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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 Snowflake Cortex 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">Snowflake Cortex</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) requires moderate IT support and Snowflake Cortex (26/100) remains developer-dependent. Both competitors trap users in single-query dashboards, blocking the iterative questioning real analysis demands. Choose Scoop for immediate independence, competitors only within existing vendor commitments.</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">Business users need to investigate data independently without IT tickets</li>
<li class="content-section__list-item">Your team lives in Excel and needs analytics there natively</li>
<li class="content-section__list-item">You want answers in minutes, not weeks of dashboard development</li>
<li class="content-section__list-item">Non-technical teams need self-service analytics without SQL training</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 Domo's ecosystem and training</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 IT resources for constant dashboard maintenance</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Snowflake Cortex If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You have a team of SQL-fluent data engineers available</li>
<li class="content-section__list-item">Your Snowflake investment requires using their native tools</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The 56-point BUA gap between Scoop and Snowflake Cortex represents the difference between business empowerment and IT dependency . The future belongs to platforms that trust business users with their own data.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">62/100</td><td class="">26/100</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>Snowflake Cortex</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="">Query Creation</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Metric Definition</td><td class="">0/8</td><td class="">0/8</td><td class="">3/8</td>
</tr>
<tr>
<td class="">Visualization Selection</td><td class="">0/8</td><td class="">0/8</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 18/20 on Autonomy versus Domo's 2/20 and Snowflake Cortex's 4/20. Business users can investigate problems through natural conversation with Scoop, while Domo requires IT to build dashboards first and Snowflake Cortex demands SQL knowledge. Only Scoop enables true self-service analytics without technical dependencies.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Workflow Integration</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="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Response Speed</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Collaboration Flow</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</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 operating natively in Slack and Teams, while Domo and Snowflake score 0/20 due to requiring separate portal access. Scoop users get answers in 10-30 seconds without leaving their workspace, whereas Domo and Snowflake force context-breaking switches to external dashboards.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Business Terminology</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Error Handling</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">0/8</td><td class="">0/8</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 Understanding versus 0/20 for both Domo and Snowflake Cortex. Scoop processes natural business language directly while Domo and Snowflake require technical query construction through SQL or structured interfaces. This 16-point advantage means business users can ask questions conversationally without learning data schemas or query languages.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Chart Selection</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Formatting & Polish</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Context & Annotations</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Export & Sharing</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Speed to Output</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</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 0/20 for Domo and Snowflake Cortex. Scoop automatically selects optimal chart types, formats professionally, and adds context in under 60 seconds. Domo requires manual chart selection and extensive formatting. Snowflake Cortex needs SQL before any visualization. Business users get board-ready outputs immediately with Scoop.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Connection Simplicity</td><td class="">2/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Preparation</td><td class="">1/8</td><td class="">2/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Semantic Layer Maintenance</td><td class="">0/8</td><td class="">1/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Data Freshness</td><td class="">3/8</td><td class="">4/8</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 16/20 on Data capabilities by eliminating semantic layers and ETL requirements. Domo and Snowflake Cortex require extensive IT setup, data modeling, and transformation before business users can analyze anything. Scoop connects directly to raw data sources, enabling immediate analysis without IT intervention.</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 platform 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 operates on a dashboard paradigm where each question requires a new card or drill-down path. You must know your investigation path beforehand. Snowflake Cortex provides powerful SQL-based exploration but requires technical expertise for each investigation step. Users write queries, not questions. Scoop treats investigation as conversation. Ask 'Why did sales drop?' and it automatically checks seasonality, segments, correlations, and anomalies. Each answer prompts natural follow-ups. This isn't about better dashboards—it's about thinking differently. Traditional platforms make you be the investigator. Scoop is your investigation partner. The difference shows in metrics: Domo users average 8 dashboard clicks to reach insights. Cortex users write 4-6 SQL queries. Scoop users have 2-3 sentence conversations. When a CEO asks why margins dropped, Domo shows the drop across beautiful visualizations. Cortex lets analysts dig with SQL. Scoop explains that three customers shifted orders to next quarter, identifies which ones, and suggests recovery actions. Same data, fundamentally different experience.