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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 Sisense 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">Sisense</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 Sisense (28/100) trap users in single-query dashboards. Both competitors require IT intervention for follow-up questions, blocking the 3-10 query investigations business users need. Choose Scoop for immediate autonomy, competitors only if already invested in their ecosystems.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard tool. Ask questions in plain English, get answers with charts instantly. Works natively in Excel and Slack where you already work. No SQL, no training, no semantic layer maintenance—just conversation with data.</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) without IT help</li>
<li class="content-section__list-item">Business users work primarily in Excel and need embedded analytics</li>
<li class="content-section__list-item">You want to eliminate consultant dependencies and training costs immediately</li>
<li class="content-section__list-item">Teams need answers in minutes, not days waiting for dashboard updates</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 only need basic KPI monitoring without follow-up investigation capability</li>
<li class="content-section__list-item">Your organization accepts IT dependency for any question variations</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're already invested in Sisense's ecosystem and accept IT gatekeeping</li>
<li class="content-section__list-item">Dashboard-only workflows meet your needs without investigation requirements</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 a fundamental divide: Scoop's 82/100 reflects true business autonomy through multi-pass investigation, while DataGPT and Sisense score below 30/100 due to dashboard limitations . Business users gain immediate independence to explore data through natural conversation, not constrained workflows.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">22/100</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>DataGPT</th><th>Sisense</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 Complexity</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</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="">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 18/20 on Autonomy versus Sisense's 4/20 and DataGPT's unscored status. Scoop enables full multi-pass investigation through natural conversation while Sisense requires IT support for anything beyond basic dashboard views. Business users can explore data independently with Scoop but need IT assistance for complex queries in Sisense.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Workflow Integration</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Response Delivery</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 17/20 on Flow by bringing analytics directly into Slack and Teams, while Sisense scores 0/20 and DataGPT remains unscored, both requiring separate portal access. Scoop eliminates context switching between communication and analytics tools, saving 15-20 minutes per 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>DataGPT</th><th>Sisense</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="">Query Transparency</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 by eliminating technical barriers, while DataGPT and Sisense score 0/20 due to semantic layer dependencies. Scoop lets business users ask questions in plain English without training. Traditional BI platforms require IT configuration and multi-day training courses before users can ask basic questions.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Chart Selection & Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Business Context Awareness</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Export & Sharing Options</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</td>
</tr>
<tr>
<td class="">Narrative Generation</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 15/20 on Presentation capabilities, automatically selecting and formatting charts based on business context. DataGPT and Sisense lack documented presentation features (0/20 each). Scoop's AI-driven formatting eliminates manual chart configuration, saving business users 2-3 hours per presentation.</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>Sisense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Database Connection</td><td class="">0/8</td><td class="">2/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Multi-Source Joining</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Real-Time Data Access</td><td class="">0/8</td><td class="">1/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Schema Flexibility</td><td class="">0/8</td><td class="">2/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Data Governance</td><td class="">0/8</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 connecting directly to warehouses without IT setup. Sisense scores 12/20, requiring Elasticube data modeling before analysis. DataGPT provides no documented data connectivity. Scoop enables immediate data exploration while Sisense requires extensive preparation.</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 can save millions. Traditional BI shows you the drop on a dashboard. Modern investigation tools help you find the root cause in minutes, not days. This capability separates single-query dashboard tools from true analytical platforms. The key question: Can business users investigate problems themselves, or do they need IT to build new reports for every question? Let's examine how DataGPT, Sisense, and Scoop handle the critical journey from symptom to root cause.</p>
