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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 Snowflake Cortex 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">Snowflake Cortex</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 Snowflake Cortex (26/100) trap users in single-query dashboards. Both competitors require extensive IT support for every new question, defeating the purpose of self-service analytics. Choose Scoop for genuine business autonomy, competitors only within existing vendor commitments.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard tool. Ask questions in plain English, get answers with charts instantly. Works natively in Excel and Slack where business users already work. No SQL, no training, no semantic layer maintenance ever required.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Choose Scoop If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">Business users need to investigate data independently without IT tickets</li>
<li class="content-section__list-item">Your team lives in Excel and needs analytics without leaving spreadsheets</li>
<li class="content-section__list-item">You want to eliminate consultant dependencies and training costs permanently</li>
<li class="content-section__list-item">Multi-step investigation matters more than pretty static dashboards</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're already committed to DataGPT's ecosystem despite limitations</li>
<li class="content-section__list-item">Single-query dashboards meet your needs without follow-up questions</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Snowflake Cortex If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">Your organization is deeply invested in Snowflake's data warehouse infrastructure</li>
<li class="content-section__list-item">You have dedicated IT resources to manage semantic layers continuously</li>
<li class="content-section__list-item">Static reporting satisfies your analytics requirements without investigation needs</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 genuine business empowerment while DataGPT and Snowflake Cortex hover in the 20s, confirming heavy IT dependence . The future belongs to platforms that trust business users with their own data.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">At-a-Glance Comparison</h2>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Dimension</th><th>DataGPT</th><th>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">22/100</td><td class="">26/100</td><td class="scoop-cell">82/100</td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">BUA Framework Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Autonomy (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Autonomy</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>DataGPT</th><th>Snowflake Cortex</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="">5/8</td>
</tr>
<tr>
<td class="">IT Dependency</td><td class="">0/8</td><td class="">2/8</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 18/20 on Autonomy by enabling multi-pass investigations without IT help. Snowflake Cortex scores 5/20 with single-query limitations requiring technical setup. DataGPT cannot be scored due to lack of public documentation. Scoop lets business users investigate independently while Cortex requires IT support for anything beyond basic queries.</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>Snowflake Cortex</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="">Export/Import Cycles</td><td class="">0/8</td><td class="">2/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Tool Switching</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 17/20 on Flow by embedding analytics directly in Slack/Teams, while DataGPT and Snowflake Cortex score 0/20 due to portal prison architecture. Scoop eliminates tool-switching and preserves context across investigations. DataGPT and Cortex require users to leave workflows and lose context between sessions.</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>Snowflake Cortex</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="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Error Handling</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</td>
</tr>
<tr>
<td class="">Context Awareness</td><td class="">0/8</td><td class="">0/8</td><td class="">1/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Understanding by processing natural business questions directly, while Snowflake Cortex scores 0/20 requiring SQL knowledge and DataGPT provides no documented natural language capability. Scoop lets business users ask 'Why did sales drop?' without knowing database structures.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Output Format Flexibility</td><td class="">0/8</td><td class="">0/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">4/8</td>
</tr>
<tr>
<td class="">Business Narrative Generation</td><td class="">0/8</td><td class="">0/8</td><td class="">3/8</td>
</tr>
<tr>
<td class="">Export and Sharing</td><td class="">0/8</td><td class="">0/8</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 0/20 for DataGPT and Snowflake Cortex (not scored). Scoop automatically generates business narratives with charts and explanations, while traditional BI platforms produce static dashboards requiring manual reformatting for executive consumption.</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>Snowflake Cortex</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Connection Setup</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Data Preparation</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Multi-Source Queries</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
