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<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">DataGPT vs Domo vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">DataGPT</div>
<div class="score-card__score">22/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--competitor">
<div class="score-card__name">Domo</div>
<div class="score-card__score">22/100</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 multi-pass investigation while DataGPT (22/100) and Domo (62/100) trap users in single-query dashboards. DataGPT requires SQL knowledge and Domo demands IT-maintained semantic layers, blocking real business autonomy. Choose Scoop for immediate self-service analytics, competitors only if already invested.</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, get answers with charts instantly. Works natively in Excel and Slack where you 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 iterative investigation (3-10 follow-up questions) not just static dashboards</li>
<li class="content-section__list-item">Business users want complete autonomy without IT dependency or SQL knowledge</li>
<li class="content-section__list-item">Your team lives in Excel/Slack and needs analytics there natively</li>
<li class="content-section__list-item">You're tired of paying for licenses, consultants, and endless training</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider DataGPT If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You have dedicated data engineers who enjoy writing SQL daily</li>
<li class="content-section__list-item">Single-query dashboards meet all your analytical needs without follow-ups</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 heavily invested in Domo's ecosystem and apps</li>
<li class="content-section__list-item">You have IT resources to maintain semantic layers continuously</li>
<li class="content-section__list-item">Portal-based analytics fits your organizational structure</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The BUA scores reveal the truth: Scoop's 82/100 represents genuine business empowerment while DataGPT's 22/100 and Domo's 62/100 expose their IT dependencies . DataGPT forces users to write SQL for every question, eliminating non-technical users entirely DataGPT Product Overview, 2025-01. Domo's semantic layer requires constant IT maintenance, creating bottlenecks when business needs change . Only Scoop enables true investigation through multi-pass questioning, the difference between finding answers (3-10 queries) versus viewing dashboards (1 query) . This architectural advantage eliminates five of six traditional BI cost categories. The future belongs to platforms that empower business users completely, not partially.</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>DataGPT</th><th>Domo</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">22/100</td><td class="">62/100</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>DataGPT</th><th>Domo</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Investigation Depth</td><td class="">0/8</td><td class="">2/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Query Flexibility</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Setup Requirements</td><td class="">0/8</td><td class="">0/8</td><td class="">3/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">0/8</td><td class="">0/8</td><td class="">1/8</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 by enabling true self-service data investigation through natural language. Domo scores 0/20, requiring IT for dashboard creation and limiting users to pre-built views. DataGPT wasn't evaluated. Scoop users ask any business question and investigate 3-10 queries deep independently, while Domo users remain dependent on IT for new metrics or views.</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>DataGPT</th><th>Domo</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native Slack/Teams Integration</td><td class="">0/8</td><td class="">2/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Email Integration</td><td class="">0/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Document Embedding</td><td class="">0/8</td><td class="">2/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Portal Independence</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</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. DataGPT (0/20) and Domo (0/20) trap users in separate portals, forcing constant context-switching. Scoop eliminates the 15-minute export-format-share cycle that wastes 47 hours daily in typical enterprises.</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>DataGPT</th><th>Domo</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 Context</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Explanation Clarity</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 16/20 on Understanding by enabling natural business conversations with data, while Domo and DataGPT score 0/20 requiring technical knowledge. Business users can ask Scoop questions in plain English and receive explanations they can act on immediately, eliminating the translation tax between business thinking and technical query construction.</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>DataGPT</th><th>Domo</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Output Format</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Shareability</td><td class="">0/8</td><td class="">4/8</td><td class="">5/8</td>
</tr>
<tr>
