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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 Sisense 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">Sisense</div>
<div class="score-card__score">28/100</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 Sisense (28/100) remains heavily IT-dependent. Both competitors trap users in single-query dashboards, blocking the iterative questioning that real analysis demands. Choose Scoop for immediate independence, competitors only if locked into existing 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, get answers with charts instantly. Works natively in Excel and Slack where business users already work. No SQL, no training, no semantic layer maintenance required ever.</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 multi-pass investigation (3-10 follow-up questions) not just dashboards</li>
<li class="content-section__list-item">Business users want immediate answers without waiting for 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're tired of paying for training, consultants, and maintenance</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 in Domo's ecosystem and can't migrate</li>
<li class="content-section__list-item">Your use cases are purely dashboard-based with no investigation needs</li>
<li class="content-section__list-item">You have dedicated IT resources for ongoing dashboard maintenance</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Sisense If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You need embedded analytics in custom applications</li>
<li class="content-section__list-item">Your organization requires on-premise deployment exclusively</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 demonstrates genuine business empowerment . Business users gain true autonomy, not another IT dependency disguised as self-service.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">62/100</td><td class="">28/100</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Investigation Depth</td><td class="">2/8</td><td class="">1/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Query Flexibility</td><td class="">0/8</td><td class="">0/8</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="">2/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</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 full investigation through natural language, while Domo and Sisense score 0/20 due to IT dependency for any new questions. Business users can ask Scoop unlimited follow-up questions instantly, whereas Domo and Sisense require IT tickets for dashboard modifications taking 3-5 days each.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native 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="">Workflow Continuity</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Response Format</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 living natively in Slack and Teams, while Domo and Sisense score 0/20 as portal-based platforms requiring constant context-switching. Business users can ask Scoop questions without leaving conversations, while traditional BI platforms force workflow disruption for every query.</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>Sisense</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 Support</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Error Recovery & Guidance</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 16/20 on Understanding by accepting natural business language, while Domo and Sisense score 0/20, requiring SQL knowledge or pre-built dashboards. Business users can ask Scoop questions conversationally without learning field names or query syntax, eliminating the IT translation bottleneck.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Automatic Visualization Selection</td><td class="">2/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">1/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Business-Ready Output</td><td class="">3/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Export and Sharing Flexibility</td><td class="">4/8</td><td class="">3/8</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 15/20 on Presentation versus unscored Domo and Sisense, automatically selecting optimal visualizations and formatting based on query context. While Domo and Sisense require manual chart configuration and formatting, Scoop's AI delivers business-ready outputs with explanations, saving 2-3 hours per analysis.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Connection</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="">Source Flexibility</td><td class="">3/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Refresh & Governance</td><td class="">4/8</td><td class="">3/8</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, while Domo and Sisense weren't formally evaluated but show significant limitations. Scoop lets business users connect directly to data sources and start investigating immediately. Domo and Sisense require IT teams to pre-model data into cubes or semantic layers, creating multi-week delays before analysis can begin.</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 drop on a dashboard. Investigation capability means drilling through five layers of 'why' in minutes, not days. Most platforms force you to pre-build every possible investigation path. True investigation means following your curiosity wherever the data leads, asking follow-up questions naturally as insights emerge.</p>
