For decades, business intelligence has been the engine of corporate decision-making. The promise was simple: gather all of your company’s data, process it, and present it in a way that reveals insights and allows leaders to make informed choices. However, the reality of this promise has always been fraught with friction. Traditional business intelligence tools were born in an on-premise world, a world where data was stored in servers down the hall, and data volumes were measured in gigabytes, not petabytes. This old world was defined by a rigid and slow process. Data was extracted from operational systems like a customer database or a sales ledger, transformed into a usable format, and then loaded into a central data warehouse. This process, known as ETL, often ran only once a night.
The tools built for this environment were heavy, complex, and designed for a specialized class of user: the business intelligence developer. These developers would take requests from the business, spend days or weeks building a data model, and then publish a static dashboard. If a marketing manager wanted to see a simple breakdown of campaign performance by a new region, they would have to file a ticket and wait. This created a massive bottleneck, turning the data team into a “report factory” and leaving business users feeling frustrated and disempowered. The data was there, but it was locked away, accessible only through a small group of technical gatekeepers.
The Frustration of Stale Data
Perhaps the single greatest complaint about traditional business intelligence is the problem of stale data. The BI dashboard finally loads after a long wait, but the numbers on the screen reflect the state of the business as of yesterday, or worse, last week. You wait for a refresh, only to be told the next one is not scheduled until midnight. This experience is familiar to countless professionals. To get their answers, they resort to exporting the data to a familiar tool like Excel, creating a local, static copy. This single act shatters the entire concept of a “single source of truth.” Suddenly, dozens of different versions of the truth are circulating through email, all of them outdated the moment they were exported.
This reliance on “data extracts” or “snapshots” was a necessary evil of the old architecture. The central data warehouse was often too slow or too expensive to query directly for ad-hoc analysis. BI tools compensated by creating their own proprietary, in-memory data engines. They would copy the data out of the warehouse and into their own system. This made the dashboards fast, but it came at a high cost. The data was perpetually out of date, and the organization was spending a fortune on redundant systems to store and process multiple copies of the same information. Teams were making critical decisions based on a lagging indicator, looking in the rearview mirror to decide where to steer the company.
Why Traditional BI is Slow
The slowness of traditional BI is not just a user-interface problem; it is an architectural one. The entire process is a series of handoffs and data movements. First, the data has to be moved from the live, operational databases to a staging area. Second, a complex ETL job has to run to clean, transform, and model this data. Third, this transformed data has to be loaded into the enterprise data warehouse. Fourth, the business intelligence tool has to copy this data again, pulling it out of the warehouse to build its own in-memory extract. Every step in this chain involves moving massive amounts of data from one system to another, and each step takes time.
This complex plumbing is not just slow; it is also brittle. If a single step in the process fails—if a column name changes in a source system or a network connection drops—the entire pipeline can break, and no one gets their reports. The data team then has to scramble to fix the pipeline, manually re-running jobs and validating the data. This reactive, maintenance-heavy workload is what keeps data teams from focusing on higher-value activities. The system was designed for stability, not speed, and in the modern economy, speed is what matters most.
The ETL Bottleneck: A Barrier to Answers
The extract, transform, and load process, or ETL, has been the backbone of data warehousing for a generation. It was the only way to make slow, on-premise databases usable for analytics. Operational databases are optimized for writing data quickly, not for the massive, complex queries needed for analysis. ETL solved this by pre-calculating, pre-aggregating, and pre-joining all the data into a format that was perfect for reporting. This worked perfectly as long as the business questions were predictable. The data team could build a “cube” or a data model that answered the 100 most common questions the business had.
The problem is that modern business questions are not predictable. A marketing manager does not want to just see “sales by region.” They want to see “sales by region, for new customers acquired via this specific social media campaign, compared to the same cohort last month, but excluding all returns processed in the last 48 hours.” This type of ad-hoc, exploratory question cannot be answered by a pre-built data model. It requires querying the raw, granular data. In the traditional BI world, this query was impossible. The raw data was not even in the BI tool, and the underlying warehouse was too slow to handle it. This is the bottleneck that modern analytics aims to break.
The Rise of the Cloud Data Warehouse
The entire landscape of data analytics was fundamentally and permanently changed by the invention of the cloud data warehouse. Platforms like Snowflake, BigQuery, and Redshift introduced a new architecture that was built from the ground up for the cloud. These platforms separated the concept of “storage” from “compute.” This was a revolutionary idea. It meant you could store nearly infinite amounts of data at a very low cost, and then spin up powerful, elastic computing clusters to query that data when you needed them. You no longer had to buy massive, expensive servers to handle your peak query load; you could just pay for compute by the second.
This new architecture made it possible to store all of your data—raw, semi-structured, and structured—in one central, scalable, and affordable location. The performance of these cloud warehouses was staggering. They could execute complex queries on billions of rows in seconds, a task that would have taken hours or simply failed on traditional on-premise systems. This immense power created a new “center of gravity” for data. The cloud data warehouse became the undisputed single source of truth. And this new, powerful architecture made the old BI tools look obsolete.
