Displayr vs Power BI: Why Displayr Is the Smarter, Simpler Choice for Research and Insight Teams

Compare Power BI and Displayr to see why Displayr is the more intelligent choice for insight teams. Simplify survey and data analysis with our built-in statistical and AI-powered tools.

This article outlines the key differences between Power BI and Displayr, with a focus on users working with survey data.

• When "Business Intelligence" Isn't Enough
• Who It's Built For
• Modeless User Interface
• Handling Survey Data
• Language and Logic
• Data Analysis and Visualization
• Filtering and Interactivity
• Advanced Analysis and Modeling
• Reporting and Outputs
• Collaboration and Version Control
• Transparency and Documentation
• Open-End Text Analysis and Coding
• AI and Automation: Built for Research
• Summary

When "Business Intelligence" Isn't Enough

Power BI is a powerful, flexible analytics platform, widely used across industries for dashboards, KPI tracking, and financial reporting. It's a brilliant tool for structured business data, but for insight and survey analysis, it isn't designed for those workflows.

That's because Power BI was designed primarily for data engineers and BI developers, not for analysts who work with respondent-level data, multiple-response questions, or statistical testing. Its strength lies in data modeling and enterprise reporting, not in the advanced analytics or automation that insight teams need day-to-day.

Displayr was built to close that gap. It's an all-in-one analytics platform for insight professionals who need to move effortlessly from raw data to meaningful stories. It automatically recognizes the structure of survey data, offers built-in statistical and advanced analysis tools, and connects everything to dynamic, automated reporting - no coding or tool-switching required.

If you've ever found yourself buried in DAX formulas, juggling Power Query transformations, or manually updating PowerPoint decks, you'll immediately see the difference. Displayr keeps the entire workflow from data preparation, analysis, visualization, to reporting, in one intuitive, collaborative space.

Here's how Power BI and Displayr compare across the areas that matter most to modern insight teams.

Who It's Built For

Both Power BI and Displayr are modern, self-service analytics platforms designed to turn data into decisions. They each enable users to connect to data sources, visualize results, and share insights across teams, but they're built for different audiences with distinct goals.

Power BI is excellent for structured enterprise data with numbers stored in databases, data warehouses, and business systems. It easily integrates with a wide range of data sources and is optimized to track transactional and KPI metrics, including sales, revenue, and performance indicators.

However, it's not designed for the complexity of people-based or respondent-level data, where each row represents an individual's answers, opinions, or feedback. This type of data, often collected through surveys, feedback forms, or tracking studies, is typically categorical rather than purely numeric.

Categorical data describes types, groups, or labels instead of continuous values.
Each entry tells you what something is, not how much of it there is. Examples include:

• Demographics: gender, region, age group

• Attitudes or ratings: "Strongly agree", "Neither nor", "Strongly disagree"

• Brand or product choices: "Which of these have you used?" (where several may be selected)

Power BI can certainly visualize this kind of data, but it doesn't natively understand the structure or the statistical relationships behind it. Handling multi-response questions, weighting, or significance testing usually requires manual reshaping or external scripts.

Displayr, by contrast, is purpose-built for this type of work. It automatically recognizes and structures categorical and survey-style data, preserving variable labels and question types. It supports multiple-response questions, weighting, banners, and statistical testing - features that typically require workarounds in Power BI. With Displayr, analysts can move from raw, respondent-level data to advanced analyses such as regression, TURF, MaxDiff, or conjoint, and produce fully automated, dynamically updating reports. All in one environment and without writing code or switching tools. It also exports directly to PowerPoint, enabling teams to generate presentation-ready decks from the same automated reporting workflow.

Modeless User Interface

Displayr's interface is designed to keep your entire workflow in one place. Everything from data preparation and analysis to visualization and reporting happens on a single canvas. You can clean data, create variables, run analyses, and design slides or dashboards in a single, integrated environment. No more jumping between tools or windows. Your entire project lives in one document. This reduces context switching, shortens the learning curve, and keeps teams focused.

In contrast, Power BI separates your workflow across multiple environments and panes:

• Power Query: data prep and transformation (requires M code)

• Data Model: relationships and calculated columns

• Report View: building visuals

• DAX Editor: logic and measures

• Service Portal: sharing and collaboration

While this structure is flexible, it often requires switching between multiple tools and workflows, which can slow progress for non-technical users or on time-sensitive projects.

Displayr simplifies everything by keeping data, analysis, and reporting in a single, connected interface. The result? Faster onboarding, smoother collaboration, and insights delivered in a fraction of the time.

Handling Survey Data

Survey data rarely comes "clean." It needs recoding, stacking, merging, weighting, and labeling - tasks that can be time-consuming and error-prone in traditional BI tools.

