible front-end development, which provides security and encourages innovation. The overall consensus was a shift towards building on a shared infrastructure, which reduces costs and increases functionality, with several attendees already initiating projects that capitalize on these insights.

Sigma's latest advancements in its Sigma Agents platform are set to redefine user interaction by enabling agents to operate outside traditional workbooks, allowing for greater flexibility and personalization. The new Agent Builder Assistant (ABA) simplifies the creation of these agents, which can now run independently, schedule tasks, and be accessed from any platform via API and MCP interfaces, enhancing operational efficiency for users. This evolution supports a shift from a workbook-centric to an agent-first approach, enabling seamless integration across various interfaces, such as Claude, Codex, and Coco. Furthermore, Sigma's platform will offer comprehensive management tools for admins to oversee agent usage and performance, ensuring transparency and control over the AI-driven processes within organizations. The initiative also includes provisions for evaluating agent effectiveness, allowing users to enhance their functionality. These updates are expected to be available by the end of Q3, providing Sigma customers with a comprehensive agent platform for building, distributing, and managing AI agents at scale.

Business intelligence (BI) and embedded analytics are distinct yet complementary approaches to data delivery, differing primarily in audience and delivery surface. While BI caters to internal employees using standalone platforms for data analysis, embedded analytics integrates analytical capabilities directly into customer-facing products, providing insights within the context of the user's workflow. This distinction impacts the architecture, governance, and platform ownership, with BI often managed by data and IT teams and embedded analytics involving product and engineering teams. Embedded analytics demands multitenant isolation and specific technical requirements like tenant-aware access control, whereas BI relies on corporate identity and role-based security. Sigma offers a unified solution, enabling both BI and embedded analytics through a single governed connection to a cloud data warehouse, maintaining data consistency and governance across internal and external audiences without duplicating infrastructure.

In 2026, selecting the right financial dashboard software is crucial for efficient financial reporting and planning, with Sigma emerging as a leading option. Sigma's platform offers live data queries from cloud data warehouses, a familiar spreadsheet interface, and native writeback capabilities, enabling finance teams to manage reporting and planning workflows without relying on SQL or external data teams. It supports governance and audit trails by inheriting security settings and allows for real-time drill-down to transaction details, essential for compliance with regulations like SOX. While other platforms like Power BI, Tableau, Looker, and Cube offer various strengths such as integration with Microsoft 365, advanced data visualization, centralized metric governance, and Excel compatibility, Sigma stands out for its ability to combine reporting and planning on a single live surface. Sigma's approach has proven effective in practice, helping companies like DoorDash and Makena Capital streamline their financial operations while maintaining data accuracy and control.

Financial analytics is a transformative discipline that elevates the finance function from merely reporting past performance to actively shaping future business outcomes through a combination of descriptive, diagnostic, predictive, and prescriptive techniques. Unlike traditional financial reporting, which focuses on compliance, financial analytics provides actionable insights by analyzing financial and operational data from sources like ERP systems and data warehouses. This approach empowers finance teams to forecast outcomes, recommend strategic actions, and make informed decisions by ensuring data freshness, allowing finance to control the modeling process, and integrating analytics directly into operational systems. Tools like Sigma facilitate this transformation by enabling finance teams to work with live data through a familiar spreadsheet interface, which supports writeback capabilities and AI-driven automation to improve efficiency and decision-making. Through Sigma, companies like Yamaha Finance and Makena Capital have significantly reduced reporting time and enhanced their financial operations, demonstrating the practical benefits of a modern financial analytics system.

Healthcare payers, such as insurers, can enhance their provider anomaly detection process using Sigma, a platform that integrates AI to streamline the identification and management of billing anomalies in claims data. Traditionally, spotting outliers relied heavily on manual analysis and static dashboards, but Sigma's approach allows analysts to interact directly with live data, using AI agents to detect and investigate anomalies like upcoding. Analysts can act on flagged claims, draft case notes with AI assistance, and escalate issues to the special investigations unit, all within the same interface. This integration facilitates a seamless workflow from detection to action, reducing the time between identifying anomalies and addressing them while keeping a comprehensive audit trail. With Sigma, teams can build and utilize apps autonomously, leveraging AI not only for anomaly detection but also for generating executive reports, ensuring that the gap between flagging an issue and taking corrective action is minimized.