A New Architecture Demands a New Tool
The rise of the cloud data warehouse created a massive architectural mismatch. The new warehouse was incredibly fast, scalable, and held all the live, granular data. The old BI tools, however, were still built around the old model of copying data. They would connect to a powerful platform like Snowflake and immediately try to extract the data, creating a stale, slow, and redundant snapshot. This was like buying a Ferrari and only being allowed to drive it in your driveway. The primary value of the cloud warehouse—its live data and elastic compute—was being completely ignored by the tools designed to analyze it.
It became clear that a new kind of BI platform was needed. A tool that was “cloud-native,” just like the warehouses it connected to. This new tool would not copy data. It would not have its own data engine or its own storage. It would be a thin, intelligent layer that sat directly on top of the cloud data warehouse. It would leverage the warehouse’s power, not fight against it. It would push all computation down to the warehouse, allowing users to analyze live, granular data at scale. This is the concept behind warehouse-native BI, and it is the paradigm that Sigma BI was built to champion.
What is Cloud-Native Analytics?
Cloud-native analytics is a modern approach to business intelligence that is designed specifically to leverage the power and architecture of the cloud. Unlike traditional BI, which often involves on-premise servers and scheduled data refreshes, a cloud-native platform connects directly to your cloud data warehouse. This means there is no need to move, copy, or extract data. The analysis is performed directly on the live, complete dataset that resides in the warehouse. This eliminates data staleness and ensures that everyone in the organization is looking at the same, most current information.
This approach also fundamentally changes the cost and scalability of BI. Instead of paying for and maintaining a separate, redundant BI server and data engine, a cloud-native tool uses the compute resources of your existing data warehouse. This “pay-as-you-go” model is often far more cost-effective, as you are only paying for the queries you run, not for idle infrastructure. It also means the platform scales elastically. If you need to run a massive query on billions of rows, the platform simply uses the warehouse’s elastic compute to do so, without any performance bottlenecks or manual reconfiguration.
Introducing the Warehouse-Native BI Concept
Sigma BI is a leading example of a cloud-native analytics and business intelligence platform that works directly with your cloud data warehouse. The core idea is that it does not copy or move data. It acts as an intelligent “pane of glass” that sits on top of your central data repository. It connects directly to platforms like Snowflake, BigQuery, and Redshift, and it pushes all computation down to the warehouse. This means you are always working with the most current data, with no need for extracts, snapshots, or refresh schedules. When you build a chart in this system, you are running a live query against your warehouse.
This warehouse-native approach has several profound benefits. First, performance is optimized. The platform uses its own query engine to streamline compute usage, helping teams run complex queries faster while keeping cloud costs under control. Second, it integrates seamlessly into any major cloud environment, whether it is AWS, Azure, or GCP, making it easy to embed into an existing cloud stack. Finally, it inherits the security of the warehouse. Enterprise-grade features like row-level access controls and audit logs are managed in one central place, ensuring data stays protected and compliant without having to manage security policies in two separate systems.
Defining the Modern BI Platform
Sigma is a cloud-native analytics and business intelligence platform designed to unlock the full potential of the modern cloud data warehouse. At its core, it is a tool that allows anyone in an organization, regardless of their technical skill, to explore live data using a familiar spreadsheet-like interface. This approach is a direct response to the failures of traditional BI. It aims to eliminate the bottlenecks created by data extracts, stale reports, and the reliance on a small group of data specialists. By providing a user-friendly and powerful interface that sits directly on top of the warehouse, it enables true data self-service, real-time collaboration, and secure governance.
The platform is not a replacement for your data warehouse; it is the interface that makes your warehouse usable for everyone. It bridges the gap between the immense power of cloud compute platforms and the business users who need to make decisions based on the data those platforms hold. This guide will explore the core architecture of this system, explaining how it works, what makes it different, and why it represents a new and more efficient way to work with live data.
The Core Principle: No Data Movement
The most important architectural principle to understand is that the platform does not copy or move data. Unlike traditional BI tools that require you to build data extracts or in-memory cubes, this system leaves your data exactly where it is: securely in your cloud data warehouse. This “warehouse-native” approach is fundamental. When a user logs in and opens a worksheet, they are not looking at a snapshot of the data. They are opening a live, secure connection directly to their Snowflake, BigQuery, or Redshift instance. This single design choice is the source of all its major benefits.
Because there is no data movement, data staleness is eliminated. The moment new data is loaded into your warehouse, it is immediately available for analysis. This is critical for teams in fast-moving environments like finance, marketing, or operations, who need to make decisions based on what is happening now, not what happened yesterday. This also dramatically simplifies the data architecture. There are no redundant data pipelines to manage, no extracts to refresh, and no separate BI servers to maintain. The cloud data warehouse is the single source of truth, and this BI tool is the single, unified interface to access it.
Live, Warehouse-Native Analytics Explained
When a user interacts with the platform—by building a chart, applying a filter, or creating a pivot table—they are dynamically generating a query. This platform’s intelligent engine translates that user action into an optimized, precise SQL query. This query is then sent directly to the cloud data warehouse, which executes it using its own powerful compute resources. The warehouse sends back only the result set, which the platform then visualizes for the user. This entire round-trip process happens in seconds, giving the user the experience of interacting with a live system.
