The 2026 map for choosing data platforms

Data has become one of the most important assets in modern business. Every sales transaction, website visit, customer-service interaction, delivery order, manufacturing activity, and marketing campaign generates data. Yet having large volumes of data does not automatically mean that an organization understands its customers better, operates more efficiently, or makes faster decisions.

The gap between having data and creating value from data lies in an organization’s ability to govern, organize, analyze, and operationalize it. A company may have visually impressive dashboards, yet different departments may still use inconsistent definitions of revenue. It may also build an AI model that predicts customer churn, but if that model is not integrated into the CRM system, assigned to an operational team, and connected to a clear retention action, it may generate little or no business value.

The strategic question, therefore, is no longer simply which technology a company should buy. The more useful question is: How should the organization build a data ecosystem that moves data from its source to a decision, and from that decision to real-world action?

Gartner’s 2026 Magic Quadrants for Analytics and Business Intelligence Platforms and AI Platforms for Data Science and Machine Learning offer a useful perspective on two of the most important layers in that journey. One focuses on helping business users access, analyze, visualize, and act on data. The other focuses on building, deploying, monitoring, and governing AI and machine-learning models at enterprise scale.

This article explains how to interpret the two Magic Quadrants, the respective roles of BI and AI in a modern data architecture, the key vendor groups represented in the market, and the practical criteria organizations should use when selecting a solution.

How should organizations use the Magic Quadrant?

The Magic Quadrant is often referenced when organizations evaluate technology vendors. However, it should not be treated as an absolute ranking, nor should it replace technical due diligence, product testing, reference checks, or a full assessment of total cost of ownership.

Each vendor is positioned according to two dimensions:

Ability to execute: This vertical axis reflects a vendor’s capacity to turn its strategy into practical, operational capability. Relevant factors may include product maturity, scalability, implementation track record, customer support, market presence, partner ecosystem, and operational stability. A vendor may have an attractive strategic vision but still rank lower on this axis if it has not demonstrated sufficient scale, support quality, or execution consistency.
Completeness of vision: This horizontal axis reflects how well a vendor identifies and responds to current and emerging market needs. In the data technology space, this may include cloud strategy, open architecture, generative AI, AI governance, self-service analytics, semantic layers, embedded analytics, real-time data, and the ability to support multiple user personas through a coherent platform strategy.

Based on these two dimensions, vendors are placed into four groups:

Leaders: These vendors generally demonstrate both strong execution and a comprehensive market vision. They are often attractive to large enterprises and organizations pursuing broad data-transformation programs, because their platforms tend to be mature and able to support diverse user groups at scale. However, choosing a Leader does not automatically mean lower cost, simpler implementation, or better fit. More comprehensive platforms often require stronger architecture, governance, and operating capabilities from the customer as well.
Challengers: Vendors in this group typically demonstrate solid execution but may have a less expansive market vision or a narrower innovation profile than Leaders. They can still be strong choices for organizations with well-defined needs, a preference for stability, or an existing technology ecosystem that aligns closely with the vendor’s strengths.
Visionaries: These vendors often stand out through innovative approaches, differentiated user experiences, or forward-looking technology strategies. They may appeal to organizations that want to accelerate areas such as embedded analytics, cloud-native architecture, augmented analytics, or modern AI. At the same time, buyers should carefully assess product maturity, integration depth, customer support, and the vendor’s ability to support large-scale deployments.
Niche players: A Niche Player is not necessarily a weak vendor. Many vendors in this quadrant are highly effective in a specific industry, region, workload, or technical community. For example, a manufacturing organization may prioritize a platform with strong engineering simulation capabilities; a research institution may value deep R and Python support; and a mid-sized company may prefer a simpler, faster-to-deploy solution with a better fit for its budget.

For this reason, the Magic Quadrant should be treated as one input into a broader decision-making process. A platform positioned in the upper-right corner may be an excellent fit for a multinational enterprise, but unnecessarily complex for a mid-market business. Conversely, a Visionary or Niche Player may be the stronger choice if it addresses the organization’s priority use case more effectively.

A modern data ecosystem starts with BI and expands into AI

To create sustainable business value, organizations should not view BI, data science, and AI as separate technology projects. They are interconnected capabilities in a continuous chain: from raw data, to understanding the present, to forecasting the future, and ultimately to taking action in daily operations.