Sigma AI Apps offer a solution for commodity trading desks to efficiently share and act upon time-sensitive information, traditionally gained through informal channels like phone calls or emails. This system allows traders to input and update live data directly from their screens, transforming isolated pieces of intel into shared, actionable assets. By integrating real-time updates into a central data warehouse, including details about vessels, cargo, routes, and refinery outages, Sigma ensures that all traders have access to the same critical information for decision-making and scenario modeling. The AI-powered platform also enables traders to ask questions in plain language and receive accurate responses based on governed data, enhancing their ability to make informed decisions without the need for custom-built software. Overall, Sigma's approach turns previously ephemeral information into a sustained competitive edge for trading desks.

Automated data insights represent a transformative approach to data analytics by shifting from traditional dashboards to systems that proactively deliver insights to users, allowing for timely actions based on live data. These insights are generated through a continuous process involving three interconnected layers: data, detection, and delivery. The process prioritizes precision to maintain user trust and involves automatic alerts that are tiered by severity and accompanied by actionable steps. Unlike conventional dashboards or scheduled reports that require manual checking, automated data insights actively notify users of significant metrics changes or anomalies, making them particularly relevant in today's data-heavy environments where quick, informed decision-making is crucial. Platforms like Sigma exemplify this by integrating detection and delivery within a unified system, enabling seamless workflow integration and real-time analysis directly from data warehouses such as Databricks, Snowflake, BigQuery, or Amazon Redshift. This approach not only enhances the efficiency of data handling but also ensures that insights are delivered in a timely manner to the right individuals, fostering proactive rather than reactive business strategies.

Big data analytics is a crucial discipline that addresses the lag in traditional business intelligence systems by examining large, fast-moving, and varied datasets to uncover patterns and trends that inform business decisions. As business data grows in volume, velocity, and variety, big data analytics employs advanced techniques like data mining, predictive analytics, and machine learning to provide insights beyond conventional reporting. The process involves a structured pipeline that ingests data from various sources, stores it at scale, processes it through batch or streaming techniques, and ultimately analyzes the output to generate actionable insights. Effective implementation requires prerequisites such as a defined business case, clean and governed data, and skilled personnel. Tools like Sigma facilitate this process by enabling live queries against cloud data warehouses, allowing business users to perform self-service analysis without writing SQL, while maintaining governed access and enabling action-oriented analysis.

Sigma's "Month of Agents" series explores the integration of data models, warehouse search services, and observability to enhance the functionality of Sigma Agents with real-time, reliable data. The initiative introduces the ability to connect data models directly as data sources for agents, allowing them to inherit pre-defined relationships, metrics, and governance from data warehouses, thereby streamlining the process for users. The series illustrates the use of warehouse search services to provide rapid, precise answers to unstructured data queries, enhancing the speed and performance of Sigma Agents. Furthermore, it discusses the importance of observability in monitoring agent usage and cost, offering users secure AI usage dashboards stored within their own warehouses. The series emphasizes the significance of drawing on existing semantic layers from partners like Snowflake and Databricks to provide agents with accurate, contextually grounded answers, and highlights the secure handling of usage data, which remains entirely within the customer's control. The series concludes with a look at the roadmap for Sigma Agents, focusing on their transition toward more autonomous and complex behaviors.

Embedded Business Intelligence (BI) integrates analytics functionalities such as charts, dashboards, and AI-powered querying directly into software products, allowing users to explore data without leaving the application, enhancing decision-making and product engagement. This approach contrasts with standalone BI tools by keeping data analysis within the user's workflow, offering benefits like shortened decision paths, increased product stickiness, and reduced load on internal analyst teams. Implementing embedded BI requires a robust infrastructure, including live warehouse queries and secure user tokens, and can be developed in-house or through a white-label platform, each with its own trade-offs in terms of control, engineering commitment, and time to market. A successful implementation focuses on maintaining live data connections, inheriting warehouse governance, enforcing multitenant isolation, and enabling interactive self-service analytics. Sigma offers a platform for embedded BI that runs live queries against cloud data warehouses, ensuring data freshness and governance while providing tiered functionalities and bi-directional integration to enhance user interaction and experience.