This architecture is the key to its scalability. A traditional BI tool would choke if a user tried to explore a table with billions of rows because it would first have to ingest all those rows. This platform does not care if the table has a billion rows or a trillion. It simply sends the query to the warehouse, and the warehouse, which is designed for this scale, handles the work. This allows business users to perform complex, ad-hoc analysis on granular, full-fidelity data without ever needing to ask a data engineer for help or for a smaller, aggregated dataset.
How Sigma Connects to Your Data
Setting up the connection is a straightforward process that highlights the security of the native architecture. The platform connects to your data warehouse using a dedicated, read-only service account. The data team creates this account within the warehouse and grants it specific permissions, defining exactly which databases, schemas, or tables the BI tool is allowed to see. The platform is then configured with the credentials for this service account. From that moment, all interactions are governed by the permissions you set in your warehouse.
This means you do not need to manage security in two different places. Your existing data governance policies are automatically inherited and enforced. If a user does not have permission to see a “Salary” column in the warehouse, that column will not be visible to them in the BI tool. This centralized governance model is a massive relief for data teams, as it ensures that data remains secure and compliant by default, even as you roll out self-service analytics to thousands of users across the organization.
The Power of Pushing Computation Down
The concept of “pushing computation down” is central to the warehouse-native model. In the old BI world, the architecture was “pull-based.” The BI tool would pull massive amounts of data out of the warehouse and then perform all the computations—the aggregations, joins, and calculations—using its own server and in-memory engine. This was computationally expensive, slow, and created the data redundancy and staleness problems we have already discussed.
The new model is “push-based.” The BI tool, acting as a smart client, pushes the computational work down to the data warehouse. Why is this better? Because the data warehouse is a massively parallel processing system specifically designed to execute these complex computations at an enormous scale, right where the data lives. It is far more efficient to send a small query to the data than it is to move petabytes of data to the query. This leverages the multi-billion dollar investment in research and development that has gone into building modern cloud warehouses, allowing a lightweight BI tool to deliver heavyweight performance.
The Alpha Query Engine: Optimizing for Speed
While pushing computation to the warehouse is the core strategy, it is not just a simple query pass-through. The platform employs a sophisticated component called the Alpha query engine, which acts as an optimization layer. This engine is responsible for translating the user’s actions in the spreadsheet interface into the most efficient SQL query possible. It streamlines compute usage to help teams run complex queries faster while minimizing the computational cost, which is a key factor in keeping cloud bills under control.
This engine is what allows a non-technical user, who knows nothing about SQL, to perform an analysis that would be equivalent to a highly complex, multi-step query. The user simply adds a column, creates a pivot table, and applies a filter. In the background, the engine is intelligently combining these actions, pruning unnecessary data, and constructing a single, optimized query to send to the warehouse. This ensures that even novice users can explore data at scale without accidentally running a costly or inefficient query. It makes the power of the warehouse accessible to everyone, safely.
Enterprise-Grade Security and Governance
Security is not an afterthought in a warehouse-native architecture; it is the foundation. Because the platform connects directly to the warehouse, it inherits all the robust security and governance features you have already implemented. This includes row-level access controls, data masking policies, and user authentication. If your warehouse is configured to only show a sales manager the data for their specific region, that is the only data they will see in this BI tool. There is no risk of a data extract accidentally leaking information from other regions.
Furthermore, the platform provides its own layer of governance and auditing features. Administrators can see who is accessing what data, what queries are being run, and which dashboards are most popular. Audit logs provide a clear, centralized trail of all data activity, making it easy to ensure compliance with regulations like GDPR or HIPAA. This dual-layer security—inheriting the warehouse’s core policies and adding its own detailed auditing—provides a secure, enterprise-grade environment for self-service analytics.
A Truly Multi-Cloud Environment
Businesses today rarely live in a single, homogenous cloud environment. They may use one provider for their applications, another for machine learning, and a third for their data warehouse. A modern BI tool must be able to operate seamlessly within this multi-cloud reality. This platform is designed to be cloud-agnostic. It integrates seamlessly with all the major cloud data warehouses, including those on Amazon Web Services, Azure, and Google Cloud Platform. This flexibility makes it easy to embed into your existing cloud stack, whatever it may be.
This multi-cloud capability also extends to data integration. While the primary analysis is done on the warehouse, the platform can also connect to other data sources to enrich the analysis. It can pull data from common tools or cloud storage, allowing you to join your live warehouse data with data from your finance tools or customer relationship management systems. This provides a truly holistic view of the business, all within a single interface, without the need for complex, manual data-blending in spreadsheets.
The Revolution of the Spreadsheet Interface
The single most important feature for its widespread adoption is its familiar, spreadsheet-like interface. For decades, the one tool that every business user—in marketing, sales, finance, and operations—knows how to use is the spreadsheet. Traditional BI tools abandoned this familiar paradigm, forcing users to learn a new, complex interface of shelves, dimensions, and measures. This created a steep learning curve and a barrier to adoption. This new platform’s core design insight was to embrace the spreadsheet, not replace it. It gives users an interface that feels like Excel or Google Sheets, but with the power of a petabyte-scale cloud data warehouse behind every cell.