Displayr is purpose-built for this kind of data. It automatically detects and structures survey question types, including single-response, multiple-response, grid, and hierarchical, without additional configuration. You can stack, merge, recode, weight, and transform data directly in the platform using intuitive, point-and-click tools.

With Displayr, survey data doesn't need to be reshaped to fit the platform. The platform is already shaped to fit the data. This makes the transition from raw data to analysis dramatically faster and frees researchers to focus on what really matters: delivering insight.

Power BI, by contrast, expects clean and tidy data and relies on Power Query or external scripts to reshape and prepare survey files. While possible, these workarounds add technical complexity and slow down the workflow.

Language and Logic

Power BI relies heavily on DAX (Data Analysis Expressions), a powerful but notoriously complex formula language. It demands a deep understanding of context transitions, filter propagation, and nested functions, concepts that make sense to engineers, not researchers. Writing and debugging DAX can be slow and error-prone, creating a steep learning curve for non-technical users.

Displayr replaces DAX with clarity and simplicity. You can create new variables, filters, and visualizations using point-and-click menus, and have the ability to see the results as you go. For advanced users, Displayr supports custom R and JavaScript calculations and includes an AI R Code Writer that intelligently generates R code within the editor, referencing items in your document and adapting suggestions in real-time.

Terminology also reflects the difference in philosophy:

• Measures in Power BI ≈ Custom calculations in Displayr

• Slicers (for filters) ≈ Combo Box or List Box Filters

• Report canvas in Displayr replaces Power BI's multi-pane interface

Together, these differences highlight Displayr's core design principle: it's built for researchers and insight professionals, not programmers, removing technical overhead while retaining full analytical power.

Data Analysis and Visualization

Tables and charts are where insights come to life, but how they're created and managed differs significantly between Power BI and Displayr.

In Power BI, building tables requires manual setup using drag-and-drop, with measures and dimensions defined via DAX. While flexible, this process can be time-consuming, especially for survey data such as multiple-response questions or banner tables, which aren't natively supported. Deeper analytical tasks, such as significance testing, regression, or weighting, require external tools or manual setup (e.g., R or Python), and visuals often require heavy customization to align with research conventions.

Displayr, by contrast, was built specifically for market research. It automatically understands survey data structures and builds tables accordingly, handling multiple-response questions, NETs, and banners out of the box. You can drag variables onto a blank page to instantly create tables or charts, apply weights and filters through the object inspector, and run statistical tests with a single click. Visuals are optimized for research reporting, with automatic color-coded formatting for significance testing, and editable export to PowerPoint. For advanced users, custom visualizations can be built directly in R.

Here's how Displayr and Power BI compare when it comes to analysis, visualization, and reporting:

Displayr combines statistical testing, advanced modeling, and reporting in one environment - no external tools required. Power BI, by contrast, depends on complex integrations or scripting to achieve the same depth of analysis.

Filtering and Interactivity

Filtering and interactivity are key to tailoring insights for different audiences, and here again, Displayr and Power BI take very different approaches.

Power BI supports filters at the visual, page, and report levels, managed through slicers, filter panes, and bookmarks. While powerful, this structure can quickly become opaque. Filters may stack unexpectedly or apply in the background, making it difficult to see what's influencing a particular visualization without digging through multiple panes or layers of logic.

Displayr, by contrast, is designed for clarity and survey-aware filtering. Filters are always visible and editable via the object inspector, making them easy to understand, audit, and adjust. You can apply filters to any output directly from the object inspector: select the output and click Data > Filters & Weight > Filter(s). Each filter is saved as a variable, which can later be edited in the Data Sources tree using the Edit filter button.

Adding interactivity is just as simple. Displayr includes purpose-built controls, Combo Box Filters, List Box Filters, and Text Box Filters, that can be inserted in seconds and linked directly to visualizations or entire pages. There's no need for bookmarks, slicers, or DAX logic, and filters automatically respect survey-specific structures such as banners, question types, and weighting.

Here's how the two tools compare at a glance:

Displayr delivers simpler, survey-aware filtering and interactivity that anyone can set up. No DAX, no bookmarks, no guesswork. Power BI's filter system is flexible but fragmented, requiring more technical knowledge to manage effectively.

Advanced Analysis and Modeling

Analysis is where research moves beyond reporting into true insight, and Power BI and Displayr take very different approaches.

Power BI is built for business reporting, not statistical analysis. While technically powerful, it relies on external tools like R, Python, or Azure ML to perform even basic modeling, let alone advanced techniques such as segmentation or regression. This creates fragmented workflows and often requires support from data scientists or technical specialists.

