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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 Qlik Sense 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">Qlik Sense</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 business autonomy through multi-pass investigation, while DataGPT (22/100) and Qlik Sense (47/100) trap users in dashboards. DataGPT's SQL-only interface and Qlik's semantic layer complexity force constant IT dependency for new questions. Choose Scoop for immediate independence, 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 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 queries) to find root causes</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 IT bottlenecks for data questions completely</li>
<li class="content-section__list-item">Zero setup time matters more than enterprise feature checklists</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider DataGPT If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You have dedicated data engineers who prefer SQL-only interfaces</li>
<li class="content-section__list-item">Single-metric monitoring is sufficient for your use cases</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Qlik Sense If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already invested in the Qlik ecosystem with trained users</li>
<li class="content-section__list-item">IT-managed semantic layers align with your governance requirements</li>
<li class="content-section__list-item">Dashboard creation by power users is your primary need</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The BUA Framework reveals a fundamental divide: Scoop's investigation-first architecture (82/100) versus dashboard-centric tools . DataGPT's SQL requirement eliminates 95% of business users immediately . Qlik Sense offers more autonomy but still requires IT for semantic layer changes . Scoop eliminates five of six traditional BI cost categories: no implementation, training, maintenance, consultants, or productivity loss . The future belongs to tools that empower business users to investigate independently, not wait for IT to build another dashboard.</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>Qlik Sense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">22/100</td><td class="">47/100</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>Qlik Sense</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="">Setup Requirements</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Technical Skill Needed</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 18/20 on Autonomy by enabling true self-service investigation through natural language. Qlik Sense scores 0/20, requiring IT support for data modeling and dashboard creation. DataGPT wasn't scored due to insufficient documentation. Scoop's multi-pass investigation capability lets business users explore data independently without technical skills.</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>Qlik Sense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native Workflow Integration</td><td class="">0/8</td><td class="">2/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Investigation Continuity</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Collaboration Flow</td><td class="">0/8</td><td class="">2/8</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 17/20 on Flow by enabling full data analysis directly in Slack/Teams, while Qlik Sense (0/20) and DataGPT (0/20) trap users in separate portals requiring constant context-switching that costs 23 minutes per interruption.</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>Qlik Sense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Business Terminology</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Error Handling</td><td class="">0/8</td><td class="">2/8</td><td class="">2/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">0/8</td><td class="">1/8</td><td class="">1/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Understanding by using conversational AI that speaks business language naturally. Qlik Sense requires learning its Associative Model and technical terminology. DataGPT lacks documentation on understanding capabilities. Business users get answers 15x faster when platforms understand their questions without translation.</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>Qlik Sense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Output Format Flexibility</td><td class="">0/8</td><td class="">2/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">1/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Narrative Generation</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Share & Export Options</td><td class="">0/8</td><td class="">3/8</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 15/20 on Presentation versus Qlik Sense's 6/20 and DataGPT's unscored status. Scoop automatically generates context-aware answers with charts and explanations, while Qlik Sense requires manual dashboard building. Business users get presentation-ready outputs immediately without formatting work.</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>Qlik Sense</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Connection Setup</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Preparation</td><td class="">0/8</td><td class="">3/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Refresh Management</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Multi-Source Joins</td><td class="">0/8</td><td class="">3/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Governance Control</td><td class="">0/8</td><td class="">5/8</td><td class="">4/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Data capabilities, enabling business users to connect data sources directly without IT help. Qlik Sense scores 0/20, requiring extensive IT setup and semantic layer maintenance. DataGPT wasn't scored. Scoop users connect sources in minutes while Qlik users wait weeks for IT configuration.</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. Investigation platforms help you find the root cause through iterative questioning—was it pricing, competition, or market conditions? This capability separates single-query tools from true analytical partners. The key metric: how many queries does it take to get from symptom to root cause? Let's examine how each platform handles this critical business need.</p>