This familiar UI dramatically lowers the barrier to entry. There is no new language to learn, no complex training required. Users can immediately start exploring data, creating formulas, building pivot tables, and applying filters, all using skills they already possess. This is not just a superficial skin; the interface is a powerful, fully-featured analytical workbench. Users can perform complex joins, aggregations, and window functions without writing a single line of SQL. This instant familiarity is what empowers non-technical teams to move from being passive consumers of data to active, self-sufficient explorers.
Why a Familiar UI Matters for Adoption
The biggest challenge for any new enterprise software is adoption. A company can spend millions of dollars on a powerful new BI platform, but if business users find it confusing or intimidating, they will not use it. They will revert to their old, comfortable workflows: exporting data to Excel. This platform’s spreadsheet interface is its solution to the adoption problem. It meets users where they are, eliminating the fear and friction associated with new, specialized tools. This familiar starting point encourages curiosity and exploration, leading to a much faster and wider adoption across the organization.
This approach transforms the dynamic between business teams and the data team. When users can answer their own questions, the data team is freed from the endless queue of ad-hoc report requests. A marketing manager can explore campaign data on their own. A sales operations leader can build their own pipeline dashboard. This self-service model is only possible because the interface is intuitive. The spreadsheet UI is the key that unlocks the data for the rest of the organization, moving the company from a state of data-dependency to one of data-literacy.
Building Dashboards and Reports with Ease
While ad-hoc exploration is crucial, organizations still run on standardized reports and dashboards. The platform provides a powerful, drag-and-drop environment for building interactive dashboards quickly. A user can explore data in a worksheet, create a visualization like a bar chart or a line graph, and then add that visualization to a dashboard. This seamless workflow from exploration to presentation is a major advantage. All elements are linked, so filtering on one chart instantly updates all other charts on the dashboard. This allows for a rich, interactive experience where users can drill down and explore the data from a high-level overview to granular details.
This ease of use extends to sharing and reporting. Once a dashboard is built, it can be securely shared with other users or teams with a simple link. Users can schedule recurring reports, setting a dashboard to be automatically emailed to their team as a PDF or image every Monday morning. They can also set up alerts, so the system actively monitors key metrics for them. For example, a user can set an alert to receive a notification if sales dip below a certain threshold or if website traffic suddenly spikes. This combination of easy building, interactive exploration, and proactive alerting makes the entire reporting lifecycle more efficient.
Real-Time Collaboration: The End of Version Control
In the traditional BI world, collaboration is a painful process. If two people are working on the same report, they are likely passing an Excel file back and forth, leading to version control nightmares like “Report_vFinal_v2_JohnsEdits.xlsx.” This platform solves this problem by being a truly collaborative, cloud-native tool. Much like a modern office document, it allows multiple people to explore and edit the same dashboard or worksheet at the same time. You can see your teammate’s cursor, watch them apply filters, and build on their analysis without overwriting each other’s changes.
This real-time collaboration completely changes how teams work with data. A team can have a “data meeting” where everyone is in the same worksheet, exploring the data together. One person can filter for a specific region, while a teammate simultaneously zooms in on a particular product category, all in the same session. If someone spots an interesting insight, they can drop a comment directly on a chart or a table to flag it for the rest of the team. Because everything runs on the same live data from the warehouse, everyone sees the same numbers at the same time. This eliminates version conflicts and the constant, time-wasting question, “Which report is the latest?”
Ask Sigma: The Rise of AI and Natural Language
To further lower the barrier to entry, the platform includes a powerful set of AI features, headlined by a natural language query tool. This feature, known as “Ask,” allows you to query your data by typing a question in plain English. A user does not need to know how to drag and drop, build a pivot table, or even know which tables the data is in. They can simply type, “What were our top 5 best-selling products in the northeast last quarter?” The AI will parse this request, find the relevant data, execute the query, and return the answer, often as a fully-formed visualization.
This feature is a game-changer for casual business users who need a quick, specific answer but do not have time to build a report. The AI can also be used to summarize data. A user can point to a large table and prompt the AI to summarize the key takeaways in simple English. The system can also suggest related data and new analysis paths to explore, helping users uncover insights they might not have found on their own. This AI-driven approach makes data accessible to the entire spectrum of users, from the first-day intern to the seasoned analyst.
From Static Dashboards to Interactive Data Apps
The platform’s capabilities go beyond just static dashboards. It allows for the creation of interactive data applications, or “data apps.” These are guided, application-like experiences that allow non-technical users to perform “what if” scenario modeling. A developer can build a data app with interactive controls like sliders, buttons, and text input fields. These controls serve as parameters for the underlying queries. This transforms a read-only dashboard into an interactive tool for decision-making.
For example, a sales team could use a data app to model their commissions. They could adjust a “discount percentage” slider from 5% to 10% and instantly see the impact on their total revenue and potential commission. A finance team could model the impact of interest rate changes on a loan portfolio. This capability to build and embed interactive data apps allows the data team to provide powerful, customized tools to the business, all without the need for a full-scale software development project. It is the natural evolution of the dashboard, moving from a passive display of information to an active tool for exploration and decision-making.
