best analytics – A new G2-led evaluation of analytics platforms for 2026 spotlights a clear divide: some tools win by lowering the barrier to reporting, while others win by tightening governance, scaling data workloads, or consolidating an entire analytics stack. Microsoft Pow
For many teams, analytics failures don’t start with missing data. They start after the dashboards are built—when people can’t agree on the numbers, can’t explain what’s driving results, or can’t move fast enough to act.
That tension sits underneath a G2-led evaluation of six analytics platforms for 2026: Microsoft Power BI. Tableau. SAS Viya. Databricks. Looker. and Domo. The focus wasn’t on who could cram in the most features. The goal was to identify which platforms actually help teams connect the dots. uncover meaningful trends. and turn data into decisions.
The platforms were selected from G2’s Summer 2026 Grid Report for analytics platforms. with the analysis drawing on G2 Score. customer satisfaction ratings. market presence. review volume. and review recency. The screenshots referenced in the evaluation come from G2 vendor listings and publicly available product materials.
Each pick comes with a slightly different promise—one that can feel comforting if your team’s problems match the tool’s design, and frustrating if they don’t.
Microsoft Power BI: affordable visualization that fits Microsoft workflows
Microsoft Power BI was positioned as the best option for affordable data visualization and integration with Microsoft 365. The platform combines interactive dashboards, reporting, and analytics with deep integrations across the Microsoft ecosystem. Pricing in the evaluation listed Microsoft Power BI at $14 per user, per month.
The review emphasized that Power BI Desktop is approachable—particularly for users familiar with Excel. Creating a basic dashboard was described as relatively straightforward. and the platform offers a broad range of reporting and visualization options from the start. including custom visuals from the AppSource marketplace.
Power BI’s flexibility in formatting, themes, tooltips, and interactivity was also highlighted, alongside collaboration features such as commenting and tagging.
The evaluation cited recurring praise from G2 feedback around Power BI’s ability to transform raw data into interactive reports and dashboards. including use cases like real-time sales tracking. ETL workflows. and more complex business intelligence initiatives. Industries mentioned in the evaluation included marketing, consulting, financial services, IT, and operations.
But the tradeoffs showed up quickly in the same feedback. Several reviewers mentioned a learning curve around DAX—Power BI’s formula language—especially for users without experience in SQL. data modeling. or business intelligence tools. Others said the interface can feel crowded with large datasets or highly customized reports. and that performance can be affected when data models are not optimized.
On G2, Power BI was reported as holding a 4.5/5 rating, with 96% of reviewers rating it four stars or higher. The evaluation noted that Power BI Desktop is free to download for Windows users. while users need a Power BI Pro license to publish. collaborate on. or share reports in the cloud. available through Microsoft Fabric and select Microsoft 365 and Office 365 plans. It also listed free Power BI trials through Microsoft Fabric.
Tableau: visualization-first storytelling with a learning curve
Tableau earned its spot as the best option for advanced data visualization and interactive dashboards. The evaluation described Tableau as a visualization-focused platform known for highly customizable dashboards. data storytelling. and exploratory analysis. with an enterprise presence strengthened by its integration into the Salesforce ecosystem. Pricing was noted as available upon request from the vendor.
In the assessment, Tableau’s strengths centered on dashboarding and visual analytics capabilities, along with straightforward data connections to common sources such as Excel, Google Sheets, SQL databases, and Snowflake.
A particular emphasis was placed on Tableau Prep, its data preparation tool. The evaluation described Tableau Prep as providing a more visual approach to data cleaning and transformation, without relying heavily on SQL or scripts.
The reporting cited G2 feedback where data visualization, dashboards, and charting were among Tableau’s highest-rated features, with satisfaction scores exceeding 93%. Tableau’s industries of strength were described as including IT, finance, higher education, and marketing.
Still. the evaluation flagged a consistent challenge: building more advanced dashboards can become difficult. particularly when blending data from multiple sources or creating complex calculations. Pricing was another repeated theme. with some smaller organizations feeling Tableau’s licensing costs were higher than competing options. even as other reviewers said its visualization capabilities justified the spend.
Tableau was reported as holding a 4.4-star rating on G2, with 94% of reviewers rating it four stars or higher. The evaluation described Tableau as available as a desktop application for Windows and macOS. through Tableau Cloud for online collaboration. and as an on-premises deployment. with a free trial available.