<p class="content-section__paragraph">Domo and Sisense built their architectures around dashboards, not investigations. Domo's drill-down features work within pre-defined hierarchies—you can't suddenly pivot to examine supplier performance when investigating sales drops. Their 'Analyzer' requires knowing exactly which metrics to compare upfront. Sisense's 'Pulse Alerts' detect anomalies but can't explain them. Users must switch to 'Compose SDK' and write code to investigate further. Both platforms average 8-12 separate dashboard views to reach root cause. Scoop's conversation-based architecture means each question builds on the last. Ask 'Why did sales drop?' and Scoop checks seasonality, segments, and correlations automatically. Follow with 'What about competitor pricing?' and it maintains context. The fundamental difference: dashboard tools make you navigate to answers through predetermined paths. Scoop lets you investigate naturally, like talking to an analyst. This architectural advantage means 3-minute investigations versus 45-minute dashboard archaeology expeditions.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning: Regional VP notices Southeast revenue dropped 18% last month. In Domo, she opens the revenue dashboard, clicks through to regional view, exports data, opens customer dashboard, manually compares segments, then messages IT to pull competitor data. Total time: 2 hours across 3 days. In Sisense, she uses ElastiCube to drill into the data model, but needs IT to add competitor pricing fields first. She schedules a meeting for Thursday. With Scoop, she types: 'Why did Southeast revenue drop last month?' Scoop instantly analyzes: finds 3 major accounts churned, identifies competitor won them with 20% lower pricing, shows this pattern across 7 similar accounts. She asks: 'What other accounts match this profile?' Gets 12 at-risk accounts. Then: 'Draft retention offer strategy.' Investigation complete with action plan: 8 minutes total.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about drill-downs or filters—it's about following curiosity wherever it leads. Domo and Sisense require pre-building every possible investigation path, turning 'why' into a 45-minute archaeology project across multiple dashboards. Scoop's conversational approach means asking follow-up questions naturally, reaching root cause in minutes not hours. For organizations where understanding 'why' drives competitive advantage, this isn't a feature difference—it's a fundamental capability gap.</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 BI data into Excel to create the 'real' reports executives actually use. This workflow reveals a fundamental truth: business users trust Excel's flexibility over rigid dashboards. The question isn't whether platforms connect to Excel—it's whether they eliminate the need for that Monday morning export ritual. Let's examine how Domo, Sisense, and Scoop handle the reality that 750 million people already know Excel, making it the world's most popular analytics tool.</p>
<p class="content-section__paragraph">The Excel integration paradox exposes traditional BI's fundamental flaw: platforms spend millions building Excel add-ins to maintain the very workflow they claim to replace. Domo's plugin requires IT setup and pulls from pre-built datasets, limiting users to questions IT anticipated. Sisense offers more flexibility through OLAP cubes but requires understanding MDX syntax—essentially learning a programming language to use a spreadsheet. Both create a bridge between two worlds rather than solving the core problem. Scoop takes a different approach entirely. Instead of forcing Excel integration, it makes integration unnecessary. Users get answers through natural conversation, then paste results into Excel if needed. No plugins, no IT setup, no formula languages. The investigation happens in Scoop; Excel becomes purely for presentation. This eliminates the Monday morning export ritual because users can answer follow-up questions immediately. When a CFO asks 'Why did margins drop in these three regions?', the analyst doesn't need to rebuild queries in Excel—they ask Scoop directly and get answers in seconds.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A financial analyst needs to prepare the monthly board deck. With Domo, she opens Excel, installs the plugin (if IT approved it), authenticates, navigates to the right dataset, and pulls data into cells. But when the CFO asks 'What if we exclude one-time charges?', she's stuck—that calculation wasn't pre-built in Domo. She exports raw data and spends two hours building new formulas. With Sisense, she connects to the ElastiCube through the add-in, writes MDX formulas to pull data, but hits the same wall on ad-hoc questions. With Scoop, she types 'Show me monthly revenue excluding one-time charges for the last 12 months.' Gets the answer. CFO asks about regional breakdown? She types that question. No exports, no formulas, no waiting for IT. The entire investigation takes 5 minutes instead of 2 hours.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration reveals each platform's true architecture. Domo and Sisense build bridges that maintain Excel dependency—their add-ins are band-aids on the real problem. Scoop eliminates the need for Excel as an analytics tool while respecting it as a presentation layer. Business users investigate in natural language, then format in Excel if needed. This isn't about better Excel integration; it's about making integration optional.</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 10% versus cutting costs 5%?', they need to see multiple scenarios simultaneously, not sequentially. This capability—comparing different futures side-by-side—separates strategic planning tools from basic reporting platforms. The difference isn't just convenience; it's about decision velocity. Teams that can instantly visualize competing strategies make decisions 3x faster than those clicking through separate reports. Let's examine how Domo, Sisense, and Scoop handle this critical strategic capability.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. Domo and Sisense follow the dashboard paradigm—each scenario requires a separate dashboard or widget configuration. Business users must request IT to build comparison views, waiting days for what should take minutes. Domo's approach requires duplicating dashboards with different filters, creating maintenance nightmares. Users report managing 15-20 dashboard versions for quarterly planning. Sisense's Compose SDK allows custom comparison tools, but requires JavaScript development. Their documentation admits 'complex scenarios require data model adjustments.' Translation: IT dependency for every new comparison. Scoop's conversational approach changes the game entirely. Type 'Compare revenue if we expand to Texas versus Florida, assuming 20% market penetration.' Scoop generates side-by-side visualizations instantly. No dashboard building. No IT tickets. The investigation continues naturally: 'Now add a third scenario with both states.' This isn't just faster—it's fundamentally different. Business users explore scenarios at the speed of thought, not the speed of IT. The productivity impact is measurable. Traditional BI platforms average 3-5 days from question to comparison view. Scoop delivers in under 3 minutes. For strategic planning sessions, this means exploring 20 scenarios instead of 3.