Empowering the Business User
The common thread through all these features is the empowerment of the non-technical business user. The familiar spreadsheet interface, the easy drag-and-drop dashboarding, the real-time collaboration, and the natural language AI features all work in concert to achieve one goal: to remove the technical barriers between a user and the data they need. This empowerment has a cascading effect on the entire organization. It allows marketing teams to check their own campaign results without waiting. It allows sales teams to track their deals in real-time. It allows operations teams to see performance trends as they happen.
This self-service does not mean a loss of control. The data team still governs everything on the backend. But by providing a tool that is both powerful for analysts and accessible for beginners, the platform fosters a true data culture. It encourages curiosity and experimentation, allowing anyone on the team to explore data and share insights with very little setup. No one has to wait for a data request, and everyone gets the information they need to do their jobs more effectively, without slowing each other down.
Beyond the Spreadsheet: Custom SQL Integration
While the spreadsheet interface is the platform’s primary draw for business users, it is not a “lite” tool. It provides a robust, fully-featured environment for technical users and data professionals. The data team, including analysts and engineers, can drop into a custom SQL mode at any time. This allows them to write their own complex queries, using the full power of the data warehouse’s SQL dialect, and then visualize the results. This is a critical feature for “power users” who need to perform advanced calculations, complex cohort analyses, or data-shaping tasks that go beyond the capabilities of the visual interface.
This hybrid approach is a key differentiator. A user can start their analysis in the spreadsheet UI, then switch to the SQL mode to refine a query, and then switch back to the spreadsheet interface to analyze the results of their custom SQL. The platform can even translate a user’s visual worksheet into SQL, which serves as an excellent learning tool for those wanting to improve their SQL skills. This flexibility ensures that the platform is not a “walled garden.” It provides a low floor for beginners and a high ceiling for experts, allowing both technical and non-technical users to collaborate effectively within the same tool.
Centralized Governance and Security
For the data team, the single most compelling reason to adopt a warehouse-native platform is the centralization of governance and security. In the traditional BI world, governance is a nightmare. The data team has to set permissions in the database, and then they have to set another set of permissions in the BI tool. If a data extract is created, that file has its own security, or lack thereof. This creates a fragmented and high-risk environment where it is difficult to ensure that data is being accessed properly. This platform eliminates this problem entirely by inheriting the warehouse’s security model.
Because the tool connects directly to the warehouse and does not store any data, the data team only needs to manage permissions in one place: the data warehouse itself. They can set up roles and grant permissions on a database, schema, table, or even column level. The BI tool automatically respects these permissions. If a user’s warehouse role does not allow them to see a specific column, that column is not available to them in the spreadsheet. This dramatically simplifies security management, reduces the risk of data leakage, and makes it easy to maintain and audit compliance.
The Power of Embedded Analytics
This platform goes beyond just being a destination for dashboards. It is designed to be a platform for “embedded analytics,” allowing you to bring data and dashboards directly into the tools your team already uses. You can build interactive data apps or dashboards and embed them directly into your internal applications, like a company portal, a CRM, or a customer-facing website. This brings data to users within their natural workflow, eliminating the need for them to switch contexts by logging into a separate BI tool.
For example, a sales team can see a live dashboard of a customer’s purchase history and support tickets directly on the customer’s account page in their CRM. A logistics team can see a real-time map of their supply chain embedded in their operations portal. This embedded capability is managed through secure URLs or, for more custom solutions, a robust set of developer APIs. This feature allows the data team to provide data as a service to the rest of the organization, seamlessly integrating insights into the business process itself.
Integration and Extensibility: The REST API
A modern BI tool cannot be a closed system. It must be able to communicate with the other components of the modern data stack. The platform is built with this integration in mind. It connects natively to all the major cloud data warehouses and platforms, including Snowflake, Databricks, BigQuery, and Redshift. This allows you to use live data from any of these systems without issue. It can also pull in data from other systems, like a CRM or finance tool, allowing users to create a single, unified view without having to flip between multiple dashboards to see the whole picture.
For more advanced, custom solutions, the platform provides a comprehensive REST API. This API allows developers to programmatically manage the environment, automate repetitive reporting tasks, or build custom applications that leverage the platform’s query engine. For example, a developer could write a script that automatically generates a new report every time a new marketing campaign is launched. This level of integration and extensibility ensures that the platform can be adapted to fit the specific, custom needs of any organization, making it a flexible component of the data stack, not a rigid one.
How Sigma Complements the Modern Data Stack
The modern data stack is a set of composable, best-in-breed tools that handle different parts of the data lifecycle. You might have one tool for ingestion, another for warehousing, a third for transformation, and a fourth for business intelligence. This platform is designed to be the definitive BI and analytics layer for this stack. It does not try to replace other tools; it works with them. It sits on top of the warehouse, consuming the clean, modeled data that has been prepared by a transformation tool.