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CFO notices operating margins dropped 3% last month. With Domo, she opens the margin dashboard, clicks through regions (5 clicks), opens cost center details (3 more clicks), exports to Excel for analysis, then schedules a meeting with analysts for deeper investigation. Total time: 2 hours across 2 days. With Cortex, her analyst writes SQL queries checking various hypotheses—labor costs, material prices, overhead allocation. After 6 queries and 45 minutes, they identify overtime spike in two facilities. With Scoop, the CFO types: 'Why did operating margins drop last month?' Scoop automatically investigates: labor costs up 8% in Southeast facilities, overtime doubled due to equipment failure, maintenance backlog grew 40%. Follow-up: 'Which equipment failed?' Answer: 'Packaging line 3 in Atlanta, down 72 hours.' Total time: 3 minutes, no analyst needed.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about features—it's about architecture. Domo and Cortex built dashboard and query tools that can be stretched for investigation. Scoop built an investigation engine from day one. The result: business users solve problems independently in minutes that previously required analysts and hours. This isn't incremental improvement. It's a paradigm shift from showing data to explaining patterns.</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 open Excel to build reports from BI data. They export from dashboards, manipulate in spreadsheets, then email the results. This workflow reveals a fundamental truth: business users think in Excel. The real question isn't whether platforms connect to Excel, but how many clicks, exports, and copy-pastes stand between a spreadsheet user and their data. Let's examine how each platform bridges this critical gap between where data lives and where work happens.</p>
<p class="content-section__paragraph">The Excel integration battle reveals competing philosophies about how business users should work with data. Domo provides a traditional add-in requiring users to navigate their semantic layer through menus and formulas. You're still building queries, just from Excel instead of the web portal. Snowflake Cortex offers no Excel integration at all—users must write SQL queries in Snowflake, export results, then import to Excel. This three-step process breaks whenever data updates. Scoop takes a fundamentally different approach: chat with your data directly from Excel. Type 'What were our top 10 customers last quarter?' and get results instantly. No formulas, no semantic layer navigation, no SQL. The architectural difference matters. Traditional BI platforms treat Excel as an output destination. Export your dashboard, manipulate the static data, email the file. Scoop treats Excel as a working environment. Ask follow-up questions, investigate patterns, refine analysis—all without leaving your spreadsheet. This eliminates the export-manipulate-redistribute cycle that consumes hours weekly. For the 750 million Excel users worldwide, the question isn't whether they'll adopt new BI tools. It's whether those tools meet them where they already work.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to build the monthly revenue report every first Monday. With Domo, she opens Excel, launches the Domo add-in, navigates through three menu levels to find the revenue dataset, writes a formula to pull last month's data, then manually formats the results. If she needs additional context, she switches to the Domo web portal, exports another dataset, and merges in Excel. Total time: 45 minutes. With Snowflake Cortex, she logs into Snowflake, writes a SQL query, exports to CSV, imports to Excel, then repeats for each data element needed. Total time: 60 minutes. With Scoop, she types in Excel: 'Show me revenue by product line for last month compared to prior year.' Results appear instantly. She asks: 'Why did Enterprise software decline?' Scoop investigates automatically. Total time: 5 minutes. The 40-minute difference happens every week, across every analyst.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration exposes the gap between vendor promises and user reality. While Domo offers an add-in, it still requires navigating complex semantic layers. Snowflake Cortex ignores Excel entirely, forcing technical workflows. Scoop eliminates the friction by letting users chat naturally with data directly from Excel. For organizations where Excel drives business decisions, this difference translates to hours saved weekly and faster, more accurate reporting.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">When executives need to compare multiple business scenarios—like 'What if we expand to Europe versus Asia?'—they need platforms that can hold multiple analyses in view simultaneously. This isn't about switching between reports; it's about seeing competing hypotheses side-by-side to make million-dollar decisions. Traditional BI forces sequential analysis, losing context with each switch. Modern platforms should enable parallel investigation paths, letting decision-makers compare outcomes, assumptions, and sensitivities across scenarios without losing their analytical thread.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. Domo's dashboard-centric model treats each scenario as a separate artifact, requiring users to mentally juggle multiple screens or manually build comparison views. You lose investigative context switching between dashboards. Snowflake Cortex offers more flexibility through SQL notebooks, but demands users write complex queries with CTEs and UNION statements to compare scenarios. Business users hit a wall immediately. Scoop's conversation-based architecture naturally supports parallel analysis threads. Ask 'Compare revenue if we raise prices 10% versus expanding to new markets' and both scenarios calculate simultaneously, displaying side-by-side with assumptions clearly marked. The key difference: Scoop maintains analytical context across scenarios while others force sequential thinking. When testing sensitivity, Domo requires Beast Mode calculations or pre-built apps—typically involving consultants. Cortex users must write parameterized SQL. Scoop handles it conversationally: 'How sensitive is this to customer churn rates?' Real scenario analysis isn't about pretty dashboards. It's about rapidly testing hypotheses, adjusting variables, and comparing outcomes—exactly what conversation enables and dashboards prevent.