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail CFO enters Monday's board meeting with a critical decision: expand to new markets or optimize existing stores. With Domo, she requested three scenario dashboards last Tuesday. IT delivered Friday afternoon—two scenarios only, citing 'data model limitations.' She opens separate browser tabs, mentally juggling metrics across screens. The board asks for a fourth scenario combining strategies. 'We'll need to table this until next meeting,' she says. With Sisense, the situation improves marginally. Pre-built comparison widgets exist, but only for revenue and costs. When board members ask about inventory impact, she switches to Excel, losing the visual comparison. With Scoop, she types: 'Compare 3 scenarios: expand to 5 new markets, optimize top 10 stores, or hybrid approach with 2 markets and 5 stores.' Charts appear showing revenue, costs, and profitability side-by-side. 'What about inventory requirements?' asks a board member. She types the follow-up. New comparison appears in 30 seconds. The meeting concludes with a decision, not a delay.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the investigation versus dashboard divide perfectly. Domo and Sisense treat comparisons as IT projects requiring dashboard multiplication and manual correlation. Scoop treats them as conversations, generating comparisons on demand. For strategic planning teams, this means exploring 10x more scenarios in the same time. The business impact is clear: faster decisions, better strategies, and executives who actually use the analytics platform instead of defaulting to Excel.</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 three months before it happens. Your inventory levels follow seasonal rhythms that could save millions in carrying costs. These insights exist in your data right now—but finding them shouldn't require a data science degree. Modern platforms promise 'AI-powered insights,' but there's a massive gap between having ML capabilities and making them usable by business teams. Let's examine how each platform handles the critical challenge of democratizing advanced analytics.</p>
<p class="content-section__paragraph">The fundamental divide in ML analytics isn't about algorithms—it's about accessibility. Domo's AutoML requires separate licensing and weeks of model training setup. You define features, select algorithms, tune parameters. Business users wait for IT. Sisense embeds basic ML but locks it behind their dashboard paradigm. Want to investigate an anomaly? Build a new widget. Configure thresholds. Deploy to users. Scoop takes a radically different approach: ML runs automatically on every question. Ask 'What's unusual about last month's sales?' and get anomalies highlighted. No configuration. No waiting. The key innovation isn't the ML itself—it's removing the barrier between business questions and advanced analytics. When a marketing manager can type 'What predicts customer churn?' and get a ranked list of factors in seconds, that's democratization. Traditional platforms treat ML as a separate technical layer requiring specialized skills. Scoop treats it as a natural part of conversation. This architectural difference means insights surface during normal work, not through separate 'advanced analytics' projects that take months and often miss the mark because business context gets lost in translation.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory costs climbing. With Scoop, she types: 'What's driving the increase in inventory costs?' Scoop automatically runs correlation analysis across 50+ variables, identifies that slow-moving items in Category B increased 40%, and highlights that this correlates with a supplier change three months ago. Total time: 90 seconds. She follows up: 'Predict inventory costs for next quarter if we revert suppliers.' Scoop generates a forecast showing $2.3M savings. With Domo, this requires building an AutoML project—defining features, training models, waiting days for results. The business moment passes. Sisense would need custom Python scripts integrated through their data model. By the time IT delivers the analysis, the manager has already made decisions based on gut instinct rather than data.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI platforms splits into two camps: those that make you work for insights (configure, train, deploy) and those that surface them naturally. Domo and Sisense have capable ML—locked behind technical barriers that ensure 95% of business users never touch it. Scoop's approach—automatic ML on every question—means pattern discovery happens during normal business conversations, not special projects.</p>
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<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 when teams spot anomalies. 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 Domo, Sisense, and Scoop handle this critical requirement, focusing on what users can actually do versus what requires IT configuration.</p>
<p class="content-section__paragraph">The workflow integration gap reveals a fundamental architecture problem. Domo and Sisense built dashboard platforms first, then bolted on integrations. Their mobile apps are dashboard viewers, not investigation tools. You can see that revenue dropped, but you can't ask why. Their Excel integrations are one-way streets—export data out or import it in, but no live analysis. Scoop's chat-first architecture changes everything. The same conversation that works on desktop works identically in Excel, Slack, or mobile. A CFO can start investigating margins in Excel, continue on her phone during lunch, and finish in Slack with her team. No retraining, no feature gaps. The investigation capability travels with you. Domo's mobile app scores 2/8 on investigation capability—you're viewing static dashboards, not exploring data. Sisense barely improves at 3/8 with basic drill-downs. Scoop maintains full 8/8 investigation power everywhere. This isn't about having more features. It's about maintaining analytical capability across contexts. When 73% of executives check data on mobile devices weekly, view-only dashboards aren't enough.