Displayr, by contrast, includes a full suite of research-focused analysis tools as standard. TURF, driver analysis, MaxDiff, segmentation, regression, conjoint analysis, cluster analysis, and other advanced methods are built in and accessible through intuitive menus. Designed for researchers rather than programmers, these tools enable sophisticated modeling to be fast, transparent, and easy to reproduce, without requiring data exports or code.

Statistical testing is also built in, with significance tests, confidence intervals, correlations, and diagnostics available by default. Researchers can apply these directly within the platform, ensuring findings are robust and grounded in sound statistical practice.

Here's how Displayr and Power BI compare at a glance:

Displayr provides researchers with end-to-end analysis and modeling capabilities in one environment, from statistical testing to advanced segmentation and conjoint analysis. In contrast, Power BI relies on external tools and coding expertise to achieve similar outcomes.

Reporting and Outputs

When it comes to turning analysis into deliverables, Power BI and Displayr offer very different models.

Power BI is built for dashboards and operational reporting, optimized for tracking KPIs and real-time business metrics. While useful for continuous monitoring, it lacks flexibility for storytelling or research-style outputs. PowerPoint export is static, essentially screenshots, and automated reporting via paginated reports requires complex setup and technical knowledge.

Displayr is both a dashboarding and reporting solution, designed specifically for researchers who need to turn data into insight-rich stories. It offers a slide-based report canvas with live charts, tables, and commentary - all linked to the underlying data. You can share outputs as interactive dashboards, web reports, PowerPoint decks, PDFs, or Excel files, and when data updates, everything updates automatically.

Displayr's automation features make it easy to reuse templates, refresh data, and regenerate entire presentations or dashboards with a single click. No repetitive manual work or technical configuration required.

Displayr is an end-to-end platform for dashboarding and reporting that combines automation, live data connections, and presentation-ready outputs. Power BI excels at KPI dashboards, but Displayr goes further, making it easy to deliver interactive dashboards and client-ready stories from a single document.

Collaboration and Version Control

Power BI collaboration centers on the Power BI Service, where teams can view and comment online; however, true co-editing isn't native. Editing typically involves sharing .pbix files or managing versions through Power BI workspaces and (in more advanced setups) Git-based workflows, which can lead to conflicts and manual cleanup. Power BI allows you to view and restore previous versions directly from the file, and for files managed through a version control system like Git, you can also revert to earlier commits as needed.

Displayr, by contrast, is cloud-native and built for real-time teamwork. Multiple users can work on the same document simultaneously and see each other's updates in real-time. Displayr auto-saves a new version of your document every 10 minutes during editing, as well as every time you publish or republish the document. Each version is kept for 24 hours, then gradually thinned over 48 weeks, at which point only one version per 4-week period is retained.

If you need to roll back, Displayr makes it easy to revert your entire document to a previous version, which is handy if you've made a mistake when updating or combining datasets, or if you need to undo a large number of changes at once. You can also download a copy of any older version for safekeeping or offline review. A built-in dependency graph makes relationships between data, calculations, and visuals precise, helping teams collaborate transparently and confidently.

Displayr provides true multi-user collaboration, automatic versioning, and simple rollback or download options, ensuring teams can work together without losing progress. Power BI supports version restoration and Git integration but lacks real-time co-editing, which slows collaboration and makes it more file-dependent.

Transparency and Documentation

Understanding how results are calculated and where they come from is essential for building trust and avoiding errors, especially in research workflows that involve multiple contributors or iterative updates.

Power BI allows users to document calculations using DAX comments or manually added notes, but this is optional and rarely standardized across projects. Because logic is often buried inside DAX formulas or Power Query scripts, auditing typically requires manually inspecting formulas or reviewing steps in the Model and Data views. While these tools help visualize relationships, tracing end-to-end dependencies, mainly when outputs rely on multiple measures, can still be complex and time-consuming.

Displayr, by contrast, makes transparency visual and collaborative. Every variable, calculation, and filter can be labeled, documented, and audited directly within the platform. The interactive dependency graph provides a visual map of how data flows from inputs to outputs, making it easy to trace the source of every number and how it was derived.

Adding comments to your document is also straightforward and highly useful when collaborating with others or leaving notes for yourself. Displayr's commenting feature allows anyone with edit access to add comments to anything in the Report or Data Sources trees, including pages, outputs such as tables, charts, or text boxes, data sources, and even variables or variable sets.

Displayr combines visual transparency, inline documentation, and collaborative commenting, enabling teams to clearly understand and validate how each result was created. Power BI provides partial lineage visibility and relies heavily on manual inspection of DAX and Power Query logic, making complete transparency at scale more challenging to maintain.

Open-End Text Analysis and Coding

Text data is often the richest and most underutilized source of insight in research. It captures nuance, emotion, and customer voice in a way numbers can't. But analyzing open-ended responses is notoriously time-consuming and challenging, especially without the right tools.