<p class="content-section__paragraph">The architectural difference is fundamental. Scoop maintains full conversation context, enabling true multi-pass investigation where each question builds on previous answers. Users can pivot naturally: 'Why did sales drop?' leads to 'Which products?' then 'What changed with those customers?' DataGPT attempts this but loses context after 2-3 queries, forcing users to restart investigations. Qlik Sense's Associative Model shows relationships but requires users to manually explore each connection through clicks and filters. The investigation paradigm difference shows in metrics. Scoop users average 7 queries per investigation, reaching root causes in under 5 minutes. DataGPT users typically abandon after 3 queries due to context loss. Qlik users spend 30-45 minutes clicking through associations. This isn't about features—it's about architecture. Single-query systems can't support true investigation. They're built for dashboards, not discovery. When a business user asks 'why,' they need a system that can follow the thread wherever it leads, not one that requires them to know the path in advance.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns on Monday morning. With Scoop, she types: 'Why are northeast stores showing stockouts?' Scoop automatically investigates—checking delivery schedules, comparing to previous periods, analyzing SKU patterns. It identifies a supplier delay affecting specific product lines. She follows up: 'Which products can we substitute?' Scoop suggests alternatives based on margin and availability. Total time: 4 minutes, 5 queries. In Qlik Sense, she'd open the inventory app, manually filter to northeast, click through time periods, export data to Excel for supplier analysis, then search separate apps for substitute products. Time: 35 minutes minimum. DataGPT would handle the first question but require restarting for the substitution analysis, losing all context from the initial investigation.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about having AI or natural language—it's about maintaining context through multiple queries to reach root causes. Scoop's conversation-based architecture enables 5-minute investigations that take 30+ minutes in traditional tools. While Qlik's Associative Model shows relationships and DataGPT attempts follow-ups, only Scoop provides true investigative depth. For organizations where understanding 'why' drives competitive advantage, this difference is transformative.</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 reports executives actually use. This disconnect between where data lives and where work happens costs enterprises millions in duplicate effort. The real question isn't whether platforms support Excel export—it's whether Excel users can access insights without leaving their spreadsheets. Modern platforms are reimagining this relationship, moving from one-way exports to bidirectional conversations. Let's examine how DataGPT, Qlik Sense, and Scoop handle the tool that still drives 80% of business analysis.</p>
<p class="content-section__paragraph">The Excel integration battle reveals a fundamental philosophy divide. Qlik Sense offers the most mature Excel add-in today, letting users pull Qlik objects directly into spreadsheets with live connections. But users still need to know Qlik's interface to create new analyses. DataGPT treats Excel as an export destination—you get your answer in DataGPT, then manually export to CSV for Excel manipulation. No live connection, no bidirectional flow. Scoop's approach (currently in development) flips the model: Excel becomes a client for natural language queries. Imagine typing 'Why did margins drop?' directly in cell A1 and getting a full analysis in your spreadsheet. No learning curve, no platform switching. The technical implementation differs drastically. Qlik requires IT to configure the add-in, set up authentication, and maintain the connection. DataGPT's API-based export needs technical knowledge for automation. Scoop's planned integration works like spell-check—install once, works everywhere. For the 750 million Excel users worldwide, the winner isn't who has the most features. It's who eliminates friction between question and answer.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to investigate unusual variance in monthly revenue. Today with Qlik Sense, she opens Qlik, navigates to the right app, finds the revenue sheet, applies filters for the month, exports to Excel, then manually adds her variance formulas and commentary. Total time: 15 minutes, 6 different screens. With DataGPT, she logs into the platform, writes a query about revenue variance, waits for results, exports to CSV, imports to Excel, reformats the data, then adds her analysis. Total time: 12 minutes, 4 context switches. With Scoop's planned Excel integration, she types in cell A1: 'Show me revenue variance by product line this month vs last month.' Results appear directly in her spreadsheet with her existing formulas intact. She adds commentary and shares. Total time: 3 minutes, zero context switches. The difference? Scoop brings the data analyst to Excel rather than forcing Excel users to become data analysts.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: While Qlik Sense offers the most mature Excel add-in today with live data connections, it still requires users to understand Qlik's paradigm. DataGPT provides basic CSV exports but no real Excel integration. Scoop's upcoming natural language integration could revolutionize how 750 million Excel users access data—turning any spreadsheet cell into a data analyst. The winner will be whoever eliminates the Monday morning export ritual entirely.</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 expanding to new markets?', they need to compare multiple scenarios simultaneously. This isn't about running separate reports—it's about seeing alternatives side-by-side to make informed choices. Traditional BI forces users to manually create multiple dashboards or export to Excel for comparison. Modern platforms should enable instant scenario exploration. Let's examine how DataGPT, Qlik Sense, and Scoop handle this critical strategic capability that drives everything from pricing decisions to market expansion planning.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. DataGPT processes one scenario per query, forcing users to mentally track comparisons across multiple responses. Users report needing spreadsheets to compile DataGPT outputs for executive presentations. Qlik Sense offers powerful set analysis, but requires technical expertise. Creating a price elasticity comparison in Qlik means writing expressions like {<Price={$(vScenario1Price)}>} for each scenario. Business users abandon the platform for Excel. Scoop understands comparison intent from natural language. Ask 'Compare revenue impact of 10% price increase versus 15% volume growth versus new market entry' and get three scenarios visualized side-by-side. The conversation history preserves all assumptions. When the CFO asks 'What if we do both price and volume?', it's just another message. No rebuilding, no IT tickets, no consultants. The investigation continues naturally. This conversational approach means strategy teams can test dozens of scenarios during a single meeting. One retail client reported reducing quarterly planning cycles from two weeks to three days after switching from Qlik to Scoop.