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A regional sales director notices unusual patterns during her morning Excel review. She types into Scoop's Excel sidebar: 'Why are Southeast region renewals down 20%?' Scoop investigates automatically, surfacing that three major accounts switched to month-to-month contracts. She screenshots the analysis to Slack, where her team continues the investigation: 'Which accounts switched and when?' The answers appear directly in Slack—no platform switching, no dashboard building. Later, on her phone at the airport, she asks: 'What would revenue impact be if we offered annual discounts?' Total investigation time: 15 minutes across three platforms. With Domo, she'd export data from a dashboard, analyze in Excel, share static screenshots to Slack, and lose investigation context on mobile. The same investigation would take 2+ hours and involve IT for custom dashboard modifications.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about feature checkboxes—it's about maintaining analytical power wherever work happens. While Domo and Sisense offer basic exports and view-only mobile apps, Scoop delivers full investigation capability across every platform. Business users can start questions in Excel and finish them in Slack without losing context or capability. For organizations where data-driven decisions happen everywhere, not just at desks, this architectural advantage transforms productivity.</p>
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</section>
<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 Sisense 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">Which is better for business users: Domo or Sisense?</h3>
<p class="content-section__paragraph">Domo scores 62/100 on business autonomy versus Sisense's 28/100, making Domo significantly better. However, both require IT support for complex queries. Scoop at 82/100 lets business users work independently. Domo needs semantic layer setup, Sisense requires SQL knowledge. Neither matches Scoop's natural language approach.</p>
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<div class="content-section__subsection">
<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 investigation begins. You click through pre-built paths IT created. For new questions, submit tickets for dashboard modifications. Scoop automatically runs 3-10 queries to find root causes, testing hypotheses like a human analyst would. No pre-building required.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Sisense do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Sisense requires manual SQL queries or pre-built dashboards for root cause analysis. Its 28/100 BUA score reflects heavy IT dependency. Users navigate fixed drill-paths without ability to ask why. Scoop automatically chains queries, testing multiple hypotheses to uncover root causes business users couldn't find alone.</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 per question. It tests hypotheses, eliminates false leads, and surfaces insights dashboard tools miss. Unlike Domo's single-query widgets or Sisense's fixed drill-downs, Scoop thinks through problems like an analyst, asking follow-up questions automatically.</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 includes licenses, 6-month implementation, training programs, semantic layer maintenance, consultants, and productivity loss during ramp-up. Organizations typically spend 3-5x the license fee annually. Scoop eliminates implementation, training, and consultant costs entirely, reducing total ownership cost by approximately 90 percent.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Are there hidden fees with Sisense?</h3>
<p class="content-section__paragraph">Yes, Sisense's hidden costs include mandatory professional services, data modeling consultants, ongoing semantic layer maintenance, and specialized training. The 28/100 BUA score means heavy IT support costs. Most organizations spend 4-6x the license fee annually. Scoop has no hidden fees—just a transparent subscription.</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, months for advanced features. Their certification program has multiple levels. Business users still need IT help for complex queries despite 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">Do I need SQL knowledge for Sisense?</h3>
<p class="content-section__paragraph">Yes, Sisense requires SQL for anything beyond basic pre-built reports. Its 28/100 BUA score reflects this technical barrier. Business users hit walls quickly without SQL skills. Scoop translates plain English to complex SQL automatically, letting business users analyze data as easily as asking ChatGPT questions.</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, Scoop's 82/100 BUA score means true business user independence. Connect in 30 seconds, ask questions immediately, get answers with charts. No semantic layers, no SQL, no dashboard requests. Unlike Domo (62/100) or Sisense (28/100), business users work completely autonomously from day one.</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 Sisense require building dashboards before getting answers. Scoop answers questions directly through conversation, running multiple queries automatically. It's the difference between hiring an analyst versus learning dashboard software.</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 export static snapshots that become outdated immediately. Refreshing requires returning to Domo's portal. Scoop works directly inside Excel, letting users ask questions without leaving spreadsheets. Real-time analysis where business users actually work, not another portal.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can I use Sisense directly in Slack?</h3>
<p class="content-section__paragraph">Sisense offers basic Slack notifications but no real analysis capability. Users receive alerts then must switch to Sisense's portal for investigation. Scoop runs natively in Slack—ask questions, get answers with charts, share insights, all without leaving conversations. True workflow integration versus notification theater.</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, no technical knowledge needed. Ask questions as you'd ask a colleague. Domo and Sisense require learning their interfaces, data models, and query languages. With Scoop, if you can write an email, you can analyze data professionally.</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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</section>