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CPO evaluates two pricing strategies for next quarter: premium pricing with fewer customers versus volume pricing with lower margins. With Scoop, she types: 'Compare revenue impact of raising prices 15% with 20% churn versus cutting prices 10% with 30% growth.' Scoop generates parallel analyses showing revenue projections, margin impacts, and customer retention curves side-by-side. She follows up: 'Add best and worst case scenarios for each.' Four scenarios now display simultaneously with key metrics highlighted. Total time: 5 minutes. In Domo, she'd need IT to build custom dashboards with Beast Mode calculations for each scenario—a 2-week project. With Cortex, she'd write complex SQL with multiple CTEs, taking hours if she has SQL skills, impossible if not. The business impact: Scoop enables rapid strategic decision-making while competitors force either weeks of delay or technical dependency.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Side-by-side scenario analysis reveals the fundamental limitation of dashboard architectures: they can't hold multiple investigative threads simultaneously. While Domo requires separate dashboards and Cortex demands complex SQL, Scoop's conversational approach naturally supports parallel analysis paths. Business users can compare scenarios, test sensitivities, and track assumptions without technical skills or IT support—turning days of analysis into minutes of 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 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 before you ask.</p>
<p class="content-section__paragraph">The architecture tells the story. Domo's AutoML requires navigating to separate tools, configuring models, and understanding parameters like confidence intervals. Business users rarely venture there. Snowflake Cortex brings ML to SQL with functions like ANOMALY_DETECTION and FORECAST, but you still need SQL expertise. A marketing manager can't type 'find unusual patterns in campaign performance.' They must write SELECT statements with proper syntax. Scoop embeds ML into natural conversation. Ask 'What's unusual about last month's sales?' and get anomalies highlighted automatically. No configuration. No separate tools. The platform runs pattern detection on every query, surfacing insights you didn't know to look for. This isn't about having more algorithms—it's about making them invisible. When ML requires zero setup, adoption goes from 5% of users to 95%. The real innovation isn't the math; it's removing the math from the user experience entirely.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory issues but can't pinpoint the cause. With Scoop, she types: 'What patterns explain our stockout problems?' Scoop automatically analyzes correlations across suppliers, seasons, promotions, and regions. It discovers that stockouts spike when promotional forecasts exceed 20% and suppliers are based in specific ports. Total discovery time: 4 minutes. With Domo, she'd need IT to build an AutoML project, taking 2-3 weeks. In Snowflake Cortex, a data analyst would write multiple CORRELATION queries, test hypotheses manually, and deliver findings in 3-4 days. The business difference: Scoop identifies the pattern before the next ordering cycle, preventing $200K in lost sales.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning becomes powerful when it's invisible. While Domo and Snowflake Cortex offer sophisticated ML capabilities, they remain locked behind technical barriers that exclude 90% of business users. Scoop makes pattern discovery as simple as asking a question, running ML automatically on every query. The result: insights that arrive before problems become crises.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Your sales team discovers a critical insight at 2 PM. By 2:15, it needs to be in your CEO's hands via Slack, your analyst's Excel model, and your field team's phones. This is the reality of modern business intelligence—insights must flow where work happens, not trap users in BI portals. The difference between platforms that integrate naturally into workflows versus those requiring constant context-switching can mean hours of productivity daily. Let's examine how Domo, Snowflake Cortex, and Scoop handle the critical challenge of meeting users where they already work.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in workflow integration. Domo offers traditional BI integrations—scheduled reports, dashboard links, basic alerts. Users can push content out, but can't pull insights in. You'll find dashboard snapshots in Slack, but investigating further means opening Domo. Their mobile app displays dashboards beautifully but offers no investigation capability. Snowflake Cortex takes a developer-first approach. Everything requires SQL, even in their Streamlit apps. There's no Slack integration, no mobile presence, no Excel add-in. It's powerful if you're writing Python, invisible if you're in sales. Scoop flips the model entirely. The same conversational interface works everywhere—Excel, Slack, mobile, email. A sales manager can start investigating in Slack, continue in Excel, and finish on their phone. No context switching, no new interfaces to learn. The natural language API means developers can embed analysis anywhere without SQL complexity. This isn't about features; it's about architecture. Dashboard tools can only push static views. Investigation tools can respond dynamically wherever users work.