Data storytelling with AI revolutionizes traditional static reporting by leveraging live data from cloud warehouses, ensuring that narratives remain current and actionable. This approach allows for real-time updates and direct interaction with data, enabling users to ask questions in plain language and receive governed, accurate responses. Unlike traditional methods where data stories become stale, AI-driven storytelling integrates narrative, visuals, and figures from a single source of truth, preventing discrepancies and ensuring consistency. Key components of successful implementation include a live data connection, a governed semantic layer, narrative generation, a conversational interface, and inherited governance to maintain security and accuracy. Tools like Sigma facilitate this process by providing a platform that supports AI-generated content while maintaining governance and security. They allow for seamless integration of narrative generation with real-time data interaction, enabling teams to make informed decisions without the burden of manual report assembly.

Sigma has introduced AI cost monitoring templates for platforms like Claude, OpenAI, and Snowflake to help businesses better understand and manage their AI expenditures. As AI costs have become a significant and complex budget item, these templates provide a unified view by integrating spend data from various AI services into standardized Sigma dashboards. They allow users to break down AI expenses by service, team, use case, and more, facilitating a clearer connection between costs and business outcomes. The templates are designed to reconcile the fragmented reporting formats of different AI services, offering insights into which use cases and teams are driving costs and whether those expenses are translating into value. Sigma's dashboards also include a built-in AI usage dashboard, which tracks how AI features are used within Sigma, enabling organizations to assess the value generated by their AI investments. These tools are part of Sigma's broader effort to bring accountability and visibility to AI spending, akin to what FinOps achieved for cloud expenditures, helping organizations optimize their investments based on measurable returns.

Big data analytics tools in 2026 serve as interfaces between cloud data warehouses and users like business analysts and AI agents, focusing on dashboarding, self-service exploration, AI-assisted analysis, and agentic workflows. The article evaluates five analytics tools—Sigma, Microsoft Power BI, Tableau, Looker, and ThoughtSpot—based on architecture, AI grounding, governance, self-service capabilities, and pricing. Sigma is highlighted as the top choice for enterprise data teams due to its ability to run live queries on cloud data warehouses, allowing business users to self-serve and build AI apps without compromising governance. It features a spreadsheet interface that compiles to SQL, enabling analysis of extensive datasets without requiring SQL expertise, and offers AI capabilities that inherit existing security permissions. Other tools like Power BI, Tableau, Looker, and ThoughtSpot excel in specific areas such as integration with Microsoft products, visualization, code-governed semantics, and search-first self-service, respectively, but Sigma's comprehensive approach makes it a standout option for organizations using cloud data warehouses seeking a robust and scalable analytics solution.

Sigma Agents offer a flexible platform for creating and managing AI-driven agents within cloud data warehouses, with a focus on governance, permissions, and integration capabilities. The Agent Builder Assistant (ABA) aids in constructing Sigma Agents, which can be promoted from development to production through a software development life cycle (SDLC) workflow using version tags. Sigma Agents can be extended with MCP connectors to interact with external platforms such as Google Drive, enabling them to access diverse data types. Warehouse agents, configured as tools, provide curated data and can be orchestrated across different platforms, including Snowflake and Databricks. User permissions are maintained to ensure that agents do not exceed authorized access, reinforcing secure and controlled operations. These features aim to enhance the functionality and reliability of Sigma's AI solutions for end-users, offering a robust orchestration layer that integrates with various data resources and APIs.