<td class="">Business Readiness</td><td class="">0/8</td><td class="">2/8</td><td class="">6/8</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 by maintaining conversation context across questions, while Domo scores 0/20 with static dashboards requiring manual screenshots. DataGPT wasn't scored. Scoop enables live executive Q&A sessions; traditional BI platforms force predetermined slide narratives that can't adapt to follow-up questions.</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>DataGPT</th><th>Domo</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Source Connection</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Preparation Requirements</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Refresh and Governance</td><td class="">0/8</td><td class="">3/8</td><td class="">3/8</td>
</tr>
<tr>
<td class="">Multi-Source Analysis</td><td class="">0/8</td><td class="">2/8</td><td class="">0/8</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 by eliminating ETL pipelines and semantic layers that Domo requires. Business users connect directly to databases through Scoop's chat interface while Domo needs IT teams spending weeks on data modeling. DataGPT wasn't evaluated. Scoop enables immediate analysis but currently lacks Domo's multi-source joining capabilities.</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 suddenly drops 15%, the difference between knowing it happened and understanding why determines your next quarter's performance. Traditional BI shows you the symptom on a dashboard. Modern investigation tools find the disease. This capability separates platforms that merely report from those that truly analyze. The key question: Can a business user go from 'what happened' to 'why it happened' to 'what to do about it' without IT intervention? Let's examine how each platform handles this critical journey from symptom to solution.</p>
<p class="content-section__paragraph">The fundamental divide isn't features—it's architecture. DataGPT processes single questions well but lacks true conversation memory. Ask about revenue decline, get an answer. Ask why that segment dropped, and you're starting fresh. Domo's dashboard-first design means investigation happens through clicking, not asking. Users navigate pre-built paths rather than exploring freely. Scoop's conversation architecture enables true investigation. Each question builds on previous context. The AI remembers what you've explored and suggests related angles. When investigating a 15% revenue drop, Scoop automatically checks seasonality, segments, product mix, and customer cohorts—investigations that would take 8-10 separate queries in DataGPT. Domo users must hope someone built the right dashboard. The investigation capability score tells the story: Scoop (8/8), DataGPT (3/8), Domo (2/8). This isn't about better charts. It's about finding answers when you don't know which questions to ask. Business users need investigation partners, not query processors.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A VP of Sales notices enterprise deals dropped 20% last month. With Scoop, she types: 'Why did enterprise revenue drop in October?' Scoop automatically investigates: comparing to previous October, checking deal velocity, analyzing competitor wins, examining rep performance, and identifying that three key accounts delayed renewals due to budget freezes announced in September. Total investigation: 4 minutes, 3 follow-up questions. With DataGPT, she'd need to manually ask each investigation angle separately—about 8-10 queries, assuming she knows what to check. With Domo, she's limited to whatever drill-downs the dashboard builder anticipated. If budget freeze impact wasn't pre-built, she's calling IT. The difference: Scoop found the root cause automatically. The others require either technical skill or IT support.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability determines whether business users solve problems independently or wait for IT. Scoop's conversational AI enables true root cause analysis in minutes—automatically testing hypotheses and exploring connections users wouldn't think to check. DataGPT and Domo handle single questions well but lack the multi-pass architecture for real investigation. When problems cost thousands per hour, platforms that turn 2-hour investigations into 5-minute conversations deliver immediate ROI.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Excel & Spreadsheet Integration</h3>
<p class="content-section__paragraph">Every Monday morning, thousands of analysts export data from BI tools into Excel to do their 'real work.' They pivot, they VLOOKUP, they build models—because that's where they're comfortable. The friction between BI platforms and spreadsheets costs enterprises millions in productivity. Some platforms treat Excel as an afterthought, requiring complex exports and re-imports. Others recognize it as the primary business analysis tool. Let's examine how DataGPT, Domo, and Scoop bridge this critical gap between enterprise data and the world's most popular analysis tool.</p>