This clean separation of concerns is highly efficient. The data engineering team can focus on building robust, well-governed data models in the warehouse. The analytics team and business users can then use this BI tool to explore those models and build their own insights, all without having to worry about the underlying data plumbing. This partnership allows each team to specialize in what they do best. The data team provides clean, reliable, and governed data, and the business teams use their domain expertise to analyze that data and make decisions.
Managing Permissions and Row-Level Access
The centralized governance model simplifies one of the most complex challenges in BI: row-level access. In many organizations, different users should only be able to see the data that pertains to them. A sales manager for the West region should only see data for the West, not for the East. A store manager should only see inventory for their specific store. In traditional BI tools, implementing this requires complex, fragile logic within the BI tool itself. You have to create user filters and data-level rules for every single dashboard.
In the warehouse-native model, this is handled centrally and securely. The data team implements row-level access policies once in the data warehouse. The warehouse itself is responsible for filtering the data based on the user who is querying it. Because this platform passes the user’s identity to the warehouse with every query, the warehouse automatically applies these rules. The user in the West region runs a query, and the database itself filters the data to only return West region rows. The BI tool never even has access to the East region data. This is a far more secure, scalable, and maintainable way to manage data access.
The Data Team as Enabler, Not Gatekeeper
The cumulative effect of these features is a fundamental shift in the role of the data team. In the traditional model, the data team is a gatekeeper. They are a service department that fields an endless backlog of requests for reports and data extracts. This is frustrating for the data team, who are stuck doing low-level, repetitive tasks, and it is frustrating for the business, which has to wait for answers.
The warehouse-native model, with its self-service interface and centralized governance, transforms the data team into an enabler. Their job is no longer to build 100 different reports. Their job is to build a single, high-quality, governed data model in the warehouse. They provide the clean, reliable data, and then they “hand the keys” to the business teams, who can use the accessible spreadsheet interface to build their own reports and dashboards. This frees the data team to focus on more valuable and strategic projects, like advanced analytics, data science, and improving the data infrastructure, while the business gets the fast, real-time answers it needs.
A New Paradigm: Sigma vs. Traditional BI
The business intelligence market has been dominated for years by a few major players. Tools like Tableau, Power BI, and Looker became the titans of the industry, each with a different approach to data analytics. However, all of these tools were designed before the modern cloud data warehouse became the undisputed center of the data universe. This platform enters the market as a “cloud-native” challenger, taking a fundamentally different approach that is designed to capitalize on the architecture of the modern data stack. To understand its value, it is essential to compare it directly to these established platforms.
The core difference lies in their relationship with the data. The traditional titans often rely on data extracts, snapshots, and proprietary in-memory engines. This means they copy data out of the warehouse to make analysis fast. This new platform is built on a “warehouse-native” philosophy, meaning it leaves the data in the warehouse and pushes the compute to the data. This single architectural difference has profound implications for data freshness, cost, performance, and governance.
Live Data vs. Data Extracts and Snapshots
The most significant difference is the timeliness of the data. When you look at a dashboard in this new platform, you are querying the live data directly from your cloud warehouse. The dashboards are always up to date, reflecting the state of the business in real time. There is no need for data extracts or scheduled refreshes. This is a massive advantage for operational teams that need to make immediate decisions.
Tableau and Power BI, the two largest incumbents, built their success on high-performance, in-memory data engines. Their primary mode of operation involves creating “extracts,” which are compressed, proprietary snapshots of the data. While this makes the dashboards very fast once loaded, the data is inherently stale. It is only as fresh as the last scheduled refresh, which might be hours or even a day old. Looker can operate on live data, but its performance can be rigid if the data model is not perfectly optimized. For true, real-time exploration, the live-query model is superior.
The Cost Equation: Total Cost of Ownership
The warehouse-native model presents a compelling cost advantage for teams already invested in a cloud data warehouse. This platform runs on your existing warehouse compute, so you are only paying for the queries you use. This pay-as-you-go model is often significantly cheaper than the alternative. Traditional BI tools can require a massive, separate infrastructure. Power BI, for example, often requires additional Microsoft infrastructure and premium capacity to function at scale. Tableau users often have to pay for and manage powerful servers to run their data engine and handle large datasets.
This traditional model means you are paying for data storage and compute twice: once in your warehouse, and a second time in your BI tool. This platform eliminates that redundancy. You pay for your warehouse, and the BI tool is a lightweight analytics layer on top. This is especially cost-effective as you scale users. The pricing model often separates “creators” from “viewers,” allowing thousands of users to view dashboards for free, which further reduces the total cost of ownership as analytics are rolled out across the enterprise.
Speed and Exploration: Real-Time vs. Refresh Cycles
The user experience of data exploration is fundamentally different. With this new platform, a user can filter, pivot, and explore live data instantly, even on massive, billion-row datasets. Every action translates to a new, optimized query sent to the warehouse, and the results come back in seconds. This encourages a state of “flow” for an analyst, allowing them to follow their curiosity and ask ad-hoc questions without penalty.