<tr>
<td class="">Real-Time Access</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Schema Evolution</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Data capabilities by enabling direct database connections without IT setup. DataGPT and Snowflake Cortex score 0/20, requiring extensive data preparation and semantic layer maintenance. Scoop eliminates the 2-3 week data pipeline setup that blocks business users from accessing their own data.</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 symptom on a dashboard. Investigation platforms help you diagnose the disease. This capability separates tools that answer 'what' from those that answer 'why.' Most platforms require you to manually construct each hypothesis—checking seasonality, segments, regions—one painful query at a time. True investigation capability means the platform thinks alongside you, automatically testing hypotheses and surfacing hidden patterns. Let's examine how each platform handles the critical journey from symptom to root cause.</p>
<p class="content-section__paragraph">The fundamental divide isn't features—it's architecture. DataGPT operates on a single-query model. You ask about revenue decline, you get one chart showing the decline. Want to know why? Start over with a new query. No context carries forward. Snowflake Cortex offers more flexibility but requires SQL expertise for real investigation. Their natural language layer translates to SQL, but complex root cause analysis means writing joins and subqueries manually. Business users hit a wall immediately. Scoop's investigation engine works differently. Ask 'Why did revenue drop?' and it automatically runs 3-10 connected queries: checking time patterns, comparing segments, analyzing correlations, testing hypotheses. It thinks like an analyst, not a query translator. The platform maintains context throughout, so follow-up questions build on previous findings. This architectural difference shows in the numbers. DataGPT users average 8-12 manual queries to reach root cause, with each query starting from scratch. Snowflake Cortex users typically give up and call IT after 2-3 attempts. Scoop users reach root cause in 3-5 minutes with one conversational thread. The investigation capability score tells the story: DataGPT at 2/8, Cortex at 3/8, Scoop at 8/8.</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 inventory levels spiking in Northeast stores?' Scoop automatically investigates: analyzing 6-month trends, comparing regions, checking SKU patterns, correlating with promotions, and identifying that a new competitor's store closures drove unexpected demand in specific categories. Total time: 4 minutes, zero SQL, one conversation. With DataGPT, she'd need to manually construct each hypothesis as a separate query: 'Show inventory by region,' then 'Compare Northeast to other regions,' then 'Show SKU breakdown for Northeast'—each time losing context and starting fresh. After 6-7 queries and 45 minutes, she might stumble upon the pattern. Snowflake Cortex would require switching to SQL mode after the first basic query, writing complex joins to analyze multi-dimensional patterns. Most business users would give up and schedule a meeting with the data team. The difference isn't just time—it's whether business users can investigate independently at all.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability reveals the truth about 'self-service' analytics. DataGPT and Snowflake Cortex handle simple questions but force manual reconstruction for real investigation. Scoop's automatic multi-pass architecture means business users actually reach root causes independently. When problems cost thousands per hour, the difference between 5-minute and 50-minute investigations isn't just convenience—it's competitive advantage. 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 open Excel to build reports from BI data. They copy charts from dashboards, export CSVs, manually update formulas, and email spreadsheets to executives. This workflow hasn't changed in 20 years—until now. The real question isn't whether platforms connect to Excel, but whether business users can work naturally in Excel without leaving it. Let's examine how each platform handles the tool that still drives 80% of business analysis.</p>
<p class="content-section__paragraph">The Excel integration divide reveals fundamental architectural choices. DataGPT treats Excel as an export destination—you get CSVs and screenshots. Business users must rebuild everything manually. Snowflake Cortex assumes Excel users know SQL, requiring complex query construction for any data pull. Neither platform offers a native Excel experience. Scoop embeds directly into Excel as an add-in. Users highlight cells and ask questions about their data. Charts appear instantly in worksheets. The conversation continues naturally—'Why did this number spike?' gets an immediate answer with supporting visualizations. This isn't about features; it's about workflow reality. Finance teams live in Excel. They need answers where they work, not in separate portals. Traditional BI forces constant context switching: log into portal, find dashboard, export data, import to Excel, rebuild analysis. Each step adds friction and error potential. Scoop eliminates these steps. Ask your question in Excel, get your answer in Excel. The 3-minute analysis stays 3 minutes, not 30.