Power BI does not include native tools for analyzing unstructured text. Users must rely on external processes, such as preparing coded responses in Excel or integrating with Azure Cognitive Services or third-party tools for sentiment analysis and keyword extraction. This adds complexity, disrupts workflow continuity, and often requires technical support or scripting.

Displayr, by contrast, includes a complete open-ended text analysis suite built directly into the platform. Researchers can manually or semi-automatically tag responses, group them into themes, and use AI-assisted categorization or sentiment analysis, all without leaving their document. Coded responses are dynamically linked to tables, charts, and dashboards, so changes are automatically updated everywhere.

Whether you're manually coding themes, using assisted tagging, or applying sentiment scoring, Displayr makes open-ended analysis fast, accurate, and repeatable. No switching tools, exporting files, or writing code is required.

With Displayr, researchers can code open-ended responses using intuitive interfaces, apply themes, perform sentiment analysis, and update visualizations instantly, without third-party tools or custom scripts. Power BI users must rely on external tools, scripts, or manual processes, which slows the workflow and increases errors.

AI and Automation: Built for Research

Modern research workflows increasingly rely on AI to accelerate manual tasks, improve quality, and enhance decision-making. Both Displayr and Power BI include AI capabilities, but their focus and usefulness differ dramatically for researchers.

Power BI offers general-purpose AI tools, including pattern detection, natural language Q&A, and integrations with Azure Cognitive Services. These features are designed primarily for structured, numeric business data, not for the complexity of survey data. While powerful, they often require configuration or external connections and don't support survey metadata or question structures.

Displayr, on the other hand, embeds AI directly into the research workflow. Its AI tools are purpose-built to understand survey data, metadata, and question types, making analysis faster, cleaner, and more intuitive.

Displayr's AI Suite includes:

• Research Agent – acts like an expert research assistant: asks questions about your data, gets contextual summaries, and receives visualization or analysis suggestions tailored to the survey structure.

• Data Preparation Agent – automatically scans data for inconsistencies, missing labels, or merge opportunities, and suggests fixes.

• Text Categorization – accelerates open-ended coding through AI-assisted tagging and theme discovery. When using Displayr for text categorization, you can significantly enhance the quality and relevance of your results by incorporating AI-based custom prompts that account for domain-specific language, tone, or brand nuance.

• AI R Code Writer – generates dynamic R code directly within your project, referencing items and adapting suggestions in real time.

Displayr's AI is research-aware and built to work with real-world survey data - categorical, weighted, looped, or messy - so researchers can automate data cleaning, text analysis, and insight generation without leaving the platform.

Displayr's AI is purpose-built for researchers. It understands survey data, automates cleaning and coding, and even generates analysis, commentary, or new variables directly within your project. Power BI's AI is powerful but general-purpose, designed for business dashboards rather than research workflows.

Summary

Displayr is an all-in-one analytics and reporting platform for market research and insight teams, with built-in support for multiple-response questions, weighting, and significance testing.

Power BI, on the other hand, is a general-purpose business intelligence tool optimized for structured, transactional data and enterprise dashboards. While it excels at operational reporting, it lacks native tools for statistical analysis, survey structures, and research-specific automation.

Why insight teams choose Displayr:

• No coding needed: Perform statistical and advanced analyses, text categorization, and automation without DAX, Power Query, or scripting.

• Survey-aware from the start: Handles multiple-response, weighting, banners, and categorical hierarchies natively.

• All-in-one workflow: Prepare, analyze, visualize, and report within a single cloud-based environment.

• AI-powered speed: Features like Research Agent, Data Prep Agent, and AI text analysis save hours of manual work.

• Live reporting: Refresh dashboards and PowerPoint slides instantly as new data comes in.

• Seamless collaboration: Multiple users can collaborate in real time, with full version history and dependency tracking.

If your work involves survey analysis, customer feedback, or people-based data, switching from Power BI to Displayr isn't just an upgrade - it's a significant leap in speed, accuracy, and insight delivery.

For insight and analytics teams, Displayr offers a faster, more innovative, and more transparent way to turn data into decisions.

Ready to make the switch? If you're currently using Power BI but working with survey data, there's never been a better time to move to Displayr. Whether you're in a trial or just exploring, our team is here to help you migrate your workflows and unlock better, faster insights. Why not start your free Displayr trial and simplify your analysis? Automate your reporting. Unlock better, faster insights today.

Next

Get Started Using AI in Displayr

Opting In and Out of Displayr AI

Using Customizable Displayr AI Features

Getting Started with AI Prompting in Displayr

Optimizing Text Categorization With Custom Prompts

Reporting and Outputs