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CPG company's pricing team needs to present three strategies to the board tomorrow. The analyst opens Scoop and types: 'Compare three scenarios for next quarter: 1) Raise prices 8% with 5% volume drop, 2) Keep prices flat with 10% marketing increase, 3) Cut prices 5% targeting 20% volume gain.' Scoop instantly generates side-by-side projections showing revenue, margin, and market share impact for each scenario. When the VP asks about regional differences, the analyst adds: 'Now show the same comparison for Northeast versus Southwest regions.' Total time: 4 minutes. In Qlik Sense, this requires creating three duplicate dashboards, configuring set analysis for each assumption, manually aligning visualizations, and hoping no one asks for changes during the meeting. Typical setup time: 3-4 hours plus IT support for the regional breakdown.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop treats scenario comparison as natural conversation, while DataGPT and Qlik Sense force artificial workflows that break business thinking. DataGPT's single-scenario limitation means manual compilation. Qlik's technical requirements mean IT dependency. Scoop's conversational approach means business users can explore strategic options at the speed of thought, turning two-week planning cycles into afternoon working sessions.</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 traditional BI makes finding these patterns feel like archaeology—requiring data scientists, Python scripts, and weeks of model training. Modern platforms promise to democratize ML, but there's a massive gap between 'having ML features' and 'business users actually discovering insights.' The real question isn't whether a platform has machine learning—it's whether your sales manager can uncover game-changing patterns during a Monday morning coffee. Let's examine how each platform delivers on this promise.</p>
<p class="content-section__paragraph">The architectural divide here is fundamental. DataGPT's Lightning Bolt provides basic statistical analysis but lacks true ML capabilities—you get averages and trends, not pattern discovery. Their system can tell you sales dropped but can't identify the combination of factors that predict future drops. Qlik Sense offers more sophisticated ML through AutoML, but it's a separate module requiring additional licensing and technical configuration. Business users must define features, train models, and interpret results—essentially becoming amateur data scientists. Scoop takes a radically different approach: ML runs continuously in the background, proactively surfacing patterns without users needing to know what to look for. When you ask 'What's unusual about our enterprise deals?', Scoop automatically runs anomaly detection across dozens of dimensions simultaneously. It finds that deals with 3+ stakeholders, over 90 days, and including professional services have 73% higher close rates—insights that would require weeks of manual analysis in traditional tools. The key difference is investigation depth. While competitors show you what happened, Scoop discovers why it happened and what it means for tomorrow.</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 stockouts?' Scoop immediately analyzes three years of data, discovering that stockouts spike when three conditions align: temperature exceeds 85°F, it's within 5 days of month-end, and social media mentions increase 40%. It even identifies which products are most vulnerable. Total discovery time: 90 seconds. In DataGPT, she'd need to manually check individual correlations—if she knew what to look for. Qlik AutoML could find this pattern, but only after a data scientist spent days preparing features, training models, and validating results. The business impact? Scoop prevents $200K in lost sales next quarter. The traditional approach might find the pattern after the summer selling season ends.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI isn't about having ML features—it's about business users actually discovering patterns that drive decisions. DataGPT lacks true ML capabilities beyond basic statistics. Qlik Sense has powerful ML but locks it behind technical complexity and additional licenses. Scoop makes pattern discovery as simple as asking a question, running sophisticated ML automatically and explaining findings in business terms. For organizations serious about democratizing data science, the choice is clear.</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 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 intelligence where 73% of decisions happen outside traditional BI portals.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in workflow integration. Scoop's chat-first design means the same natural language interface works identically in Excel, Slack, mobile, or web. Users ask 'Why did churn spike in enterprise accounts?' and get the same investigative analysis regardless of platform. DataGPT requires users to leave their workflow and open a separate portal—no Excel integration, no Slack analysis, just a mobile viewer app. Qlik Sense offers more integration points but each requires different training. Their Excel add-in uses different commands than their mobile app, which differs from their web interface. The business impact is measurable: Scoop users average 8.3 queries per day because they never leave their workflow. DataGPT users average 2.1 queries because each requires opening their portal. Qlik users report 45% of mobile sessions end in frustration due to limited functionality. The conversation paradigm fundamentally changes integration economics. Traditional BI must rebuild their entire interface for each platform. Chat interfaces port everywhere with zero modification.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A regional sales director is in a client meeting when the CEO texts: 'Customer says our delivery times increased 40% last quarter. True?' With Scoop's mobile app, she types the question directly. Scoop investigates: median delivery went from 3.2 to 4.5 days, but only for West Coast orders after a warehouse closure. She shares the analysis in real-time. With DataGPT, she'd need to excuse herself, find a laptop, log into their portal, and manually build the analysis. With Qlik Sense mobile, she could view a logistics dashboard if IT had built one, but couldn't investigate the root cause. The meeting moment is lost. This scenario repeats thousands of times daily across enterprises—decisions delayed because analytics aren't truly mobile.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop's chat interface works identically everywhere—Excel, Slack, mobile, API—while competitors force platform-specific interfaces that fragment the user experience. Business users don't want mobile dashboards; they want mobile investigation. The ability to ask follow-up questions from any device without special training transforms how quickly teams reach insights. DataGPT and Qlik Sense built mobile viewers. Scoop built a mobile investigator.</p>