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to investigate unusual variance in regional sales for the board deck due tomorrow. With Scoop's Excel add-in, she highlights the suspicious numbers and types 'What drove this 15% variance vs forecast?' Scoop analyzes the data, identifies three contributing factors, and inserts charts directly into her spreadsheet. She follows up: 'Break this down by product category.' New charts appear. Total time: 4 minutes. With DataGPT, Sarah exports data to CSV, imports to Excel, manually calculates variances, builds charts from scratch, and hopes she didn't miss anything. Time: 45 minutes. With Snowflake Cortex, she writes SQL queries for each analysis angle, exports results, and rebuilds in Excel. Time: 60 minutes if she knows SQL; impossible if she doesn't.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration isn't about connection—it's about continuation. DataGPT and Snowflake Cortex treat Excel as a destination for exported data, forcing manual rebuilding of every analysis. Scoop makes Excel a natural environment for AI-powered investigation. Business users stay in their workflow, ask questions in plain English, and get answers instantly. For the 750 million Excel users worldwide, this difference determines whether AI analytics is actually usable.</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 parallel scenario modeling—not sequential dashboard updates. This capability separates investigation platforms from reporting tools. True scenario analysis means exploring multiple futures simultaneously, comparing outcomes in real-time, and adjusting assumptions on the fly. Let's examine how DataGPT, Snowflake Cortex, and Scoop handle this critical strategic planning need.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. DataGPT's single-query paradigm forces users to run separate analyses then manually compare results—destroying the cognitive flow of strategic thinking. Each scenario requires a new conversation, losing all context from previous explorations. Snowflake Cortex offers more flexibility through SQL, but demands users write complex UNION queries and window functions to achieve side-by-side views. Business users hit an immediate wall. They need data engineers to construct comparison frameworks. Scoop's conversation memory changes everything. Ask 'Compare revenue if we raise prices 10% versus adding 3 new sales reps.' Scoop maintains both scenarios simultaneously, showing parallel projections. Follow up with 'What if we did both but phased over 6 months?' The context persists. The scenarios evolve. No SQL. No rebuilding. This isn't just convenience—it's the difference between dynamic strategy sessions and static report generation. When boards ask tough questions about competing strategies, only Scoop enables real-time exploration without technical intermediaries.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CPO evaluates pricing strategy for 2025. She starts: 'Show me revenue impact of 10% price increase versus 15% increase with 5% churn.' Scoop instantly displays both scenarios side-by-side with projected quarterly outcomes. She refines: 'Add a third scenario with 5% increase but expanded features.' The comparison updates, maintaining all three views. She spots opportunity: 'Which customer segments show least price sensitivity in scenario 2?' Scoop drills into the 15% scenario, revealing enterprise accounts absorb increases better. Total analysis time: 8 minutes. With DataGPT, she'd run five separate queries, copy results to Excel, build comparison charts manually. Time: 45+ minutes. Snowflake Cortex would require IT to write multi-CTE SQL statements with CASE logic for each scenario branch. Time: 2-3 hours including IT coordination.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the investigation versus dashboard divide. DataGPT and Snowflake Cortex treat each scenario as isolated queries, forcing manual comparison and destroying analytical flow. Scoop maintains parallel scenarios in conversation memory, enabling true what-if exploration. For strategic planning where comparing options drives decisions, only Scoop delivers the fluid, iterative analysis executives need. The difference: 8 minutes of exploration versus hours of query construction.</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 automatic pattern discovery and ML-powered insights, yet most still require Python scripts, model training, and statistical expertise. Let's examine how each platform actually delivers on the promise of democratized machine learning for business users.</p>