The modern data stack represents a shift from traditional, on-premise data systems to cloud-native, modular tools that efficiently transfer data from various source systems into a central warehouse for transformation and analysis. It replaces legacy systems that relied on monolithic ETL pipelines and closed BI suites, instead using cloud infrastructure to separate storage and compute, enabling independent scaling and more agile data management. The modern data stack integrates through APIs, allowing flexibility in tool selection and operation. It consists of six layers: data sources, ingestion and integration, storage and compute, transformation, analytics, BI, and activation, with governance and observability spanning all layers. Key benefits include scalability, faster insights, centralized governance, and self-service capabilities for business users. Best practices for modernizing a data stack highlight the importance of anchoring on a cloud data warehouse, choosing ELT over ETL, centralizing metric definitions, and keeping analysis and AI agents within governed systems. Sigma, as a consumption-layer platform, exemplifies these principles by enabling real-time analytics and decision-making directly on live warehouse data without requiring data extraction, thus maintaining governance and integrity across operations.

Deciding whether to build multitenant analytics in-house or purchase a white label analytics platform involves considering the depth of customization and brand integration required, as white label solutions can range from simple logo swaps to full brand ownership. Building in-house can demand significant engineering resources and risk security issues, while buying can expedite deployment if the chosen platform meets specific product needs without requiring additional governance layers. The guide contrasts embedded analytics, which integrates vendor identity into a product, with white label analytics, which eliminates vendor branding for a seamless customer experience. It also outlines five criteria for selecting a platform: depth of white-labeling, multitenant data isolation, developer experience, governance inheritance, and pricing model. The text reviews several leading platforms—Sigma, Domo, Tableau, Microsoft Power BI Embedded, and Looker—highlighting their strengths and limitations, such as Sigma's comprehensive white-labeling and governance features and Looker's robust semantic modeling but steep learning curve. The guide concludes with steps to choose the right platform based on data architecture, brand needs, tenant isolation, and cost projections, ultimately recommending Sigma for its warehouse-native governance and full edit mode capabilities.

Embedded reporting serves as a foundational analytics feature for SaaS product teams by providing scheduled, audit-ready reports directly through the product's interface, ensuring governance and branding alignment. After launching such a feature, customer demands typically evolve towards self-serve analytics, where users desire the ability to filter, drill down, and interact with live data independently. By leveraging a warehouse-native platform, teams can ensure that both embedded reporting and self-serve analytics share a unified foundation, including governance and security models, which prevents redundant data models and ensures consistent metric definitions. Sigma exemplifies this approach by enabling embedded reporting and analytics directly on live cloud data warehouse data, supporting features like row-level security, multitenant isolation, and the ability to extend from scheduled reports to interactive analytics without additional platforms or re-architecting security models. This architecture allows product teams to respond to evolving customer needs without significant overhead, positioning reporting and analytics as potential revenue streams through tiered offerings.

Retail analytics is a crucial practice in the big data-driven retail industry, focusing on transforming vast amounts of data from various sources like POS systems, CRM platforms, and external signals into actionable insights that enhance decision-making regarding inventory, pricing, and customer retention. It encompasses four analytical modes—descriptive, diagnostic, predictive, and prescriptive—that build on each other to provide a comprehensive understanding of retail operations. To effectively implement retail analytics, retailers must ensure role-based data governance, self-service access for business teams, and the ability to act on insights within the same system. Sigma offers a platform that integrates these elements by connecting live cloud data warehouses with user-friendly interfaces, enabling real-time data querying and facilitating seamless transitions from analysis to action. By centralizing and governing data access, Sigma allows retail teams to make informed decisions quickly, ultimately leading to improved margins, inventory efficiency, and customer retention.

Automating financial reporting can significantly enhance efficiency and accuracy by reducing manual data crunching and enabling finance teams to focus more on analysis rather than assembly. The process involves consolidating data into a governed warehouse layer, defining metrics in a semantic layer, building reports directly on this model, and maintaining human oversight for flagged exceptions. Key prerequisites include having clean source data, shared metric definitions, documented transformation logic, and role-based access controls. Sigma provides a platform that facilitates these processes by allowing users to build reports directly on live cloud data warehouse data, ensure data accuracy with live queries, and streamline the workflow with features like input tables, exception-review workflows, and agentic workflows. Ultimately, automation leads to faster, more reliable decision-making, minimizes manual errors, and scales seamlessly with business growth, transforming financial reporting into a structured, repeatable process.