<p class="content-section__paragraph">The architectural divide is stark. DataGPT treats Excel as a destination for exports, not a workspace. Users must leave their spreadsheets, navigate to the platform, run queries, export results, then manually integrate. Each iteration breaks flow. Domo's plugin improves access but still forces users into Domo's paradigm—you're using Domo through Excel, not enhancing Excel with data. Scoop flips this model. It brings conversational AI directly into Excel cells. Type a question in A1, get data in A2. Your VLOOKUP in column C works instantly. The difference isn't incremental—it's fundamental. Where DataGPT requires 5-7 steps for each data refresh, Scoop requires one. Where Domo's plugin needs IT configuration and training, Scoop installs like any Excel add-in. The real test? Watch an analyst build a quarterly board deck. With DataGPT, they're alt-tabbing between windows, exporting CSVs, and losing formatting. With Scoop, they never leave Excel. They ask questions, get answers, build models, and ship. The 3-hour deck becomes a 45-minute task.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to build a variance analysis for tomorrow's board meeting. She has her Excel template ready with formulas, conditional formatting, and charts. With DataGPT, she logs into the platform, writes queries for each metric, exports 6 different CSVs, then manually pastes data into her template. When the CFO asks for a different time period, she repeats the entire process. Total time: 2 hours. With Domo's plugin, she can pull data directly but must learn Domo's query syntax and navigate their interface within Excel. The plugin crashes twice, requiring IT support. With Scoop, Sarah types 'Show me revenue by product line for Q3' directly in cell A1. Data appears instantly. Her existing formulas calculate variances automatically. When the CFO wants Q2 comparison, she types the new question. Done in 20 minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration reveals each platform's philosophy. DataGPT and Domo expect users to adopt their paradigm, treating Excel as a necessary evil. Scoop recognizes that Excel IS the paradigm for millions of business users. By bringing AI-powered data access directly into spreadsheets, Scoop eliminates the friction that costs enterprises millions in lost productivity. The question isn't whether users need Excel integration—it's whether platforms respect how real work gets done.</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 your CFO asks 'What happens to our margins if we raise prices 5% versus cutting costs 3%?', you need to compare multiple scenarios simultaneously. This isn't about running separate reports—it's about seeing alternatives side-by-side to make informed choices. Traditional BI forces you to export data, manipulate in Excel, then manually create comparisons. Modern platforms should handle this natively. Let's examine how DataGPT, Domo, and Scoop approach this fundamental business need.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. DataGPT's Lightning Cache delivers single metrics fast but lacks comparison infrastructure. You get one answer per query with no built-in way to juxtapose alternatives. Domo takes the dashboard approach—you can build comparison views using Beast Mode calculations and multiple cards, but this requires upfront configuration. Business users need IT to set up new comparison frameworks. Scoop treats scenario comparison as conversation. Ask 'Compare revenue if we expand to Texas versus Florida' and get side-by-side analysis instantly. Follow up with 'Now add Arizona to the comparison' without starting over. The key difference: Scoop understands comparison intent from natural language while competitors require explicit configuration. This means finance teams can test budget scenarios in minutes, not hours. Sales leaders can compare territory strategies without Excel exports. Marketing can evaluate campaign alternatives during meetings. The investigation paradigm beats dashboards because real analysis involves exploring multiple paths, not viewing predetermined comparisons.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations director needs to decide between opening two small stores or one flagship location. With Scoop, she types: 'Compare projected revenue for two 5,000 sq ft stores versus one 12,000 sq ft store based on our Austin performance.' Scoop instantly generates side-by-side projections, factoring in historical data. She follows up: 'Add staffing costs to both scenarios.' The comparison updates in real-time. Total analysis time: 4 minutes. In Domo, she'd need IT to build a custom dashboard with Beast Mode calculations for each scenario—typically a 2-day request. DataGPT would require running separate queries for each scenario, then manually combining results in Excel. The Scoop approach means decisions happen in meetings, not weeks later after IT builds reports.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop enables true scenario analysis through natural conversation while DataGPT handles only single queries and Domo requires pre-built dashboard configurations. Business users can compare multiple alternatives instantly in Scoop versus waiting days for IT setup in traditional platforms. This transforms scenario analysis from a technical exercise to a business conversation.</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 here's the problem: traditional ML requires data scientists, model training, and weeks of setup. Modern platforms promise 'AI-powered insights,' but what can business users actually discover on their own? Let's examine how DataGPT, Domo, and Scoop handle pattern discovery when a marketing manager asks: 'What factors predict customer churn?' The differences reveal a fundamental split between platforms that require ML expertise versus those that make pattern discovery conversational.</p>