A modern data ecosystem typically includes at least four layers of capability:

Data infrastructure and governance: This is the foundation of the entire ecosystem. Organizations need to collect, integrate, store, standardize, and protect data from ERP systems, CRM platforms, accounting software, POS systems, websites, mobile applications, IoT devices, warehouse and logistics systems, advertising platforms, and external partners. More importantly, this layer must address data quality, access control, encryption, lineage, and sensitive-data management. If customer records are duplicated, product codes are inconsistent, or update timestamps are unreliable, every dashboard and AI model built on top of that data becomes less trustworthy.
Analytics and business intelligence: Once data is better organized, BI helps organizations understand what is happening. This layer produces dashboards, reports, alerts, trend analyses, and operational metrics. BI is often the most logical starting point because it serves a broad range of users: executives, department heads, sales teams, finance teams, marketers, operations managers, and HR professionals. A useful dashboard does more than show that revenue is declining; it helps users understand where the decline is occurring, why it may be happening, and who needs to take action.
Data science, machine learning, and AI: Once an organization has reliable data and visibility into operational trends, AI can answer more advanced questions. It can forecast demand, detect abnormal transactions, score risk, recommend products, optimize pricing, identify customers at risk of churn, or extract information from documents. In the era of generative AI, this layer also includes internal knowledge assistants, semantic search, retrieval-augmented generation, prompt management, and LLMOps.
Applications and operational workflows: This is the layer that determines whether data investments create measurable value. Dashboards, insights, and AI outputs need to appear in the systems employees use every day, such as CRM, ERP, planning software, dispatch tools, sales applications, and customer portals. A demand-forecasting model is useful only when it informs purchasing decisions, inventory allocation, or production planning. A risk alert matters only when it reaches the responsible person, triggers a clear workflow, and enables a timely response.

These four layers should not operate in isolation. When BI, AI, and operational systems are disconnected, organizations may have many reports and models but still make slow decisions or rely heavily on individual experience and intuition.

This structure also reflects the typical maturity path for many organizations. Most companies need to establish BI capabilities first, creating a shared language for data and performance measurement. They can then expand into AI and machine learning. AI can be powerful, but if the business cannot agree on what revenue means, how to calculate active customers, or which inventory data source is authoritative, advanced models will struggle to produce reliable outcomes.

Analytics and BI: where data becomes a common business language

Analytics and business intelligence are often narrowly understood as dashboarding or reporting tools. In reality, BI’s most important role is to help the organization develop a common view of business performance.

Consider a consumer-goods distribution company. The executive team wants to know whether revenue targets are being met. The sales director needs to identify regions at risk of missing quota. Finance wants visibility into gross margin and overdue receivables. The supply-chain team monitors inventory and on-time delivery. If every department extracts data from a different source and applies different definitions, the company may have numerous reports but no shared version of the truth.

Modern BI must address two needs at the same time:

Business users need faster, more independent access to data so they can explore issues and make decisions without waiting for every report to be produced by IT.
The organization must maintain governance so that critical KPIs are consistently defined, sensitive data is protected, and official reports can be validated.

This is why capabilities such as a semantic layer, data catalog, certified datasets, row-level security, and data lineage matter. They may not be as visually exciting as a polished dashboard, but they are fundamental to establishing trust in the numbers.

Figure 1. Magic Quadrant for Analytics and Business Intelligence Platforms, Gartner, May 2026.

According to the chart, the Leaders in the Analytics and BI Platforms market are Microsoft, Amazon Web Services, Salesforce (Tableau), Google, Qlik, and ThoughtSpot. These vendors represent different approaches, ranging from enterprise-wide BI adoption and cloud analytics to data visualization, associative analytics, and natural-language querying.

Microsoft: democratizing BI across the enterprise

Microsoft holds a strong position in the Leaders quadrant and is often a compelling option for organizations that already use Microsoft 365, Azure, Teams, Dynamics 365, or Power Platform. A major advantage of this approach is its ability to bring data insights into employees’ familiar working environments.