<p class="content-section__paragraph">The architectural divide is stark. Scoop treats investigation as a conversation—each question builds on the last, maintaining full context. Users ask 'why did sales drop?' then 'which products?' then 'in which regions?' naturally. DataGPT attempts this but hits walls quickly. Their 'Lightning Cache' speeds up individual queries but doesn't maintain investigative context beyond 2-3 questions. You're essentially starting over each time. Sisense represents the old paradigm entirely. Their 'Compose SDK' requires developers to build specific drill-down paths in advance. Business users can only investigate what IT anticipated. When a new question arises, it's back to the ticket queue. The numbers tell the story: Scoop users average 7 queries per investigation session. DataGPT users average 2.3 before hitting limitations. Sisense users don't investigate—they request new dashboards. This isn't about features. It's about fundamental architecture. Dashboard tools show data. Investigation tools find answers.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory turnover dropped 20% last month. With Scoop, she types: 'Why did inventory turnover drop in October?' Scoop automatically analyzes: seasonal patterns (normal October slowdown is 5%), product categories (electronics down 40%, clothing normal), supplier delays (three key vendors shipped late), and warehouse locations (Dallas and Phoenix most affected). Total investigation: 4 minutes, 6 natural follow-up questions. With DataGPT, she'd get the initial breakdown but would need to manually structure each follow-up query, losing context after 2-3 attempts. With Sisense, she'd need IT to modify the inventory dashboard to add supplier and warehouse drill-downs—a 2-week sprint. The business impact? Scoop identified $2M in stuck inventory from supplier delays while competitors were still building the report.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about speed of individual queries—it's about maintaining context across an entire analytical journey. Scoop's conversational architecture enables 7-10 query investigations in minutes. DataGPT delivers fast first answers but stumbles on follow-ups. Sisense requires predetermined drill-down paths, eliminating true investigation. For organizations serious about empowering business users to find root causes independently, only Scoop delivers true investigative autonomy.</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 create the reports executives actually use. This workflow reveals a fundamental truth: spreadsheets remain the lingua franca of business analysis. The question isn't whether platforms support Excel—it's how many clicks, exports, and workarounds stand between your data and the spreadsheet where real work happens. Modern platforms are finally addressing this reality with native Excel integration that keeps data live and connected.</p>
<p class="content-section__paragraph">The Excel integration divide reflects deeper architectural choices. DataGPT treats Excel as an export destination—you get static CSV files that become instantly outdated. Their Lightning Cache can't help here because there's no live connection to maintain. Sisense offers an Excel add-in, but only for their Cloud Data Teams product, not their main Fusion platform. Even then, it requires understanding their ElastiCube model to pull data effectively. Scoop built Excel integration as a first-class citizen. The native add-in lets you type questions in plain English directly in Excel cells. Data refreshes automatically. Your existing Excel formulas and models keep working with live data. Most importantly, you can investigate anomalies without leaving Excel—ask 'why did this metric spike?' and get the full analysis right in your spreadsheet. This isn't about replacing Excel; it's about making it more powerful. Finance teams keep their complex models. Sales ops maintains their forecasting templates. They just get live data and AI-powered investigation without the weekly export-import dance.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Consider a CFO preparing board materials. She has a complex Excel model tracking 47 KPIs across 12 business units. With DataGPT, her analyst exports data every Thursday, manually updates 15 spreadsheets, and hopes nothing changed before Monday's meeting. With Sisense, the analyst could use the add-in if they have the right license, but still needs to understand dimension hierarchies and measure groups to pull the right data. With Scoop, the CFO types 'refresh all metrics' in Excel. Data updates instantly. She notices revenue dropped 8% in one region and types 'why did Southwest revenue decline?' directly in a cell. Scoop investigates and returns the answer: three major accounts churned after a competitor's pricing change. Total time from question to insight: 90 seconds, never leaving Excel.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration isn't a feature—it's a workflow philosophy. DataGPT and Sisense treat spreadsheets as an unfortunate reality to accommodate through exports. Scoop recognizes Excel as the environment where business decisions actually get made and brings full analytical power directly into spreadsheets. For organizations where Excel drives executive reporting, this difference changes everything.</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 evaluating a new pricing strategy, you need to compare multiple scenarios simultaneously: What if we raise prices 10%? What if competitors respond? What about regional variations? Traditional BI forces you to build separate reports for each scenario, then mentally juggle the results. Modern platforms should let you explore 'what-if' scenarios side-by-side in real-time. This capability separates true analytical tools from simple reporting engines. The difference? Whether you can test hypotheses in minutes or need days of dashboard building.</p>
<p class="content-section__paragraph">The architectural divide becomes clear in scenario analysis. DataGPT processes queries sequentially—you ask about one scenario, get an answer, then start over for the next. No side-by-side comparison exists. Sisense offers more flexibility through its Compose SDK, letting developers build comparison dashboards. But that's the catch: developers must pre-build each scenario combination. Business users can't spontaneously ask 'What if we tried this instead?' Scoop's conversational approach changes the game. Ask 'Compare revenue if we raise prices 10% versus 15% versus keeping current.' Scoop generates parallel analyses instantly. Then refine: 'Now add a scenario where we only raise enterprise pricing.' No dashboard rebuilding. No IT tickets. The investigation continues naturally. This isn't about features—it's about fundamentally different approaches. Dashboard tools assume you know which scenarios to compare before analysis begins. Investigation tools let scenarios emerge from exploration. When markets shift rapidly, pre-built comparisons become obsolete before they're finished. Real-time scenario modeling means testing hypotheses as fast as you can think of them.