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<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">Domo vs Power BI Copilot vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">Domo</div>
<div class="score-card__score">62/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--competitor">
<div class="score-card__name">Power BI Copilot</div>
<div class="score-card__score">62/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--scoop">
<div class="score-card__name">Scoop Analytics</div>
<div class="score-card__score">82/100</div>
<div class="score-card__label">BUA Score</div>
<div class="score-card__badge">WINNER ✓</div>
</div>
</div>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">Executive Summary</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">TL;DR Verdict</h3>
<p class="content-section__paragraph">Scoop (82/100 BUA) enables true business autonomy through multi-pass investigation, while Domo (62/100) and Power BI Copilot (32/100) trap users in dashboards. Both competitors require IT support for anything beyond pre-built views, creating bottlenecks Scoop eliminates entirely. Choose Scoop for immediate independence, competitors only if locked into their ecosystems.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard tool. Ask questions in plain English and get answers with charts instantly. Works natively in Excel and Slack where business users already work. No SQL, no training, no semantic layer maintenance ever required.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Choose Scoop If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You need real investigation capability with 3-10 follow-up questions per analysis</li>
<li class="content-section__list-item">Business users want complete autonomy without waiting for IT dashboard updates</li>
<li class="content-section__list-item">Your team works primarily in Excel and needs analytics without leaving it</li>
<li class="content-section__list-item">You're tired of paying for consultants to modify simple dashboard filters</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Domo If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already invested heavily in the Domo ecosystem and can't migrate</li>
<li class="content-section__list-item">Your use cases are purely operational dashboards without investigation needs</li>
<li class="content-section__list-item">You have dedicated BI developers who enjoy maintaining semantic layers</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Power BI Copilot If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">Your organization mandates Microsoft-only solutions regardless of user experience</li>
<li class="content-section__list-item">You only need basic KPI dashboards without follow-up investigation capability</li>
<li class="content-section__list-item">IT control over all analytics is more important than business user autonomy</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The data reveals a clear hierarchy: Scoop's 82/100 BUA score reflects genuine business empowerment . Business users gain true autonomy, asking follow-up questions immediately instead of filing IT tickets for dashboard modifications.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">At-a-Glance Comparison</h2>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Dimension</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">62/100</td><td class="">32/100</td><td class="scoop-cell">82/100</td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">BUA Framework Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Autonomy (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Autonomy</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Investigation Depth</td><td class="">2/8</td><td class="">3/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Setup Requirements</td><td class="">0/8</td><td class="">2/8</td><td class="">5/8</td>
</tr>
<tr>
<td class="">Query Flexibility</td><td class="">0/8</td><td class="">2/8</td><td class="">5/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 18/20 on Autonomy, enabling business users to investigate data independently through natural language. Power BI Copilot scores 7/20, requiring IT-maintained semantic layers. Domo scores 0/20, limiting users to pre-built dashboards. Scoop's multi-pass investigation capability eliminates IT bottlenecks completely.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Flow (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Flow</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native Integration</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Workflow Continuity</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 17/20 on Flow by working natively in Slack, Teams and email where business users already collaborate. Power BI Copilot manages 6/20, limited to Teams integration. Domo scores 0/20, requiring portal access for everything. Scoop brings data to users' workflows; others force users to visit BI portals.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Understanding (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Understanding</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Business Terminology</td><td class="">0/8</td><td class="">3/8</td><td class="">5/8</td>
</tr>
<tr>