A company can create role-based BI views, for example:

Executive leadership can monitor revenue, gross margin, cash flow, receivables, inventory turnover, and plan attainment. Instead of waiting for month-end reports, leaders can detect early signs of declining sales in a region or margin pressure in a product category.
Sales leadership can monitor performance by region, representative, strategic account, and sales channel. If conversion rates fall, managers can analyze the issue by lead source, response time, customer segment, industry, or product type to identify the likely causes.
Operations and supply-chain teams can monitor inventory by warehouse, SKU, and inventory age, while also tracking on-time delivery, delayed orders, and stockout risks. These insights can support purchasing decisions, inter-warehouse transfers, and more responsive replenishment planning.
Finance teams can monitor the variance between plan and actuals, product-level profitability, overdue receivables, and cost performance. This ensures that commercial decisions are evaluated not only through revenue growth, but also through the quality and sustainability of profit.

The BI tool itself, however, is only one part of the solution. If the organization has no consistent definitions for revenue, margin, or customer status, it may create many dashboard versions without ever aligning on the numbers used in management discussions. BI implementation should therefore be accompanied by KPI standardization and clearly assigned data ownership.

Salesforce (Tableau): turning visualization into data exploration

Salesforce (Tableau) remains in the Leaders quadrant, reflecting its strength in data visualization and exploratory analytics. This can be particularly valuable for organizations that want analysts and business users to investigate questions more independently and flexibly.

Consider a hotel chain that sees a rising cancellation rate. A traditional report may simply show monthly cancellations. With strong exploratory analytics, users can investigate further:

Is the increase concentrated in direct bookings, online travel agencies, or travel partners?
Does it affect one city, one property, or the entire portfolio?
Which customer segments are cancelling more often: leisure travelers, business travelers, early bookers, or last-minute bookers?
Are certain pricing rules, cancellation policies, or promotional campaigns associated with the change?
Has the higher cancellation rate actually reduced realized revenue, or has it been offset by new bookings?

The value of BI in this situation is not the chart itself. It is the ability to turn an early warning signal into a structured sequence of questions that leads to action. If the company discovers that a highly flexible cancellation policy increases bookings but also drives last-minute cancellations, it may adjust policies by customer segment rather than applying the same rule to everyone.

AWS, Google, Qlik, and ThoughtSpot: four approaches to modern analytics

Amazon Web Services and Google are both positioned as Leaders, illustrating how analytics is increasingly connected to cloud data architecture. This approach is relevant for organizations that need to process large data volumes, work with rapidly changing datasets, or connect BI tightly to a broader cloud environment.

A food-delivery platform offers a clear example. Operations teams need to monitor order volumes by area, meal-preparation times, available drivers, late-delivery rates, cancellation rates, and customer satisfaction. When delivery times rise sharply in a particular district, managers need to understand whether the cause is a driver shortage, restaurant overload, traffic congestion, or a problem in the dispatching system.

If dashboards are updated quickly and connected to operational data, the company can respond while the issue is still occurring. This is an important shift from retrospective reporting to analytics that actively supports operational decision-making.

Qlik is often associated with an associative approach to analytics, which can be useful when the root cause of an issue is not found in a single data dimension. For example, when late deliveries increase, a company may need to analyze suppliers, warehouses, product categories, delivery routes, logistics partners, order times, and inventory availability at the same time. The ability to explore relationships across many dimensions can help users identify root causes more flexibly.

ThoughtSpot represents the trend toward search-driven analytics and natural-language data interaction. A sales executive may ask questions such as: “Which products have experienced the largest margin decline this quarter?” or “Which customers are most at risk of not renewing their contracts?” This kind of experience lowers technical barriers for business users.

However, organizations should not assume that natural-language interfaces can replace data governance. If terms such as “profit,” “active customer,” or “net revenue” are not standardized, the system may return answers quickly but based on inconsistent logic. An understandable answer is not necessarily a correct answer.

Other BI options worth considering

The Visionaries in the Analytics and BI Platforms chart include SAP, Oracle, Databricks, ServiceNow (Pyramid Analytics), IBM, SAS, GoodData.AI, Strategy, and Tellius. These vendors demonstrate that the BI market is evolving in multiple directions, from analytics embedded in enterprise applications to augmented analytics and the convergence of data, analytics, and AI.

SAP and Oracle are often worth considering for organizations that run core business systems within their respective ecosystems. In many cases, the benefits of integration with operational data, security models, financial processes, and enterprise workflows may outweigh the appeal of a standalone BI tool.
Databricks is increasingly relevant for organizations that want analytics to operate closer to their data and AI environments. This approach can suit companies with large-scale data architectures that want to reuse the same governed data across BI, data engineering, and machine-learning workloads.
ServiceNow (Pyramid Analytics) can be relevant for companies seeking to connect analytics with service management, IT operations, or workflow-driven internal processes.
GoodData.AI, Tellius, and Strategy reflect demand for embedded analytics, augmented analytics, governed enterprise reporting, and varied data experiences for business users.