Sigma has introduced App Templates in public beta, providing a curated collection of templates designed to help teams build AI-driven applications more efficiently by using pre-structured examples. These templates cover common use cases such as project management, revenue forecasting, and demand planning, enabling teams to create working apps quickly by connecting their own warehouse data to these templates. Each template showcases Sigma's features like Input Tables, Sigma Agents, and AI Columns, allowing both new and experienced users to understand and utilize the platform's capabilities. The templates are fully editable, enabling customization to meet specific organizational needs, and they integrate seamlessly with Sigma's warehouse-native architecture, ensuring that existing data governance measures remain intact. With App Templates, teams can bypass the challenges of starting from scratch, accelerating the app-building process while maintaining flexibility for personalization.

Financial planning and analysis (FP&A) teams often struggle with time-consuming data collection and manual reporting processes, which limits their ability to focus on analysis crucial for strategic decision-making. This comprehensive guide emphasizes the importance of automating FP&A reporting to enhance efficiency and accuracy by leveraging a centralized cloud data warehouse such as Databricks, Snowflake, BigQuery, or Amazon Redshift. By automating data pulls, modeling financial data, and building live report templates, finance teams can ensure reports are consistently updated and reflect current data, enabling faster and more informed decisions. The guide outlines the benefits of automation, including a single source of truth, reduced manual reconciliation, and the ability to reinvest time saved into advanced analysis and AI-assisted commentary. Sigma is highlighted as a tool that facilitates this automation by providing an interface familiar to finance professionals, allowing them to work directly with live data without needing extensive IT involvement, and integrating AI to assist with data analysis and reporting.

AI agents, which are software systems that perceive, decide, and act toward human-defined goals, are categorized into six types based on their decision-making behavior: simple reflex, model-based reflex, goal-based, utility-based, multi-agent systems, and learning-augmented agents. Each type has distinct strengths and limitations, such as simple reflex agents operating on fixed rules without memory, and learning-augmented agents improving decisions over time through feedback. These agents differ from other AI implementations like chatbots and rule-based automation, as they offer bounded autonomy, where human-defined objectives and guardrails guide their actions. Sigma's platform integrates these AI agents with live cloud data warehouses, allowing them to operate under permissions and governance models, which ensures data security and compliance while offering business users flexibility and IT control. Sigma facilitates interaction with these agents through a user-friendly interface, enabling various workflows, from conversational queries to fully autonomous tasks, transforming how business teams leverage data for decision-making.

A sales dashboard is a centralized tool that consolidates data from CRM, billing, and other sales systems to provide a unified view for sales reps, managers, and executives, facilitating informed decision-making and improving efficiency. The guide emphasizes the importance of defining audience-specific questions, preparing clean and governed data, and ensuring consistent metric definitions before building the dashboard. Successful dashboards rely on a single source of truth, role-based access, and a build platform that accommodates both technical and non-technical users. Key benefits include reducing manual reporting, enhancing team-wide visibility, and enabling faster decision-making through real-time data and role-specific views. Sigma supports sales dashboard creation by providing a spreadsheet interface that interacts directly with live data in cloud warehouses, allowing teams to build, adjust, and act on data without technical bottlenecks, thereby transforming the dashboard into an actionable tool rather than a static report.

Agentic workflows represent a significant evolution in business intelligence, where AI agents autonomously execute data-driven tasks, transforming raw data into actionable insights without continuous human intervention. These workflows leverage large language models (LLMs) to reason, plan, and execute multi-step processes across various tools within a governed data environment, ensuring that every action is logged and permissions are strictly enforced. Unlike traditional chatbots or scripted automation, agentic workflows operate with a high degree of autonomy, adapting to dynamic inputs and conditions while maintaining human checkpoints for strategic oversight. Sigma, a platform facilitating these workflows, integrates seamlessly with major data warehouses like Databricks, Snowflake, BigQuery, and Amazon Redshift, offering robust governance, audit trails, and the ability to run SQL queries natively. This setup empowers business teams to rapidly deploy and scale analytical applications and workflows, maintaining control and security while enhancing efficiency and flexibility in data operations.