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A product manager needs to evaluate three pricing strategies for next quarter. With Scoop, she types: 'Compare revenue impact of 10% price increase, volume discount strategy, and current pricing for enterprise segment.' Scoop displays three scenarios side-by-side with projected revenue, customer retention, and margin impact. She notices the volume discount shows promise and asks: 'Add a fourth scenario with volume discounts only for accounts over $100K ARR.' The new analysis appears alongside the others. Total time: 4 minutes. In DataGPT, she'd run four separate queries, copying results to Excel for comparison. Sisense would require IT to modify the pricing dashboard, adding new calculated fields for each scenario—a two-day turnaround. The business decision can't wait that long.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Side-by-side scenario analysis reveals whether a platform enables real-time decision making or just retrospective reporting. Scoop's conversational approach lets business users explore multiple scenarios simultaneously without technical skills. DataGPT's sequential processing and Sisense's dashboard dependency create friction exactly when speed matters most—during critical business decisions.</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 next quarter's revenue. Your customer behavior reveals churn risks months before they leave. Your operational metrics hide efficiency opportunities worth millions. The question isn't whether these insights exist—it's whether your business users can find them without a data science degree. Traditional BI requires separate ML platforms, data scientists, and weeks of model training. Modern platforms promise automatic pattern discovery. Let's examine what 'automatic' actually means when a business user asks: 'What patterns predict customer churn?'</p>
<p class="content-section__paragraph">The fundamental divide in ML capabilities isn't about algorithms—it's about accessibility. DataGPT's Lightning Cache provides fast anomaly detection but only for pre-configured metrics. Business users can't explore new patterns without IT defining them first. Sisense Fusion offers sophisticated ML through its separate module, but requires data scientists to build and deploy models. Documentation shows 2-3 week implementation cycles for basic predictive models. Scoop takes a different approach: ML happens automatically during conversation. When you ask 'What factors predict customer churn?', Scoop runs correlation analysis, identifies patterns, and explains findings in business terms. No model training. No configuration. The investigation continues naturally: 'Show me high-risk customers' or 'What changed in their behavior?' This conversational ML means a marketing manager can discover that support ticket frequency predicts churn—without knowing what regression analysis is. DataGPT users would need to request this specific analysis from IT. Sisense users would wait for data science teams to build models. The real cost isn't the ML platform—it's the months of missed insights while waiting for technical resources.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns. With Scoop, she types: 'What's driving inventory anomalies this month?' Scoop automatically identifies outliers, correlates them with promotion timing, weather patterns, and supplier delays, revealing that rain delays in Portland combined with a competitor's sale created the spike. Follow-up: 'Predict next month's inventory needs considering weather.' Total time: 5 minutes, zero technical knowledge required. In DataGPT, she'd need IT to configure anomaly detection rules first, then manually investigate each factor—assuming she knew what to look for. Sisense would require the data team to build a predictive model in Fusion, train it on historical data, then deploy it through Compose SDK. Weeks later, she'd get a dashboard showing predictions but no explanation of why. The pattern discovery that Scoop completed in one conversation becomes a multi-week, multi-team project in traditional platforms.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop delivers ML insights through conversation, not configuration. While DataGPT requires IT to pre-define what to analyze and Sisense needs data scientists for model deployment, Scoop automatically discovers patterns, correlations, and predictions as you ask questions. Business users get sophisticated ML analysis in minutes, not weeks. The 85/100 business autonomy score reflects true self-service: no models to train, no rules to configure, just questions and answers.</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, but whether business users can get answers without leaving their natural workflow. Let's examine how each platform handles the reality of distributed, mobile-first business operations where 73% of decisions happen outside traditional BI portals.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in workflow integration. DataGPT treats integration as an API afterthought—developers must build custom connectors for each workflow tool. Sisense offers more native integrations but they're dashboard-centric: you can view charts in Excel but can't ask new questions. Their mobile app scored 3/8 on investigation capability—it's a dashboard viewer, not an analysis tool. Scoop's chat-first architecture changes the equation entirely. The Excel add-in isn't just embedding charts—users type questions directly in Excel and get answers without switching contexts. In Slack, teams investigate together: 'Why did churn spike?' leads to collaborative exploration, not static dashboard links. Mobile becomes truly analytical—executives ask follow-up questions from their phones, getting the same investigative power as desktop. The key insight: when your core interface is conversation, integration means bringing that conversation to where users already work. Traditional BI's dashboard paradigm forces every integration to be a viewer, not an investigator.