The Niche Players include Zoho, Sigma, Incorta, Alibaba Cloud, and Domo. These platforms may be effective choices for mid-sized organizations, cloud-based spreadsheet-style analytics needs, companies already invested in a particular software ecosystem, or organizations that need to connect complex operational data sources quickly.

AI platforms: the next step from observation to prediction and optimization

BI helps organizations understand the current state of the business. AI platforms enable them to go further. Instead of merely seeing that revenue has declined, a business can forecast revenue for the coming weeks. Instead of only identifying customers who have already stopped buying, it can detect customers at risk of churn before they leave. Instead of reviewing equipment failures after they happen, a factory can predict likely failures and schedule maintenance proactively.

The core distinction is that AI does not simply display data. It uses historical data, patterns, rules, and algorithms to generate predictions, classifications, recommendations, or partial automation of decisions.

Building a model, however, is only the beginning. An enterprise AI platform must support the complete model lifecycle: data preparation, experimentation, training, validation, production deployment, monitoring, and governance.

Key capabilities typically include:

Model development and reproducibility: Data-science teams need to manage datasets, code, training parameters, and model versions. This enables the organization to answer critical questions: Which data was used to build the production model? Who approved it? Can the result be reproduced if it is challenged or audited?
MLOps and production deployment: A model that works in an experimental environment does not automatically work in a live system. Organizations need to package models as APIs, batch processes, or embedded application components. They also need testing, version control, rollback mechanisms, and automated deployment pipelines.
Drift and model-quality monitoring: Customer behavior, prices, market conditions, and business policies change continuously. A model trained on last year’s data may degrade significantly after only a few months. Organizations need to monitor predictive accuracy, shifts in input data, error rates, operating costs, and the appropriate time for retraining.
AI governance and generative AI controls: When AI influences material decisions or processes sensitive data, organizations need to control access, data sources, approval processes, model bias, explainability, and audit trails. For generative AI, they must additionally manage prompts, model outputs, data-leakage risks, knowledge sources, grounding, and citation mechanisms.

Figure 2. Magic Quadrant for AI Platforms for Data Science and Machine Learning, Gartner, June 2026.

The Leaders in the AI Platforms chart are Databricks, Google, Amazon Web Services, Microsoft, IBM, Dataiku, DataRobot, and Snowflake. Although they occupy the same quadrant, they serve different architectural priorities and operating models.

Databricks and Snowflake: bringing data, analytics, and AI together

Databricks holds a prominent position among the Leaders and is relevant to organizations seeking to connect data engineering, analytics, machine learning, and generative AI within a lakehouse architecture. This approach can be especially suitable for companies that manage large, diverse, and frequently changing datasets.

An e-commerce marketplace, for example, may use the same data foundation for several objectives:

BI teams monitor revenue, conversion rates, marketing performance, and inventory conditions.
Data-engineering teams process clickstream data, orders, prices, product information, and customer behavior.
Data-science teams build recommendation engines, demand-forecasting models, and fraud-detection models.
Software-development teams integrate recommendation results into websites and mobile applications.
Executive leaders monitor business outcomes such as higher revenue, lower stockout rates, or improved average order value.

Databricks is often most effective when the organization has strong technical capabilities, or is prepared to invest in data engineering and ML engineering as strategic functions. For a company that only needs a few dashboards or a single model, this approach may introduce more complexity than necessary.

Snowflake is also a significant Leader, reflecting the trend of bringing AI closer to the cloud data platform. When data is centralized, properly governed, and securely shareable across business units, organizations can reduce the time needed to prepare for analytics and AI initiatives.

For example, a consumer-goods group may consolidate sales data from distributors, retail stores, e-commerce marketplaces, loyalty programs, and field-sales teams. On that shared foundation, it can build sales-performance dashboards, forecast demand by region, and identify customer segments with a higher likelihood of repurchase.

Google, AWS, and Microsoft: cloud-native AI connected to business operations

Google, AWS, and Microsoft are all major Leaders in the AI platform market. Organizations should assess them through the lens of their existing ecosystems, internal technical skills, and the way AI outputs need to be operationalized.