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning, 9 AM. Your European sales director messages the team Slack channel: 'German enterprise deals are down 40% this month. What's happening?' With Scoop's Slack integration, she types her question directly in the channel. Scoop responds with initial analysis, she asks follow-ups, teammates add questions—all in the same thread. By 9:15, they've identified that a competitor launched aggressive pricing two weeks ago. The whole investigation is visible to the team. With Domo, she'd share a dashboard link. Team members would leave Slack, log into Domo, navigate to the dashboard, and see the what but not the why. Investigation would happen in isolation. With Snowflake Cortex, there's no Slack integration at all. Someone would need to write SQL queries, run them in Snowflake, screenshot results, and paste them back into Slack. The investigation would take hours instead of minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop brings full analytical power directly into Excel, Slack, and mobile—where 90% of business work happens. Domo pushes static dashboards to these channels but requires portal access for real investigation. Snowflake Cortex offers no workflow integration beyond SQL APIs. For organizations tired of forcing users into BI portals, Scoop's architecture eliminates the friction between questions and answers.</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 Domo and Snowflake Cortex 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">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop excels at multi-step investigation, automatically chaining 3-10 queries to find root causes. Domo requires manual dashboard navigation, Snowflake Cortex handles single queries only. Scoop tests hypotheses like a human analyst would, asking follow-up questions until it finds the complete answer.</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">Absolutely. Scoop scores 82/100 on Business User Autonomy, versus Domo's 62/100 and Snowflake Cortex's 26/100. Business users connect data sources, ask questions, and share insights completely independently. No semantic layers, no SQL, no IT tickets. Just natural conversation with an AI that understands your business.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What does Domo really cost including implementation?</h3>
<p class="content-section__paragraph">Domo's true cost typically reaches 5-10x the license fee. Beyond licensing, expect implementation consultants, training programs, semantic layer maintenance, dashboard developers, and ongoing IT support. Most enterprises spend $500K-2M annually. Scoop eliminates these hidden costs with its no-implementation AI approach.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Snowflake Cortex?</h3>
<p class="content-section__paragraph">Yes, Snowflake Cortex requires SQL for anything beyond basic queries. While it offers natural language, complex analysis still needs SQL expertise. Domo uses Beast Mode calculations requiring technical skills. Scoop handles everything through conversation—no SQL, no formulas, just plain English questions.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: Domo or Snowflake Cortex?</h3>
<p class="content-section__paragraph">Domo offers more business user features with its 62/100 BUA score versus Snowflake Cortex's 26/100. However, both still require significant IT support. Domo needs dashboard builders, Cortex needs SQL writers. Scoop's 82/100 score means business users work completely independently.</p>
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<div class="content-section__subsection">
<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 for basic proficiency, plus months to master Beast Mode and ETL. Most organizations need ongoing training programs. Scoop requires zero training—if you can type a question, you're ready. Business users become productive immediately, not after weeks of classes.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Snowflake Cortex do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Snowflake Cortex handles single queries only, scoring 2/8 on investigation capability. It can't automatically explore hypotheses or chain queries. Domo scores 4/8 with manual drill-downs. Scoop scores 8/8, automatically running multiple analyses to uncover root causes without user guidance.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Domo work with Excel?</h3>
<p class="content-section__paragraph">Domo offers limited Excel export but no native integration. Users must log into Domo's portal, losing Excel's familiar interface. Snowflake Cortex has no Excel integration. Scoop works directly inside Excel—analyze data without leaving your spreadsheet, combining AI power with Excel's flexibility.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What's the typical implementation time for Snowflake Cortex?</h3>
<p class="content-section__paragraph">Snowflake Cortex implementation typically takes 3-6 months including data modeling, security setup, and user training. Domo averages 4-8 months for full deployment. Scoop connects in 30 seconds with no implementation phase—just authenticate your data source and start asking questions immediately.</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 platform. Traditional BI like Domo and Snowflake Cortex require building views first, then exploring within those constraints. Scoop answers any question directly through conversation, investigating automatically with multiple queries to find complete answers.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need consultants to use Domo?</h3>
<p class="content-section__paragraph">Yes, most Domo implementations require consultants for setup, dashboard design, and Beast Mode calculations. Partners charge $150-300/hour, with projects often exceeding $100K. Snowflake Cortex needs data architects. Scoop eliminates consultant dependency—business users connect and analyze independently from day one.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can I use Snowflake Cortex directly in Slack?</h3>
<p class="content-section__paragraph">No, Snowflake Cortex requires using Snowflake's interface or SQL clients. Domo offers limited Slack notifications but not full analysis. Scoop works natively in Slack—ask questions and get charts directly in your channels, making data insights part of your team's natural workflow.</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—no special syntax or technical knowledge needed. Domo requires learning dashboard navigation, Beast Mode, and ETL concepts. Snowflake Cortex needs SQL understanding. With Scoop, if you can ask a colleague a question, you can analyze data.</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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