<td class="">Error Recovery</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Understanding versus Power BI Copilot's 7/20 and Domo's unscored status. Scoop handles natural business language without semantic layers, while Power BI Copilot requires specific phrasing and Domo relies entirely on pre-built dashboards. Business users can ask questions naturally in Scoop without learning technical terminology.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Presentation (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Presentation</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Output Format Flexibility</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Business-Ready Output</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Multi-Stakeholder Support</td><td class="">0/8</td><td class="">1/8</td><td class="">3/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 15/20 on Presentation versus Power BI Copilot's 6/20 by generating context-aware outputs for each question. Power BI locks users into predetermined dashboard layouts requiring manual reformatting. Scoop adapts presentation format to audience needs, creating executive summaries or detailed analyses automatically.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Data (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Data</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>Domo</th><th>Power BI Copilot</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Connection Setup</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Data Preparation</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Refresh Management</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Source Flexibility</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Data capabilities, enabling business users to connect databases directly without IT help. Power BI Copilot scores 6/20, requiring IT-maintained semantic models before any access. Domo wasn't scored but shares similar IT dependencies. Scoop eliminates weeks of waiting for new data connections.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">Capability Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Investigation & Root Cause Analysis</h3>
<p class="content-section__paragraph">When revenue drops 15% unexpectedly, the difference between platforms becomes stark. Traditional BI shows you the drop happened. Investigation platforms help you discover why. This capability separates single-query dashboards from true analytical thinking. Most platforms require you to know what questions to ask. Investigation means the system helps you discover questions you didn't know existed. It's the difference between a speedometer showing you're slowing down and a diagnostic system explaining why your engine is failing.</p>
<p class="content-section__paragraph">The architectural divide is fundamental. Domo and Power BI Copilot operate on a dashboard paradigm—each question gets one answer. You ask about revenue. You get a chart. To investigate why revenue dropped requires opening new dashboards or writing new queries. Scoop maintains conversational context across multiple queries. Ask about revenue drops. Scoop shows the decline, then you ask 'why?' Scoop automatically investigates seasonality, customer segments, product mix, and regional variations. It's testing hypotheses you haven't even formed yet. Power BI Copilot can generate initial visualizations from natural language but requires DAX knowledge for deeper investigation. Each follow-up question essentially starts fresh. Domo's Beast Mode calculations enable complex analysis but demand technical skills. You're building investigation paths manually that Scoop explores automatically. The difference shows in metrics: average root cause discovery takes 3-5 minutes in Scoop versus 30-60 minutes in traditional platforms. That's not just efficiency—it's the difference between investigating hunches and accepting surface-level answers.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager sees same-store sales dropped 12% last month. In Scoop, she types: 'Why did same-store sales drop in October?' Scoop immediately analyzes: weather impact (no correlation), inventory levels (normal), promotional calendar (gap identified), competitor activity (new store openings), and transaction patterns (fewer visits, same basket size). It surfaces that three competitors opened within 2 miles of top stores. Total investigation: 4 minutes, 3 follow-up questions. In Power BI, she'd need separate reports for weather analysis, promotional impact, and competitive intelligence—if they exist. Each requires different DAX formulas. Domo would require Beast Mode calculations for year-over-year comparisons, then manual correlation with external competitive data. The investigation becomes a multi-hour project requiring IT support versus a coffee-break conversation.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about features—it's about architecture. Scoop's conversational AI maintains context across queries, automatically tests hypotheses, and discovers patterns without configuration. Traditional platforms require you to know what to ask and how to ask it. When finding root causes takes 3 minutes instead of 30, investigation becomes routine rather than exceptional. That transforms how businesses understand their operations.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Excel & Spreadsheet Integration</h3>
<p class="content-section__paragraph">Excel remains the world's most-used business intelligence tool, with 750 million users who already know how to use it. The real question isn't whether platforms connect to Excel—it's how seamlessly business users can leverage their existing Excel workflows while gaining modern analytics power. Most BI vendors treat Excel as an afterthought, a place to export data when their tools fall short. But for business users, Excel is home base—where analysis begins, where models live, and where decisions get made. Let's examine how each platform bridges this critical gap.</p>