<p class="content-section__paragraph">The fundamental divide in ML capabilities isn't about algorithm sophistication—it's about accessibility. Snowflake Cortex offers powerful ML functions like FORECAST() and ANOMALY_DETECTION(), but business users must write SQL like 'SELECT anomaly_detection(sales_amount, timestamp) FROM sales_data'. That's not democratization; it's gatekeeping with better marketing. DataGPT provides some automated insights through its Lightning Cache, but pattern discovery remains limited to predefined metrics and KPIs. Users get alerts about anomalies but can't explore why they occurred without technical help. Scoop takes a different approach: every question automatically triggers pattern analysis. Ask 'Show me Q3 sales' and Scoop doesn't just show numbers—it identifies unusual patterns, correlations with other metrics, and potential anomalies without being asked. The platform runs multiple ML models in parallel, selecting the most relevant insights for the business context. This isn't about having more algorithms; it's about making them invisible. A marketing manager investigating campaign performance doesn't need to know about regression models or confidence intervals. They need to know that email open rates predict purchase behavior three days later. Scoop surfaces these insights in plain English, while competitors require users to explicitly request each analysis type.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory costs rising. With Scoop, she asks 'Why are inventory costs increasing?' Scoop automatically runs pattern detection, finding that slow-moving items in Category B increased 40% after a supplier change. It correlates this with weather patterns, discovering that unseasonable temperatures reduced demand. Finally, it predicts that continuing current ordering patterns will increase costs by $2.3M next quarter. Total interaction: one question, two follow-ups, five minutes. With Snowflake Cortex, she'd need IT to write SQL queries using TIME_SERIES_FORECASTING() and CORRELATION() functions, interpret statistical outputs, and manually connect findings. DataGPT would flag the cost increase but require multiple manual queries to investigate causes, with no automatic correlation discovery. The business impact is clear: Scoop delivers actionable insights in minutes, while competitors require hours of technical work or IT involvement.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI platforms isn't about having the most algorithms—it's about making them useful to actual business users. While Snowflake Cortex offers powerful ML functions for technical users and DataGPT provides basic anomaly detection, only Scoop automatically applies ML to every business question without requiring any technical knowledge. The result: pattern discovery that happens at the speed of curiosity, not at the speed of IT ticket resolution.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Your sales team discovers a critical insight at 3 PM. By 3:15, it needs to be in Excel for the board deck, shared in Slack for discussion, and accessible on the CEO's phone during her flight. This isn't about having mobile apps—it's about how insights flow through your existing work tools. Modern analytics must meet users where they already work, not force them into yet another portal. The difference between platforms here isn't features; it's friction.</p>
<p class="content-section__paragraph">The architectural divide shows clearly in workflow integration. DataGPT treats Excel as an export destination—you get static data that ages immediately. Snowflake Cortex requires switching between SQL interfaces and has no native business tool integration. Users must leave their workflow, learn a new interface, then manually move results back. Scoop embeds directly into Excel as a live add-in. Type questions in a sidebar, get charts that auto-update. The Slack integration goes beyond notifications. Teams investigate together in channels, with Scoop responding to natural language queries inline. Mobile reveals the same pattern. DataGPT offers view-only dashboards on phones—no investigation possible. Cortex requires the Snowflake mobile app with limited functionality. Scoop's chat interface works identically on mobile. Ask follow-up questions, dive deeper, share findings—all from your phone. The API comparison is telling. DataGPT and Cortex offer traditional REST/SQL APIs requiring technical translation. Scoop's natural language API means any app can embed 'ask data questions' functionality without SQL knowledge.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning, 8 AM. The VP of Sales is reviewing quarterly results in Excel when she spots an anomaly in the Southwest region. With Scoop's Excel add-in, she types 'Why did Southwest enterprise deals drop 20% last quarter?' directly in the sidebar. Scoop investigates automatically, revealing a correlation with a competitor's new pricing. She copies the chart into her deck, then shares the finding in the sales Slack channel. Her team asks follow-up questions right in Slack: 'Which products were most affected?' 'Show me deal velocity changes.' The regional manager, traveling to a client meeting, continues the investigation on his phone during an Uber ride. By 9 AM, the team has identified three at-risk accounts and adjusted their strategy. With DataGPT or Cortex, this same investigation would require logging into a separate platform, building multiple queries, exporting results, and losing all context between tools.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about feature checkboxes—it's about friction. Every context switch costs 23 minutes of productivity. Every export-import cycle introduces errors. Scoop eliminates these switches by living inside Excel and Slack, while maintaining full investigation capability on mobile. DataGPT and Cortex force users out of their tools into separate portals, breaking flow and losing context.</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 Snowflake Cortex which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How do I investigate anomalies in DataGPT?</h3>