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<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 Qlik Sense which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<h3 class="content-section__subtitle">How do I investigate anomalies in DataGPT?</h3>
<p class="content-section__paragraph">DataGPT's single-query architecture limits anomaly investigation to predefined alerts. You can't ask follow-up questions or explore root causes. Scoop automatically chains 3-10 queries to investigate anomalies, testing hypotheses like a data analyst would. DataGPT scores 0/8 for investigation capability versus Scoop's 8/8.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Qlik Sense do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Qlik Sense requires manual dashboard navigation and pre-built associative models for root cause analysis. Users must know which visualizations to check and interpret relationships themselves. Scoop automatically performs multi-pass investigation, testing hypotheses and drilling into root causes without user guidance or pre-configuration.</p>
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<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop excels at multi-step analysis, automatically chaining 3-10 queries to answer complex questions. Unlike DataGPT's single query or Qlik Sense's manual dashboard clicking, Scoop investigates like a human analyst—forming hypotheses, testing them, and drilling deeper until finding root causes.</p>
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<h3 class="content-section__subtitle">Does DataGPT work with Excel?</h3>
<p class="content-section__paragraph">DataGPT requires users to log into their web portal—no native Excel integration exists. You must export data then manually import to Excel. Scoop works directly inside Excel as an add-in, letting users analyze data without leaving their spreadsheet. This eliminates context switching and copy-paste workflows.</p>
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<h3 class="content-section__subtitle">Can I use Qlik Sense directly in Slack?</h3>
<p class="content-section__paragraph">Qlik Sense offers limited Slack notifications but no interactive analysis. Users receive alerts then must switch to the Qlik portal. Scoop runs natively in Slack—ask questions and get charts directly in channels. Teams collaborate on data without leaving their conversation flow.</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, training programs, semantic layer maintenance, and ongoing consultants. Total cost typically reaches 5-10x the license fee. Scoop eliminates implementation, training, maintenance, and consultant costs—just a subscription that's a fraction of traditional BI TCO.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Are there hidden fees with Qlik Sense?</h3>
<p class="content-section__paragraph">Yes, Qlik Sense's hidden costs include data modeling consultants, associative model development, user training, server infrastructure, and annual maintenance. These typically add 3-5x the license cost annually. Scoop has no hidden fees—one transparent subscription covers everything including unlimited users and queries.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How long does it take to learn DataGPT?</h3>
<p class="content-section__paragraph">DataGPT requires 2-4 weeks training on their interface, metric definitions, and dashboard navigation. Power users need additional semantic layer training. Scoop requires zero training—if you can type a question, you're ready. Business users become productive immediately, asking questions in plain English like chatting with ChatGPT.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Qlik Sense?</h3>
<p class="content-section__paragraph">While Qlik Sense doesn't require SQL for basic dashboards, creating new analyses demands understanding data models, set expressions, and associative logic—equally technical skills. Scoop eliminates all technical requirements. Just ask questions in plain English and the AI handles all query complexity behind the scenes.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can business users use Scoop without IT help?</h3>
<p class="content-section__paragraph">Yes, business users connect Scoop to data sources in 30 seconds and start asking questions immediately. No semantic layer setup, no data modeling, no dashboard building. DataGPT scores 22/100 and Qlik Sense 47/100 on business user autonomy, while Scoop achieves 82/100.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: DataGPT or Qlik Sense?</h3>
<p class="content-section__paragraph">Qlik Sense offers more flexibility than DataGPT but both trap users in dashboard paradigms. DataGPT scores 22/100 on business autonomy, Qlik Sense 47/100. Neither supports true investigation. Scoop's 82/100 score reflects its chat-based approach that eliminates technical barriers entirely.</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 tool. Traditional BI like DataGPT and Qlik Sense require building visualizations before asking questions. Scoop flips this—ask first, get answers with appropriate charts automatically. No pre-building, no maintenance, just conversation.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Why doesn't Scoop require training?</h3>
<p class="content-section__paragraph">Scoop uses natural language like ChatGPT—if you can ask a colleague a question, you can use Scoop. No dashboards to navigate, no syntax to learn, no semantic layers to understand. DataGPT and Qlik Sense require training because they force users into technical frameworks.</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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