<p class="content-section__paragraph">The fundamental disconnect between traditional BI and Excel isn't technical—it's philosophical. Domo and Power BI view Excel as a destination for data export, not a legitimate analytics environment. Their Excel add-ins are essentially data pumps that push pre-aggregated results into spreadsheets, breaking the formulas and models users spent years building. Power BI's 'Analyze in Excel' maintains live OLAP connections but requires users to understand MDX concepts and pivot table limitations. Neither platform allows natural language investigation from within Excel itself. Users must context-switch to the vendor's interface for anything beyond basic data refresh. Scoop takes a radically different approach. Instead of forcing Excel users to learn a new platform, Scoop brings AI-powered analysis directly to where they work. Users can investigate complex questions through natural conversation while preserving their existing Excel models and formulas. A financial analyst can type 'Why did margins drop in Northeast region?' and get a complete investigation—checking seasonality, comparing product mix, analyzing customer segments—all while staying in Excel. This isn't about features; it's about respecting how 750 million people actually work.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CFO maintains a complex Excel model for quarterly forecasting, with 200+ custom formulas linking multiple worksheets. She notices an anomaly in Southeast region margins. With Domo, she must leave Excel, log into Domo's portal, navigate through dashboards, and manually export data back—breaking her formula links. Power BI Copilot requires her to rebuild queries using DAX expressions, losing her investigation context with each export. With Scoop, she simply types in a chat window: 'Why did Southeast margins drop 5% last quarter?' Scoop investigates automatically, checking product mix changes, customer concentration, pricing variations, and competitive pressure. Each answer stays in context, allowing follow-up questions like 'Which customers drove the biggest impact?' Her Excel formulas remain intact, updating automatically with fresh insights. Total time: 4 minutes versus 45 minutes of context-switching and data reconstruction.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration reveals each platform's true philosophy about business users. Domo and Power BI treat Excel as a necessary evil, a place to dump data when their tools aren't enough. Scoop recognizes Excel as the legitimate analytics environment it is for millions of users. By bringing AI-powered investigation directly to Excel without breaking existing workflows, Scoop eliminates the false choice between familiar tools and modern analytics.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">Business decisions rarely happen in isolation. When executives ask 'What happens if we raise prices 5% while competitors hold steady?', they need to see multiple scenarios playing out simultaneously. This isn't about static what-if calculators—it's about dynamically comparing different business paths with full analytical depth. Traditional BI forces users to build separate reports for each scenario, then mentally juggle the comparisons. Modern platforms should let business users explore parallel universes of their data, seeing how different decisions cascade through metrics. The ability to run side-by-side scenarios without IT involvement fundamentally changes how quickly teams can evaluate strategic options.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. Domo treats scenarios as dashboard variants, requiring users to duplicate entire workbooks and manually maintain parallel versions. Each scenario change means rebuilding calculations in Beast Mode. Power BI Copilot offers basic parameter adjustments through slicers, but creating true side-by-side comparisons demands DAX expertise. Users must pre-define scenario logic in measures, limiting exploration to IT-approved paths. Scoop's conversational approach transforms scenario analysis. Users simply describe scenarios in plain English: 'Compare current pricing with 5% increase and 10% increase, showing impact on revenue, churn, and market share.' The AI understands relationships between metrics, automatically calculating cascading effects. When assumptions change, users just tell Scoop the new parameters. No formulas, no rebuilding, no waiting for IT. This isn't just convenience—it's about investigation speed. Strategic decisions often require exploring 10-15 scenarios before finding the optimal path. In Domo or Power BI, each scenario takes 30-45 minutes to properly configure. That's a full day of work. Scoop users explore the same scenarios in under an hour, leaving time for deeper analysis of promising options.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A pricing manager needs to evaluate three strategies before tomorrow's board meeting: maintain current prices, match competitor's 5% cut, or differentiate with premium positioning at 10% higher. In Domo, she'd need to create three dashboard copies, manually adjusting Beast Mode calculations in each. The revenue impact formulas alone would take an hour per scenario. Power BI Copilot could handle basic price changes through parameters, but showing customer segment impacts would require pre-built DAX measures her team doesn't have. With Scoop, she types: 'Show me three scenarios side by side: current pricing, 5% decrease, and 10% increase. Include impact on revenue by segment, projected churn rates, and competitor market share shifts.' Within minutes, she sees all three futures displayed simultaneously, with automatic calculations of cross-elasticity effects. She notices the premium scenario actually increases revenue despite volume loss. She asks Scoop to add a fourth scenario: '7% increase with loyalty discounts for long-term customers.' The complete analysis that would take all day in traditional BI platforms is done in 20 minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Side-by-side scenario analysis reveals the investigation gap in traditional BI. While Domo and Power BI treat scenarios as static dashboard variants requiring technical setup, Scoop enables fluid exploration through conversation. Business users can test multiple strategies simultaneously without formula knowledge or IT support. The difference isn't just speed—it's the ability to explore enough scenarios to find optimal decisions rather than settling for the few IT has time to build.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Machine Learning & Pattern Discovery</h3>
<p class="content-section__paragraph">Your sales data contains hidden patterns that predict customer churn, forecast demand spikes, and reveal competitive threats. But discovering these patterns shouldn't require a data science degree. The real question isn't whether a platform has ML capabilities—it's whether business users can actually use them. Let's examine how each platform democratizes pattern discovery, from automatic anomaly detection to predictive insights that arrive without configuration.</p>