Tools like Tableau and Power BI can feel sluggish and restrictive in comparison. If a user wants to explore a dataset, they must first create and wait for a large extract to be built. If they want to add a new column to their analysis, they may have to go back, modify the extract, and wait again. This “extract-refresh-explore” cycle breaks the flow of analysis and discourages ad-hoc exploration. Looker, while it queries the warehouse directly, can also feel rigid if a user wants to ask a question that was not anticipated in the pre-defined data model, forcing them to wait on a developer.
The User Experience: Democratization vs. Specialization
This platform’s greatest differentiator is its spreadsheet-like interface. It is designed for everyone. This familiar, intuitive UI makes it easy for non-technical users in marketing, sales, and finance to explore data and create their own dashboards without heavy training. This “democratization” of data access is its core mission.
Tableau and Power BI are incredibly powerful tools, but they are also complex. They require trained BI developers or highly skilled analysts to build sophisticated reports. The learning curve is steep. Looker, while powerful for data governance, is even more restrictive for non-technical users. Exploration is only possible within the rigid confines of the data model built by a developer using a proprietary language called LookML. This platform is the only one of the group that provides a path for a true, ungated self-service experience for all users, not just technical ones.
Comparing Sigma and Tableau
Tableau is the industry leader in data visualization. Its strength is its “pixel-perfect” design flexibility. A skilled Tableau developer can create stunning, highly customized, and artistic dashboards that are suitable for presentation to a board of directors. This platform can make clean, simple, and effective charts and dashboards, but it does not (and does not try to) compete with Tableau on pure visualization artistry. The trade-off is clear: Tableau offers unparalleled visualization flexibility, but at the cost of data staleness, infrastructure overhead, and a steep learning curve. This new platform prioritizes live data, ease of use, and governance over visualization customization.
Comparing Sigma and Power BI
Power BI is a powerful, enterprise-grade solution that is deeply integrated into the Microsoft ecosystem. Its primary advantage is this deep integration; it works seamlessly with other Microsoft products. However, like Tableau, it is primarily an extract-based tool that requires its own additional infrastructure and premium capacity to handle large datasets. It also has a steep learning curve and is a complex tool designed for BI professionals. This platform is a more modern, lightweight, and cloud-agnostic alternative. It is not tied to a single vendor’s ecosystem and is far easier for non-technical users to adopt, while often being more cost-effective in a non-Microsoft cloud environment.
Comparing Sigma and Looker
Looker is the most architecturally similar competitor. It also sits on top of the data warehouse and queries it directly. Its primary strength is its powerful, centralized data modeling layer, LookML. This modeling layer provides exceptionally strong data governance, as it ensures that everyone in the company is using the same definitions for key metrics. However, this strength is also its greatest weakness. The LookML layer is a massive bottleneck. A non-technical user cannot explore new data or ask a new question unless a developer first defines that data in the LookML model. This new platform solves this problem. It provides the same centralized governance by inheriting the warehouse’s permissions but removes the LookML-style bottleneck by providing a spreadsheet interface that allows any user to safely explore the governed data models directly.
The Visualization and Design Trade-Off
It is important to be clear about the trade-offs. If your organization’s primary need is to create highly customized, presentation-level, or artistic dashboards for external clients, a tool like Tableau or Power BI still leads the market in pure visualization options and flexibility. Their advanced design capabilities are extensive. This new platform’s visualization capabilities are clean, modern, and effective for the vast majority of internal business analytics, but they are not as flexible or as granular as the visualization-first tools. The platform makes a conscious trade-off: it sacrifices some of the “pixel-perfect” design flexibility in exchange for live data, ease of use, lower cost, and centralized governance.
Market Adoption and Future Positioning
While the traditional titans all have larger, established user bases and mature ecosystems, this new platform is gaining traction remarkably quickly. It is consistently recognized in industry analyses like the Gartner Magic Quadrant as a visionary and a leader in the new wave of cloud analytics. It is being adopted by a growing number of modern, cloud-first enterprises that are moving their entire data stack to the cloud. These companies recognize the architectural mismatch of the old tools and are choosing a platform that was built for the world they live in. This platform is strongly positioned as the modern, cloud-native alternative for teams that value speed, governance, and data democratization over all else.
Real-World Examples of Modern BI
The true test of any business intelligence platform is not its feature list, but the tangible business value it delivers. Companies across a wide range of industries are adopting this warehouse-native approach to make their data more accessible, more usable, and more impactful. These real-world examples demonstrate how moving to a live, self-service model can transform an organization’s relationship with its data, moving it from a slow, reactive process to a fast, proactive one that directly influences business outcomes. By empowering employees at all levels, this new model unlocks value that was previously trapped behind technical barriers.
The following examples are based on public case studies, anonymized to remove specific brand and company names, to illustrate the common patterns of success. These stories highlight how different types of businesses, from high-growth tech companies to established retail brands, are leveraging this new BI architecture to solve long-standing data challenges. They provide a practical look at what happens when an entire organization gains the ability to analyze live data for themselves.
Case Study: A Large Food Delivery Service
A large, well-known food delivery and logistics company faced a common data problem: massive data volume and a huge number of non-technical employees who needed data to do their jobs. Their data was growing exponentially in their cloud warehouse, but their existing analytics tools were slow and required specialist knowledge. The data team was overwhelmed with requests for simple reports from departments like operations, marketing, and finance. They needed a tool that could provide direct access to live data for thousands of users without overwhelming their data warehouse or their data team.