An accounts payable (AP) dashboard is a vital tool for finance teams to manage payables by providing a real-time, interactive view of open invoices, due dates, balances, and payment statuses. Unlike static reports, dashboards keep data current and allow for filtering and drill-downs, helping teams address operational issues promptly, such as overdue payments or approval bottlenecks. The dashboard should be built using live data from systems like ERPs and AP automation platforms, ensuring it reflects the latest financial activities. It includes crucial metrics like aging buckets, days payable outstanding, and upcoming cash outflows to guide daily decisions. Best practices involve setting up role-based access controls to protect sensitive data and integrating AI tools for enhanced insights and automation. Sigma, a cloud-native analytics platform, facilitates the creation and management of these dashboards by allowing finance teams to work directly with live data in a familiar spreadsheet interface, while maintaining data governance and security.

Sigma has introduced the new Agent Builder Assistant (ABA), a natural language interface designed to simplify the creation of custom AI agents within Sigma's cloud data warehouse environment. Previously, building these agents required manual configuration, but ABA allows users to describe workflows in plain language, automatically generating the necessary tools and instructions. This update is part of the "Month of Agents" series, aimed at enhancing Sigma Agents by making them more intuitive and accessible for users of all technical levels. ABA can suggest agent possibilities based on added data sources, and it utilizes large language models to translate user prompts into actionable tools, streamlining the agent-building process significantly. The initiative reflects Sigma's commitment to making AI agent creation more efficient, with the ultimate goal of enabling users to quickly and effectively deploy agents tailored to their specific needs.

Supply chain analytics is a multi-layered capability stack that integrates data, analysis, and action on top of a cloud data warehouse, facilitating the transformation of raw data into operational insights. The guide outlines the four types of supply chain analytics tools—descriptive, diagnostic, predictive, and prescriptive—that answer key questions from "What happened?" to "What should we do?" and emphasizes the importance of a robust platform that supports live data queries, self-service for business users, native writeback, enforced governance, and AI operations on governed data. The analytics stack is composed of three capability layers: the data layer, which consolidates diverse data sources into a central warehouse; the analysis layer, which converts data into actionable insights; and the action layer, which implements insights into decisions through features like alerts and workflow triggers. Sigma, a prominent tool in the analytics and action layers, enables live queries and decision writebacks directly to the warehouse, ensuring streamlined operations without compromising governance. The guide underscores best practices such as starting with a centralized data warehouse, defining consistent metrics, and ensuring governance across all layers to achieve a cohesive and effective supply chain analytics stack.

Sigma has been recognized for the second consecutive year in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms, highlighting its innovative approach in the evolving analytics market. Unlike traditional business intelligence tools focused on static reporting, Sigma provides a dynamic runtime layer that allows businesses to run analytics, applications, and agents directly on live data in the warehouse. This approach facilitates a seamless transition from insight to action without leaving the governed environment. The platform's unique spreadsheet-style interface enables users to explore live data and build applications or agents that can act on it, thereby bridging the gap between data analysis and operational execution. Sigma also integrates advanced AI capabilities, such as Sigma Assistant and Sigma Agents, to convert plain language prompts into actionable tasks and automate responses to business conditions. With a strong emphasis on governance, security, and self-service, Sigma ensures that business users can innovate rapidly while maintaining IT oversight. Garnering positive feedback on Gartner Peer Insights, Sigma boasts a high customer satisfaction rating, with substantial adoption across various industries, including multiple Fortune 10 companies. Looking forward, Sigma aims to continue enhancing its governed runtime environment to empower teams to build AI-driven applications directly on live cloud data, thereby transforming analytics from a reporting tool into a comprehensive platform for business operations.