<p class="content-section__paragraph">The architecture tells the story. Domo's AutoML lives in a separate workbench requiring configuration, training, and deployment—a multi-week process that excludes 95% of business users. Power BI Copilot offers basic pattern detection through its Key Influencers visual, but you must know to use it and interpret results correctly. Scoop takes a fundamentally different approach: ML runs automatically on every query. Ask 'What's driving customer churn?' and Scoop tests dozens of variables, surfaces correlations, and explains findings in plain English. No configuration. No waiting. The technical sophistication happens invisibly. This isn't about having more ML algorithms—Domo probably has more. It's about making ML accessible to the marketing manager who notices something odd in campaign performance. When pattern discovery requires zero setup, it actually gets used. Business users discover insights they'd never think to look for because the barrier disappeared.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns. With Scoop, she types: 'What's causing stockouts in California stores?' Scoop automatically analyzes: correlating weather data, comparing promotional calendars, checking supplier delays, testing demographic shifts, and identifying that a competitor's aggressive pricing in three key markets is driving unexpected demand. Total time: 2 minutes, zero configuration. With Domo, she'd need to request an AutoML project from IT, wait weeks for model training, then interpret technical outputs. Power BI would require manually building multiple reports to test each hypothesis—assuming she knew what to test. The Scoop user already has actionable insights while others are still defining requirements.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in Scoop isn't a feature you configure—it's intelligence that runs automatically on every question. While Domo and Power BI gate ML behind technical interfaces and separate modules, Scoop makes pattern discovery as simple as asking a question. Business users get predictive insights without knowing they're using ML. That's the difference between ML as a capability and ML as an everyday tool.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Modern business happens everywhere—in Excel models, Slack threads, mobile devices, and email chains. Yet most BI tools force users into separate portals, breaking their flow and creating context-switching friction. The real test isn't whether a platform has mobile apps or integrations, but whether analytics naturally flows into existing work patterns. When a sales manager gets a Slack alert about dropping conversion rates, can they investigate right there, or must they switch to another tool? This capability determines whether analytics becomes part of daily work or remains a separate, occasional activity.</p>
<p class="content-section__paragraph">The fundamental divide isn't about having integrations—it's about investigation depth within workflows. Domo and Power BI treat integrations as viewing windows into their main platforms. You can see dashboards in Teams or get alerts in Slack, but real analysis requires returning to the portal. This creates a two-class system: mobile users who can only consume, and desktop users who can analyze. Scoop's architecture enables full investigation everywhere because natural language doesn't require complex UIs. A CEO can ask 'Why did margins drop?' in email and receive not just the answer but continue investigating with follow-ups. The Excel integration exemplifies this difference. Power BI requires learning Power Query syntax and DAX formulas. Domo exports static data. Scoop adds a chat panel where users type questions in plain English while staying in their spreadsheets. For organizations where Excel remains the primary analysis tool—which Gartner estimates at 71% of enterprises—this native integration eliminates the traditional BI adoption barrier. Mobile reveals another architectural truth. Dashboard-based tools struggle on small screens because their visual paradigm requires significant real estate. Natural language thrives on mobile because it only needs a text input and response area.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A VP of Sales receives a Slack alert: 'Southeast region missed quota by 12%.' With traditional BI, she must switch to her laptop, log into the portal, navigate to the regional dashboard, apply filters, and build visualizations to investigate. Total time: 15-20 minutes, assuming she remembers how. With Scoop, she types directly in Slack: 'Which products underperformed in Southeast?' Scoop responds with a chart. She follows up: 'Compare to last quarter.' Then: 'Show me top accounts that didn't reorder.' Within 3 minutes, without leaving Slack, she's identified that three major accounts delayed orders due to budget freezes. She forwards the thread to her team with action items. The investigation happened where the conversation was, preserving full context for everyone.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about checkboxes for Slack, Teams, or mobile apps. It's about investigation depth within natural workflows. While Domo and Power BI offer viewing windows into their platforms, Scoop enables full analytical conversations wherever work happens. This transforms analytics from a destination to a capability embedded throughout the organization.</p>
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<div class="content-section__container">
<h2 class="content-section__title">Frequently Asked Questions</h2>
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<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard. Ask questions in plain English, get answers with charts. Works natively in Excel and Slack. Unlike Domo and Power BI Copilot which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<h3 class="content-section__subtitle">Which is better for business users: Domo or Power BI Copilot?</h3>