Since adopting a warehouse-native platform, this company has successfully given its employees across all departments direct access to live data. They no longer have to wait in a queue for an analyst to build a report. The platform was rolled out to thousands of users, who rebuilt over five thousand dashboards themselves. The non-technical teams are now able to answer their own questions, from tracking delivery times in real-time to analyzing marketing campaign effectiveness. A key result was that they managed to increase the total number of queries by about twenty-five to thirty percent, reflecting this surge in data engagement, without increasing their cloud warehouse compute costs, thanks to the platform’s efficient query engine.
Case Study: A Growing Fashion Brand
A growing fashion brand was struggling with a different data challenge: their data was siloed. They had in-store, point-of-sale data in one system and e-commerce, online data in another. This made it impossible to get a single, unified view of their customers. They could not easily identify their “best” customers—those who shopped both online and in-store—and as a result, their marketing efforts were inefficient. They needed a tool that could easily combine these different datasets and make them accessible to their marketing and merchandising teams.
Using a modern BI platform, this fashion brand was able to combine their online and in-store data in their cloud warehouse and use the platform’s spreadsheet interface to analyze this unified view. Their marketing team, now able to see the full customer journey, could target their highest-value, multi-channel customers more effectively. The business results were dramatic and direct. They reported a twenty-five percent higher return on investment from their email marketing, a twenty percent lower customer acquisition cost, and an eleven percent higher return on ad spend. This is a clear example of how direct access to unified data can translate directly into improved profitability.
Understanding the Pricing Model
The pricing model for this new generation of BI tools is also designed to be more modern and scalable. The pricing is simple and aligned with its goal of broad adoption. The platform typically charges based on the number of “creators”—the users who are building worksheets, running deep analysis, and creating dashboards. A key advantage is that “viewers,” or users who only need to look at and interact with existing dashboards, are often free and unlimited. This model is extremely cost-effective as you scale analytics across an organization. You are not penalized for having thousands of employees consume the data; you only pay for the smaller number of people who are actively building with it.
It also follows a pay-for-what-you-use model for compute. Because the platform uses your data warehouse’s compute, you only pay for the queries your team actually runs. This aligns perfectly with the pay-as-you-go model of the cloud. There is no need to pay for and maintain expensive, idle BI servers. Because the platform runs entirely in the cloud, there is also no need to manage hardware or install software upgrades. The system scales with your business without any of the extra overhead, making the total cost of ownership transparent and often much lower than traditional solutions.
What’s Next in Sigma?
This platform is not static; it is constantly evolving to help teams get more from their data. The company is actively working on new features to expand its capabilities. One major area of development is the analysis of unstructured data. Today, most analytics is focused on structured, tabular data. The future involves tools that can analyze unstructured data, like text documents, customer reviews, and images, allowing you to pull insights from more than just database tables. This would enable a user to analyze customer sentiment from support tickets alongside their sales data, all in one place.
Another area of focus is improving how the platform works with semantic layers. A semantic layer is a business-friendly map of the data that sits between the physical data warehouse and the end-user, ensuring that metrics like “revenue” or “active user” are defined consistently across the entire company. The platform is improving its integration with these layers, so data stays consistent, reliable, and easy to govern as an organization scales its data culture. This focus on enterprise-grade governance is critical for large, complex organizations.
AI and the Future of Analytics
Artificial intelligence will be a bigger part of the platform’s future, but with a unique focus on trust and transparency. Many BI tools are adding AI features, but they often function as a “black box,” giving you an answer without explaining how they got it. This makes it difficult for teams to trust the insights for critical decisions. The future of this platform’s AI, which builds on its “Ask” feature, is different. Every answer the AI provides will show exactly where the data came from and how the calculation was performed. The AI will literally “show its work.”
This transparent approach allows teams to trust the insights and make confident decisions. A user can ask a question in plain English, and the AI will not just give them a number; it will give them the answer along with the full, auditable path it took to get there. This makes the AI a trustworthy assistant, not an opaque oracle. This focus on trustworthy AI will help teams make better decisions without needing to wait for help from data experts, further accelerating the democratization of data.
Final Thoughts
This new wave of business intelligence, led by platforms like Sigma, represents a fundamental shift in how organizations work with data. By giving teams live, direct access to their cloud data through a simple and familiar spreadsheet interface, it removes the friction and complexity of traditional BI. It is a tool that is built for the new reality of the cloud data warehouse, prioritizing speed, collaboration, and centralized governance. It successfully bridges the gap between powerful data infrastructure and the non-technical users who need to make decisions.
This approach transforms the data team from a gatekeeper to an enabler, and it empowers business users to finally answer their own questions. It makes data a real-time, collaborative, and accessible resource for everyone, not just a select few. If your organization has invested in a modern cloud data warehouse, but your teams are still struggling with stale data, complex tools, and report request backlogs, this new, warehouse-native approach to BI may be the missing link.