Google may be a strong fit for organizations prioritizing cloud-native AI, large-scale data processing, and use cases such as document processing, text analytics, image recognition, and generative AI. For example, a bank can use AI to extract information from loan applications, compare data across multiple documents, and identify cases requiring deeper review.

AWS can be particularly relevant for companies already operating substantial workloads on AWS or those that need to connect AI to a broad, flexible cloud-services architecture. A logistics company may build a model that predicts delivery delays from GPS signals, traffic data, weather conditions, warehouse capacity, cargo type, and route history. The best outcome is not a static report; it is an alert integrated directly into the dispatch system so employees can reroute vehicles, reassign deliveries, or notify customers proactively.

Microsoft can be especially attractive to organizations already invested in Azure, Microsoft 365, Dynamics 365, and Power Platform. A practical example is an internal AI assistant for insurance or banking employees. The assistant can help staff search policies, summarize documentation, and draft customer responses. Yet it must be designed with strict access controls, approved source materials, and citations that allow employees to validate information before acting on it.

IBM, Dataiku, and DataRobot: enterprise AI requires governance and collaboration

IBM can be particularly relevant in complex enterprise environments and highly regulated industries. In credit-scoring use cases, for instance, a bank cannot focus only on model accuracy. It must understand why an application has been classified as high risk, which variables influenced the result, whether the model introduces unfair bias, and which model version was used for each decision.

Dataiku is relevant for organizations that need strong collaboration between technical teams and business-domain experts. This matters because many AI initiatives fail not because the algorithm is weak, but because the model does not properly reflect real operational knowledge.

In retail demand forecasting, a data scientist may have historical sales data, but commercial teams understand promotion schedules, supply-chain teams understand warehouse constraints, and finance teams understand working-capital pressure. A platform that supports collaborative workflows can help these groups build, validate, and use the model together.

DataRobot can be relevant for organizations that want to accelerate relatively standardized machine-learning use cases, such as revenue forecasting, fraud detection, customer churn prediction, or sales-lead scoring.

For example, a telecommunications company may use AI to identify subscribers at risk of leaving. The model may use service consumption, payment history, complaint frequency, plan type, call-center interactions, and responses to previous offers. Yet the business should not simply send blanket discounts to every high-risk customer. It should combine churn probability with customer lifetime value, offer cost, and expected response to determine the most profitable action.

Specialized options can still deliver substantial value

Beyond the Leaders, the AI platform market includes notable vendors such as SAS, Cloudera, Teradata, Siemens (Altair), Domino Data Lab, H2O.ai, Red Hat, Posit, and MathWorks.

These platforms may be appropriate in specific contexts:

SAS is often associated with quantitative analytics, risk management, and sectors such as banking, insurance, and government.
Cloudera and Red Hat can be relevant for organizations with hybrid-cloud architectures, strict infrastructure-control requirements, or constraints that prevent all data from moving to public cloud environments.
Siemens (Altair) and MathWorks may be particularly relevant for engineering, simulation, manufacturing, computer vision, signal processing, and control systems.
Domino Data Lab, H2O.ai, and Posit can serve data-science teams that need deep technical flexibility, AutoML acceleration, or strong R/Python-based development environments.

In manufacturing, for example, a predictive-maintenance program may combine vibration, temperature, pressure, rotational speed, maintenance history, and production-schedule data. The objective is not merely to predict that a machine may fail. It is to identify the optimal maintenance window: reducing downtime, preventing major failures, and avoiding unnecessary early replacement of components.

In this context, the best solution must integrate with industrial data sources, predictive models, and maintenance-management systems. A vendor’s Magic Quadrant position is only one consideration; fit with the actual production environment is what ultimately determines success.

BI and AI form a continuous value loop

BI and AI are not substitutes. They are two parts of a continuous improvement cycle.

A multi-channel retailer may begin with BI to monitor revenue by store, region, category, and sales channel. Dashboards can alert managers when stockout rates rise, a campaign underperforms, or margin declines in a specific category.

As data quality and maturity improve, the company can use AI to forecast demand by SKU and store, optimize promotions, recommend products to customers, and identify stockout risks early.

After AI is deployed, BI becomes essential again to measure AI’s real-world impact:

Has the stockout rate fallen?
Has excess inventory been reduced?
Have revenue and margin improved?
Does the model remain accurate after several months of operation?
Which stores or customer segments benefit most?
Are there unintended impacts that require adjustment?