<p class="content-section__paragraph">Domo scores 62/100 on business autonomy versus Power BI Copilot's 32/100. Domo offers better self-service capabilities but still requires semantic layer maintenance. Power BI Copilot heavily depends on IT for DAX expressions. Neither matches Scoop's 82/100 score for true business user independence.</p>
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<h3 class="content-section__subtitle">Can Power BI Copilot do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Power BI Copilot generates single queries from prompts without investigation capability. It can't chain multiple queries to find root causes. Users must manually craft follow-up questions using DAX. Scoop automatically runs 3-10 connected queries, testing hypotheses until it finds the underlying cause.</p>
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<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop excels at multi-step investigation, automatically chaining 3-10 queries per question. It tests hypotheses, eliminates false leads, and drills into root causes without user intervention. Domo and Power BI Copilot require manual query crafting for each step, breaking the analytical flow.</p>
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<h3 class="content-section__subtitle">What does Domo really cost including implementation?</h3>
<p class="content-section__paragraph">Domo's true cost includes licenses, 3-6 month implementation, training programs, semantic layer maintenance, consultants, and productivity loss during adoption. Total cost typically reaches 5-10x the license fee. Scoop eliminates implementation, training, and consultant costs—reducing TCO by 90% versus traditional BI platforms.</p>
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<h3 class="content-section__subtitle">How long does it take to learn Domo?</h3>
<p class="content-section__paragraph">Domo requires 2-4 weeks of formal training plus 3-6 months to achieve proficiency. Users must learn Beast Mode calculations, ETL processes, and dashboard design. Power users need additional SQL training. Scoop requires zero training—if you can type a question, you can analyze data immediately.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can business users use Scoop without IT help?</h3>
<p class="content-section__paragraph">Yes, business users connect Scoop to data sources in 30 seconds and start asking questions immediately. No semantic layers, no SQL, no IT tickets. Domo requires IT for data modeling and Power BI Copilot needs DAX expertise. Scoop delivers true self-service analytics.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Power BI Copilot integrate with Microsoft Teams?</h3>
<p class="content-section__paragraph">Power BI Copilot works within Power BI Desktop and Service, not directly in Teams. Users must switch contexts, breaking workflow. Sharing requires publishing reports first. Scoop works natively in Slack and Teams, answering questions where conversations happen without context switching or report publishing.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How is Scoop different from traditional BI tools?</h3>
<p class="content-section__paragraph">Scoop is an AI analyst you chat with, not a dashboard builder. Ask questions naturally, get answers with charts instantly. No semantic layers, no report building, no SQL. Traditional BI requires months of setup; Scoop connects in 30 seconds and answers questions immediately.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Power BI Copilot?</h3>
<p class="content-section__paragraph">Power BI Copilot requires DAX knowledge for anything beyond basic queries. Complex questions need manual formula writing. The AI assists but can't replace technical expertise. Scoop handles all query complexity internally—business users just ask questions in plain English without any technical knowledge.</p>
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<h3 class="content-section__subtitle">How do I investigate anomalies in Domo?</h3>
<p class="content-section__paragraph">Domo requires building drill-down dashboards before anomalies occur. Investigation means clicking through pre-built views, not asking why. Real root cause analysis requires creating new cards manually. Scoop investigates anomalies dynamically, running multiple queries automatically to find causes without pre-configuration.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What's the typical implementation time for Power BI Copilot?</h3>
<p class="content-section__paragraph">Power BI Copilot requires 2-4 months: setting up semantic models, training users on DAX, building initial reports, and establishing governance. Plus ongoing maintenance as data changes. Scoop connects in 30 seconds with no setup, no training, and no maintenance required.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Is Domo easier to use than Power BI Copilot?</h3>
<p class="content-section__paragraph">Domo scores higher on usability (62/100 versus 32/100 BUA), offering better drag-and-drop interfaces. However, both require technical knowledge—Beast Mode for Domo, DAX for Power BI. Neither approaches Scoop's simplicity where business users just type questions and get answers instantly.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Why doesn't Scoop require training?</h3>
<p class="content-section__paragraph">Scoop uses natural language processing—you ask questions like you'd ask a colleague. No query languages, no formula syntax, no dashboard design. The AI handles all technical complexity internally. If you can write an email or use ChatGPT, you already know how to use Scoop.</p>
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</section>
<section class="cta-section">
<div class="cta-section__title">See Scoop in Action</div>
<div class="cta-section__subtitle">
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</div>
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