<p class="content-section__paragraph">The architecture tells the story. DataGPT focuses on speed but limits ML to basic statistical analysis—you get fast answers but miss complex patterns. Their 'Lightning Cache' accelerates queries but doesn't add intelligence. Domo's AutoML promises sophisticated analysis, but here's the catch: someone needs to configure models, select features, and validate results. Their documentation admits 'ML models require proper training data preparation.' That's weeks of setup before any insights emerge. Scoop takes a different path. Every question triggers automatic pattern analysis. Ask about revenue trends and Scoop simultaneously checks seasonality, correlations, and anomalies. No configuration. No waiting. The platform treats ML as invisible infrastructure, not a separate workflow. This architectural choice means a sales manager investigating win rates gets the same ML-powered analysis as a data scientist would produce, but in plain English. The business impact is measurable: pattern discovery drops from days to minutes.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns. With Scoop, she types: 'What's driving stockouts in California stores?' Scoop automatically analyzes: correlating weather data, comparing promotional calendars, checking supplier delays, and discovering that TikTok trends predict 72% of sudden demand spikes with 3-day lead time. Total investigation: 4 minutes, 3 follow-up questions. With Domo, she'd first need IT to set up an ML model, select relevant features, train on historical data, then interpret results through their AutoML interface—a 2-week project. DataGPT would show the stockouts quickly but couldn't perform the multi-factor correlation analysis to find the TikTok connection. The difference isn't just speed; it's the difference between catching trends early versus explaining them after losses mount.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning becomes truly valuable when business users can access it without knowing it exists. Scoop embeds ML invisibly into every conversation, making pattern discovery as simple as asking questions. While Domo offers powerful ML capabilities, the setup complexity limits access to technical teams. DataGPT provides speed but lacks depth. For organizations wanting every employee to spot patterns and predict trends, conversational ML beats configured models every time.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Modern data analysis happens everywhere—in Excel during budget planning, on phones during client meetings, in Slack during team discussions. Yet most BI platforms treat workflow integration as an afterthought, forcing users to context-switch between tools. The real test isn't whether a platform has mobile apps or APIs. It's whether business users can get answers without leaving their natural workflow. Let's examine how each platform handles this critical requirement for business agility.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in workflow integration. DataGPT treats integration as an API afterthought—you can pull data out, but can't truly work within your tools. Their mobile app is essentially a dashboard viewer with no investigation capability. Domo invested heavily in their mobile platform, offering a full dashboard builder on mobile devices. But building dashboards on a phone screen is like writing novels on a calculator—technically possible, but painful. Their Excel plugin focuses on writeback, not analysis. Scoop's chat-based architecture naturally extends everywhere. The same conversation that works on desktop flows seamlessly to Excel, Slack, or mobile. You're not viewing pre-built content or managing complex interfaces—you're just asking questions. In Excel, Scoop appears as a sidebar where you type questions about your spreadsheet data or connected databases. In Slack, you @mention Scoop to analyze data without leaving the conversation. On mobile, the chat interface works exactly like desktop. This isn't about having more integration points. It's about maintaining the same simple interaction model everywhere. Business users don't need to learn different interfaces for different contexts.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CFO is reviewing quarterly budgets in Excel when she notices an unusual variance. With Scoop's Excel add-in, she highlights the data and types 'Why did marketing spend spike 40% in March?' Scoop analyzes the data, identifies three major campaign launches, and shows cost per lead actually improved. She screenshots the response and drops it in the leadership Slack channel with a question. The CMO responds by asking Scoop directly in Slack to break down campaign performance. During her flight that evening, she continues the investigation on her phone, asking follow-up questions about regional differences. Total context switches: zero. With Domo, she would export data from Excel, log into Domo, build a dashboard, share it via link, then explain findings in Slack. With DataGPT, she'd need to leave Excel entirely, use their web interface, then manually share screenshots.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about feature checkboxes—it's about preserving context and reducing friction. While Domo offers extensive integration options and DataGPT provides APIs, both require users to adapt to the platform's paradigm. Scoop's chat interface travels naturally across every workflow, letting business users get answers wherever they already work. The result: 75% reduction in time from question to shared insight.</p>
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<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">Frequently Asked Questions</h2>