<p class="content-section__paragraph">DataGPT shows anomalies but can't investigate why they happened. You get alerts saying 'revenue dropped 15%' without root cause analysis. Scoop automatically chains 3-10 queries to find why: which products, regions, or customers drove the change. DataGPT's single-query limitation means manual Excel investigation afterward.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Snowflake Cortex do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Snowflake Cortex requires you to write SQL queries sequentially for root cause analysis. It can't automatically chain investigations or test hypotheses. Scoop runs multiple analytical passes automatically, testing correlations and drilling into segments without manual query writing. Cortex is a SQL assistant, not an analyst.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop automatically chains 3-10 queries for complete investigations. Ask 'why did sales drop?' and Scoop analyzes products, regions, customers, and time periods automatically. DataGPT and Snowflake Cortex require manual follow-up queries. Scoop thinks like an analyst, not just answering one question at a time.</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. You download CSVs from their web portal, losing formatting and interactivity. Scoop works directly inside Excel as an add-in. Ask questions in a sidebar, get answers in your spreadsheet. No export-import cycles needed.</p>
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<h3 class="content-section__subtitle">Can I use Snowflake Cortex directly in Slack?</h3>
<p class="content-section__paragraph">No, Snowflake Cortex only works within Snowflake's SQL worksheet environment. You must log into Snowflake, write queries, then manually share results. Scoop has native Slack integration—ask questions directly in channels, get charts instantly. Your team sees analysis in real-time without leaving their collaboration workspace.</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, ongoing maintenance, and often consultants. Total typically reaches 5-10x the license fee. Hidden costs include semantic layer updates and IT support hours. Scoop eliminates these categories—just subscription pricing with 30-second setup.</p>
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<h3 class="content-section__subtitle">Do I need SQL knowledge for Snowflake Cortex?</h3>
<p class="content-section__paragraph">Yes, Snowflake Cortex helps write SQL but you still need to understand query structure, joins, and aggregations. It's an SQL assistant, not a replacement. Business users struggle without database knowledge. Scoop requires zero SQL—just ask questions naturally like 'show me sales by region last quarter.'</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 without IT. No semantic layers, no permissions setup, no training. DataGPT and Snowflake Cortex require IT for setup, maintenance, and troubleshooting. Scoop's 82/100 BUA score reflects true business autonomy versus their sub-30 scores.</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 covering their interface, metric definitions, and dashboard building. Users need understanding of their semantic layer and predefined KPIs. Most companies hire consultants for initial setup. Scoop needs zero training—if you can type a question, you're already an expert.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: DataGPT or Snowflake Cortex?</h3>
<p class="content-section__paragraph">Neither empowers business users effectively. DataGPT scores 22/100 BUA, Snowflake Cortex 26/100—both require heavy IT support. DataGPT limits you to predefined metrics; Cortex requires SQL knowledge. Scoop at 82/100 BUA lets business users investigate freely without technical skills or IT dependency.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How is Scoop different from traditional BI tools?</h3>
<p class="content-section__paragraph">Scoop is an AI analyst you chat with, not a dashboard builder. Traditional BI requires building reports before getting answers. With Scoop, ask any question instantly without pre-built dashboards. It's the difference between having an analyst on your team versus learning to be one yourself.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Why doesn't Scoop require training?</h3>
<p class="content-section__paragraph">Scoop uses natural language like ChatGPT—no special syntax or technical knowledge needed. DataGPT requires learning their metrics system, Snowflake Cortex needs SQL understanding. If you can ask a colleague for data, you can use Scoop. The interface disappears, leaving just conversation about your business.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Scoop support financial planning analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop handles complex financial analysis including variance analysis, forecasting, and scenario planning through natural conversation. Ask 'compare budget to actuals by department' or 'project cash flow next quarter.' Unlike DataGPT's fixed metrics or Cortex's SQL requirements, Scoop adapts to any financial analysis need instantly.</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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