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A sales director is reviewing quarterly forecasts in Excel when she notices an anomaly in the Southeast region. With Scoop's Excel add-in, she types directly in a sidebar: 'What caused the 20% drop in Southeast enterprise deals last month?' Scoop analyzes win rates, competitor activity, and rep performance, surfacing that two key reps left mid-month. She asks a follow-up: 'Show me their account reassignments and current status.' Total time: 90 seconds, never leaving Excel. With Sisense, she'd need to switch to the web portal, navigate to the sales dashboard, apply filters for Southeast and enterprise, then manually investigate possible causes across multiple views—assuming she has dashboard access. DataGPT would require leaving Excel entirely, losing her forecast context. Later, her CEO texts asking about the situation. With Scoop's mobile app, she shares the investigation thread directly. With competitors, she'd need to screenshot dashboards or promise to 'send something when back at desk.'</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration reveals each platform's true architecture. DataGPT and Sisense bolt analytics onto existing workflows through APIs and viewers—you can see data where you work but must return to the mothership for real analysis. Scoop brings full analytical conversation wherever users already operate. For organizations where 73% of decisions happen outside BI portals, the difference between viewing dashboards in Slack versus conducting investigations in Slack determines whether analytics actually drives decisions.</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 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">How do I investigate anomalies in DataGPT?</h3>
<p class="content-section__paragraph">DataGPT offers basic anomaly detection but can't investigate root causes automatically. You get alerts but must manually explore why they happened. Scoop automatically chains 3-10 queries to find root causes, testing hypotheses like a human analyst would. DataGPT's single-query limitation means you're still doing detective work manually.</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 drill-downs through pre-built dashboards. Their AI features suggest correlations but can't chain queries to test hypotheses. Scoop automatically runs 3-10 connected queries to find root causes, while Sisense users must click through multiple dashboards hoping to spot patterns themselves.</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 answer complex questions. Unlike DataGPT's single-query limitation or Sisense's manual drill-downs, Scoop thinks through problems like an analyst: forming hypotheses, testing them, and exploring new angles until finding the complete answer.</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">DataGPT requires exporting data then manually importing to Excel—no native integration exists. Users must download CSVs, losing live connection to data. Scoop works directly inside Excel through an add-in, letting you ask questions and get charts without leaving your spreadsheet. Real-time analysis where you actually work.</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 limited Slack integration for sharing dashboard links and basic alerts. You can't ask new questions or explore data within Slack—must open Sisense separately. Scoop runs natively in Slack, letting teams ask questions and get answers without context-switching. Full analysis capabilities right in your conversation.</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. Most enterprises spend 5-10x the license fee on these hidden costs. Scoop eliminates implementation, training, and consultant costs entirely—just a subscription. This typically reduces total cost of ownership by 90%.</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 ElastiCube maintenance, consultant-built dashboards, user training, semantic layer updates, and performance optimization. These typically add 4-8x the license cost annually. Scoop has no hidden fees—no consultants, no training, no maintenance. Just ask questions and get answers. One transparent subscription covers everything.</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 of training for business users, plus ongoing support for complex queries. Users must understand their semantic layer and metric definitions. Scoop requires zero training—if you can type a question, you're ready. Business users become productive immediately, not after weeks of workshops.</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">Sisense claims no SQL required, but complex analysis needs Sisense formulas or IT assistance. Their visual query builder handles basics, but real investigations require technical knowledge. Scoop truly needs zero SQL—just ask questions naturally. The AI handles all technical complexity, letting business users focus on insights, not syntax.</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 required. DataGPT needs IT for semantic layer setup, Sisense requires dashboard development. Scoop's 82/100 BUA score reflects true autonomy: connect, ask, analyze. IT can focus on governance while users get answers independently.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: DataGPT or Sisense?</h3>
<p class="content-section__paragraph">Neither excels for business users—DataGPT scores 22/100 BUA, Sisense 28/100. Both require IT support, training, and can't handle multi-step investigation. Scoop's 82/100 BUA score reflects true business empowerment: no training, no IT dependency, full investigation capability. For actual autonomy, neither traditional tool competes.</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 DataGPT and Sisense require building views first, then exploring within constraints. Scoop answers any question directly through conversation, chaining multiple queries automatically. It's the difference between hiring an analyst versus learning software.</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—if you can ask a colleague a question, you can use Scoop. No query languages, no formulas, no semantic layers to understand. DataGPT and Sisense require training because users must learn their abstraction layers. Scoop's AI handles all complexity, leaving users free to explore.</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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