This loop ensures that organizations do not evaluate AI by the number of models built. Instead, they evaluate it by actual effects on revenue, cost, risk, customer experience, and operational performance.

Six questions to answer before investing

Before selecting a BI or AI platform, organizations should answer six fundamental questions:

Which business problem should be prioritized?
Start with decisions that are slow, inaccurate, or overly dependent on intuition. Examples may include demand forecasting, inventory reduction, sales conversion improvement, receivables management, fraud detection, or churn reduction.
Is the data ready?
Data should be evaluated for completeness, accuracy, timeliness, consistency, and traceability. A forecasting model will not be reliable if transactions are duplicated, return data is incomplete, or product codes are inconsistent across systems.
Who will use the output?
An executive dashboard is different from a warehouse-manager dashboard. A platform for data scientists is different from a tool for sales representatives. The solution must suit the target users’ skills, working context, and decision responsibilities.
Which workflow will use the insight or model?
A prediction creates value only when a clear action follows. If a system identifies customers at risk of churn, which team will respond? What offers can they make? Where is the result stored? Who tracks the outcome?
How will governance and security be designed?
Organizations must control access rights, sensitive-data handling, data lineage, dashboard and model approvals, audit trails, generative-AI policies, and processes for handling unreliable AI outputs.
What is the total cost of ownership?
Cost is not limited to licensing. It includes compute, storage, data transfer, integration, consulting, training, operations, hiring, user expansion, and the risk of vendor dependency.

A practical roadmap for creating value from data and AI

Rather than trying to build a perfect enterprise platform from the start, organizations should progress in stages and demonstrate value continuously.

Start by building trustworthy data foundations: Inventory key data sources, identify critical datasets, standardize KPI definitions, assign data owners, and establish data-quality controls. This work may not be highly visible, but it determines the success of later BI and AI initiatives.
Deploy BI for high-impact decisions: Do not create hundreds of dashboards immediately. Prioritize a small number of high-value decision areas, such as revenue management, inventory, cash flow, sales effectiveness, and operational quality. Each dashboard should have an owner, a clear usage cadence, and an agreed action when a metric exceeds a defined threshold.
Select AI use cases with measurable ROI: Strong use cases usually have adequate historical data, frequent recurring decisions, and measurable outcomes. Demand forecasting, fraud detection, churn prediction, and inventory optimization are often more practical starting points than a general-purpose chatbot with no defined business value.
Integrate outputs into operational workflows: Forecasts should flow into planning systems, alerts should reach accountable employees, and recommendations should appear in CRM or sales applications. If users must export AI results to spreadsheets and process them manually, much of the potential value is lost.
Scale through reusable enterprise capabilities: Once multiple use cases exist, organizations should invest in data catalogs, semantic layers, MLOps, AI governance, certified datasets, and reusable components. This helps prevent every department from creating isolated dashboards, pipelines, or models that cannot be shared or governed effectively.

Conclusion

The 2026 data-technology market shows a clear convergence of BI, analytics, data science, machine learning, and generative AI. Organizations should no longer think in terms of selecting a single standalone tool. They need to design a connected set of capabilities that moves data from source to decision, and from decision to action.

Leaders in Analytics and BI Platforms — including Microsoft, AWS, Salesforce (Tableau), Google, Qlik, and ThoughtSpot — represent different ways of bringing data closer to business users. Meanwhile, Leaders in AI Platforms — including Databricks, Google, AWS, Microsoft, IBM, Dataiku, DataRobot, and Snowflake — represent different approaches to developing, deploying, and governing AI at enterprise scale.

The best platform is not necessarily the vendor positioned highest on a chart. It is the platform that best fits the organization’s existing architecture, team capabilities, data maturity, governance requirements, and — most importantly — the business decisions it needs to improve.

Competitive advantage does not come from owning more dashboards or building more AI models. It comes from building a decision-making system that is more trustworthy, faster, more consistent, and capable of learning continuously from data.

References

Gartner, Magic Quadrant for Analytics and Business Intelligence Platforms, June 2026.
https://www.gartner.com/en/documents/8062933
Gartner, Magic Quadrant for AI Platforms for Data Science and Machine Learning, June 2026.
https://www.gartner.com/en/documents/8001969

The 2026 Map for Choosing Data Platforms: From BI to AI