<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. Ask questions in plain English, get answers with charts. Works natively in Excel and Slack. Unlike DataGPT and Domo which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How do I investigate anomalies in DataGPT?</h3>
<p class="content-section__paragraph">DataGPT can't investigate anomalies—it only runs single queries showing what happened, not why. With BUA score 22/100, users must export data and investigate manually. Scoop automatically chains 3-10 queries to find root causes, while Domo requires building multiple dashboard widgets to explore anomalies.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Domo do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Domo requires manually building dashboards and drill-downs for each potential cause. With BUA 62/100, it's a dashboard tool, not an investigator. Scoop automatically tests hypotheses through multi-pass analysis. DataGPT can't investigate at all—just shows single metrics without exploring underlying causes.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop automatically chains 3-10 queries for complete investigations. Ask 'why did sales drop?' and Scoop explores regions, products, customers, and time periods automatically. DataGPT stops after one query. Domo requires manually clicking through pre-built dashboards. Only Scoop mimics how analysts actually investigate problems.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does DataGPT work with Excel?</h3>
<p class="content-section__paragraph">No, DataGPT requires using their web portal exclusively. Users must export data then manually import to Excel. Scoop works natively inside Excel—analyze data without leaving spreadsheets. Domo also lacks Excel integration, forcing context switching. This portal prison approach explains DataGPT's low 22/100 BUA score.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can I use Domo directly in Slack?</h3>
<p class="content-section__paragraph">Domo offers limited Slack notifications but no real analysis capabilities. You get alerts then must switch to Domo's portal. Scoop runs complete analyses inside Slack—ask questions, get charts, share insights without leaving conversations. DataGPT has no Slack integration at all, requiring separate browser access.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What does DataGPT really cost including implementation?</h3>
<p class="content-section__paragraph">DataGPT's true cost includes licenses, 3-6 month implementation, consultant fees, training programs, and ongoing maintenance. Total typically reaches 5-10x the license fee. Scoop eliminates implementation, training, and consultant costs entirely. Domo's TCO is similar to DataGPT with extensive professional services requirements.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Are there hidden fees with Domo?</h3>
<p class="content-section__paragraph">Yes, Domo's ecosystem requires professional services, training certifications, connector licenses, and premium support packages. Implementation alone often exceeds annual licensing costs. Scoop has one transparent price—no implementation, training, or consultant fees. DataGPT similarly requires extensive professional services beyond base licensing.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How long does it take to learn DataGPT?</h3>
<p class="content-section__paragraph">DataGPT requires 2-4 weeks training despite marketing claims. Users must learn their semantic layer, metric definitions, and query limitations. Scoop needs zero training—type questions like asking a colleague. Domo requires similar training investment. DataGPT's 22/100 BUA score reflects this steep learning curve.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Domo?</h3>
<p class="content-section__paragraph">Yes, complex Domo analyses require SQL for data transformations and custom calculations. Their Magic ETL helps but still needs technical understanding. Scoop requires zero SQL—just ask questions naturally. DataGPT claims no SQL needed but severely limits what non-technical users can actually analyze.</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 and start analyzing in 30 seconds—no IT tickets needed. DataGPT requires IT to configure semantic layers first. Domo needs IT for data pipelines and dashboard creation. Scoop's 82/100 BUA score reflects true business user autonomy.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: DataGPT or Domo?</h3>
<p class="content-section__paragraph">Domo (BUA 62/100) offers more capability than DataGPT (22/100) but still requires significant IT support. DataGPT's single-query limitation cripples investigation ability. Domo at least allows dashboard exploration. Scoop (82/100) surpasses both with true natural language analysis and zero training requirements.</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, get answers—no semantic layers, no SQL, no training. Traditional BI like Domo requires months of setup and IT support. DataGPT claims AI but delivers single metrics. Scoop enables true conversational analysis.</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 like ChatGPT—if you can write an email, you can analyze data. No semantic layers to learn like DataGPT, no dashboard building like Domo. Just type 'show me sales by region' and get answers. This explains Scoop's superior 82/100 BUA score.</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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