SAS Viya: governed analytics and AI for regulated environments
SAS Viya was placed at the top as the best option for governed analytics and AI in regulated industries. The evaluation described SAS Viya as a cloud-native analytics and AI platform that brings data preparation. modeling. governance. and deployment together in one environment. Pricing was noted as available upon request from the vendor.
The assessment emphasized SAS Viya’s end-to-end approach—data preparation, modeling, reporting, and deployment within a single environment—so teams do not have to switch constantly between separate tools. It also highlighted support for SAS, Python, R, and SQL workflows.
Users, according to the evaluation, repeatedly mentioned SAS Viya’s performance at scale, including faster processing times for computationally intensive analyses and large data volumes. The review tied this to SAS Viya’s distributed, in-memory architecture.
Governance and explainability were positioned as key strengths, with reviewers in banking, healthcare, insurance, and other regulated industries highlighting capabilities such as model auditability, lineage tracking, monitoring, and compliance support.
The evaluation also cited data visualization performance: 89% satisfaction for data visualization and 89% satisfaction for data filtering. It added that AI-assisted visualizations were mentioned as helping connect data exploration with decision-making.
Still. the learning curve was described as one of the most commonly mentioned challenges. especially for users transitioning from older SAS environments or navigating multiple Viya applications for the first time. Pricing came up again as a difficulty for some users who compared it to open-source alternatives. though enterprise reviewers argued the broader functionality and governance justified the investment.
SAS Viya was reported as holding a 4.3-star rating on G2, with 94% of users rating it four or five stars. The evaluation described SAS Viya as available across public cloud, private cloud, and hybrid environments, and said SAS offers demos and consultations.
Databricks: consolidation for unified engineering. analytics. and AI
Databricks was selected as the best platform for unified data engineering. analytics. and AI workloads. The evaluation described Databricks as a unified data and AI platform built around the Lakehouse architecture. combining the flexibility of data lakes with the performance and governance capabilities of data warehouses.
The review said Databricks was created by the original developers behind Apache Spark, Delta Lake, and MLflow. It emphasized consolidation as the core theme in user feedback—teams replacing fragmented stacks built from separate ingestion, transformation, governance, and analytics tools.
Delta Lake. Workflows. and Unity Catalog also came up as areas where reviewers described moving data management. orchestration. and governance into the same environment rather than maintaining multiple systems. The evaluation said reducing the number of tools can lower maintenance overhead and simplify day-to-day operations for data teams.
Scale was another repeated point: large datasets. streaming workloads. and machine learning projects were described as being supported by Spark-powered processing and managed infrastructure. Collaboration and productivity features were tied to Databricks’ notebook environment. described as a shared workspace for analysts. engineers. and data scientists.
The evaluation also cited integrations and cloud support. stating Databricks connects with cloud storage platforms. BI tools. orchestration frameworks. machine learning services. and external data sources. It listed support for AWS, Azure, and Google Cloud, as well as open formats like Delta Lake and Apache Iceberg.
For AI and machine learning, the review pointed to MLflow for experiment tracking and model management and highlighted the Genie AI assistant as a productivity tool.
The tradeoff, according to the evaluation, is complexity. Reviews mentioned learning curve issues related to cluster configuration, Spark optimization, permissions management, and platform administration. Cost management also appeared as a recurring concern. with compute usage. storage costs. and DBU consumption described as requiring ongoing monitoring. The evaluation argued that consolidation could still improve efficiency and reduce costs elsewhere in the analytics stack.
Databricks was reported as holding a 4.6-star rating out of 5 on G2, with 94% of users rating it four or five stars. The evaluation described Databricks as available as a cloud-native platform across AWS, Microsoft Azure, and Google Cloud, with a free trial.
Looker: semantic modeling that keeps metrics consistent
Looker was described as the best option for scalable BI with centralized data modeling and governance. In the evaluation. Looker was positioned differently from many dashboard-first tools: it emphasizes data modeling. governance. and consistency across the organization.
The assessment described Looker as model-driven and developer-oriented, built around SQL-based analytics workflows. After connecting to a data source, users define metrics, relationships, and business logic using LookML, Looker’s proprietary modeling language.
The evaluation highlighted the semantic layer as a shared source of truth—helping teams work from consistent definitions for metrics such as customer churn or lifetime value. It also cited G2 feedback praising Looker’s semantic layer and the control LookML provides over how data is queried. governed. and reused.
Integrations were described as including cloud data warehouses such as BigQuery. Snowflake. and Redshift. aligning with Looker’s Google Cloud roots. Looker Blocks—its library of prebuilt code. dashboards. and data models—was also named as a way to reduce setup time for common use cases involving marketing reporting. Google Analytics. Salesforce. or HubSpot data.
The evaluation also referenced embedded analytics and said governance and dashboard consistency improve after data models are established.
The downside was a learning curve. The review said Looker requires a different mindset than traditional dashboarding tools. especially when working with LookML or building custom data models. Performance concerns were also mentioned occasionally, including slower loading times with large datasets or complex reporting environments.
Looker was reported as holding a 4.4 out of 5 rating on G2, with 95% of reviewers rating it four stars or higher. The evaluation positioned Looker as especially valuable for governed metrics. semantic modeling. and cloud-scale analytics—particularly for teams already invested in Google Cloud or modern data warehouse architectures.
Domo: self-service analytics that aims for adoption. not gatekeeping
Domo was selected as the best for self-service analytics and business user adoption. The evaluation described Domo as a cloud-native analytics platform combining data integration. transformation. visualization. and application development in a single environment. with more than 1. 000 prebuilt connectors.
Ease of use was repeatedly emphasized in the evaluation as the standout reason organizations chose Domo. Several G2 reviews, as summarized in the evaluation, described how quickly non-technical teams could navigate dashboards, filter data, and answer their own questions without extensive training.
Magic ETL was highlighted as a feature that gave users more control over data preparation without relying heavily on SQL or engineering support. The evaluation said reviewers described using Magic ETL and Beast Mode calculations to build transformations, metrics, and reporting logic independently.
Connector ecosystem strength was another key point: users described bringing together data from CRM platforms, marketing tools, financial systems, websites, and cloud applications. The evaluation said this was often used as a central hub across business functions.
Dashboarding performance was described with specific satisfaction figures: graphs earned a 90% satisfaction rating, while dashboards and data visualization both received 89%. The evaluation also stressed that dashboards update from live data sources. aiming to move teams away from manually compiled reports and toward real-time visibility.
Domo’s approach to balancing self-service with governance was described as a strength, with centralized teams managing trusted datasets while business users build and customize their own reporting experiences.
App Studio was also presented as a way to extend beyond traditional BI, allowing teams to create custom applications, branded experiences, and specialized dashboards when standard reporting isn’t enough.
The evaluation listed limitations too. It said visualization flexibility can feel more limited than platforms focused heavily on visualization, though App Studio and custom development were referenced as ways to extend capabilities.
Pricing and consumption model were another major recurring discussion point. The evaluation said Domo uses a credit-based consumption model. and reviewers mentioned that monitoring usage and forecasting costs can take time—though long-term users said costs become easier to manage once reporting workflows and usage patterns are established.
Domo was reported as holding a 4.3 out of 5 rating on G2, with 94% of users rating it four or five stars. The evaluation described Domo as a cloud-native platform with web and mobile access for reporting and dashboard consumption, and said the company offers personalized demos and trial options.
Underneath the six product scores is a consistent lesson: the “best” analytics platform depends on the friction you’re trying to remove—whether that’s report-building effort, data governance, scaling to heavy workloads, metric consistency, or the simple question of who gets to ask the next question.
The evaluation’s final takeaway was that analytics is no longer only about analysts. The strongest platforms, as described, help more people across the business ask questions, explore data, and make decisions without waiting for someone else to pull the numbers.
It framed the picks as matching different organizational realities: Databricks for complex data ecosystems. Looker for governed metrics and consistency. Tableau for communicating insights visually. Domo for self-service adoption. Power BI for balancing flexibility with accessibility. and SAS Viya for environments where governance and compliance cannot be an afterthought.
analytics platforms 2026 G2 Summer 2026 Grid Report Microsoft Power BI Tableau SAS Viya Databricks Looker Domo business intelligence data visualization self-service analytics data governance semantic modeling Lakehouse architecture