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<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">Snowflake Cortex vs Zenlytic vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">Snowflake Cortex</div>
<div class="score-card__score">26/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--competitor">
<div class="score-card__name">Zenlytic</div>
<div class="score-card__score">42/100</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 Snowflake Cortex (26/100) and Zenlytic (42/100) trap users in dashboard paradigms. Both competitors require IT support for anything beyond pre-built views, defeating the promise of self-service analytics. Choose Scoop for genuine independence, competitors only if already locked into 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 real investigation capability with 3-10 follow-up questions per analysis</li>
<li class="content-section__list-item">Business users want complete autonomy without IT dependency for new questions</li>
<li class="content-section__list-item">Your team lives in Excel and needs analytics without leaving their workflow</li>
<li class="content-section__list-item">You're tired of paying for training, consultants, and semantic layer maintenance</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Snowflake Cortex If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already deeply invested in Snowflake's data ecosystem and can't switch</li>
<li class="content-section__list-item">Your use cases are purely operational dashboards with no investigation needs</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Zenlytic If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You specifically need e-commerce metrics and can accept limited flexibility</li>
<li class="content-section__list-item">Your team prefers traditional BI interfaces despite the learning curve</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The 56-point BUA gap between Scoop and Cortex represents the difference between true autonomy and IT dependency . While Cortex and Zenlytic promise self-service, both require technical skills for anything beyond basic dashboards . Scoop eliminates five of six traditional BI cost categories by removing implementation, training, maintenance, consultants, and productivity loss . The investigation paradigm shift means business users can actually answer complex questions independently. This isn't about features—it's about fundamentally changing who controls data access in your organization.</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>Snowflake Cortex</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">26/100</td><td class="">42/100</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>Snowflake Cortex</th><th>Zenlytic</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="">Technical Skill Needed</td><td class="">0/8</td><td class="">1/8</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 versus near-zero for Snowflake Cortex and Zenlytic. While Snowflake requires SQL knowledge and Zenlytic demands semantic layer setup, Scoop enables immediate natural language investigation without IT involvement, reducing time-to-insight from days to minutes.</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>Snowflake Cortex</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native Integration</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Workflow Continuity</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 17/20 on Flow by embedding analytics directly in Slack, Teams, and Salesforce, while Snowflake Cortex and Zenlytic score 0/20, requiring users to leave their workflow for separate portals. Scoop enables in-context analysis without app-switching, preserving conversation flow and reducing time-to-insight by 70%.</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>Snowflake Cortex</th><th>Zenlytic</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="">7/8</td>
</tr>
<tr>
<td class="">Business Terminology</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Error Recovery</td><td class="">0/8</td><td class="">0/8</td><td class="">3/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Understanding, while Snowflake Cortex and Zenlytic score 0/20. Scoop understands natural business language without semantic layer setup. Cortex and Zenlytic require IT teams to pre-map every business term, limiting users to predefined concepts.</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>Snowflake Cortex</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Automatic Chart Selection</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Business-Ready Output</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
<tr>
<td class="">Export and Sharing</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 versus 0/20 for Snowflake Cortex and Zenlytic (not scored). Scoop automatically selects optimal chart types and formats visualizations based on question context, while traditional platforms require manual chart configuration and formatting by users or IT teams.</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>Snowflake Cortex</th><th>Zenlytic</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="">7/8</td>
</tr>
<tr>
<td class="">Schema Requirements</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Data Freshness</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Source Flexibility</td><td class="">0/8</td><td class="">0/8</td><td class="">5/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Data capabilities, while Snowflake Cortex and Zenlytic score 0/20. Scoop lets business users connect data sources directly via OAuth in 60 seconds. Snowflake Cortex and Zenlytic require IT teams to configure warehouses and semantic layers first.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">Capability Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Investigation & Root Cause Analysis</h3>
<p class="content-section__paragraph">When revenue drops 15% unexpectedly, the difference between platforms isn't who can show you the drop—any dashboard can do that. It's who can tell you why. Real investigation means following threads through multiple questions: Was it regional? Product-specific? A pricing issue? Customer segment shift? Traditional BI makes you build each query manually. Modern platforms should think like analysts, automatically exploring hypotheses and surfacing root causes. This capability separates dashboards from true analytical partners.</p>
<p class="content-section__paragraph">The architectural divide is stark. Snowflake Cortex translates English to SQL—one question, one query, investigation over. You get what you asked for, nothing more. No follow-up questions. No automatic exploration. Just SQL in a friendlier package. Zenlytic adds some investigation capability through its semantic layer, but you're still constrained by pre-defined metrics and relationships. It can handle 'show me sales by region,' then 'break that down by product.' But ask 'why did this change?' and you hit a wall. Scoop operates differently. It thinks like an analyst. Ask about a revenue drop and it automatically investigates: checking time periods, comparing segments, analyzing correlations, testing hypotheses. One question spawns 3-10 analytical queries. The platform pursues leads you didn't know to ask about. This isn't about better NLP or fancier visualizations. It's about fundamental architecture. Dashboard tools answer the question you asked. Investigation tools answer the question you meant to ask, plus the five follow-ups you need to understand why.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning crisis: conversion rates dropped 20% over the weekend. The CMO needs answers before the 10am board call. With Snowflake Cortex, the analyst writes SQL to check traffic sources. Normal. Then SQL for device types. Also normal. Then SQL for page performance. Still investigating. Each query takes 5 minutes to write and run. With Zenlytic, she uses natural language but still builds each query manually: 'Show conversion by source.' 'Now by device.' 'Now by landing page.' Faster than SQL but same manual process. With Scoop, she types: 'Why did conversion drop this weekend?' Scoop automatically investigates: correlates the drop with a Friday deployment, identifies mobile users affected, pinpoints three broken landing pages, and shows that iOS users couldn't complete checkout. Total time: 3 minutes. The difference? Scoop pursued the investigation autonomously while others required manual direction for each step.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about query languages or visualization options. It's about whether the platform thinks analytically. Snowflake Cortex gives you exactly what you ask for—no more, no less. Zenlytic enables some follow-up through its semantic layer but requires manual navigation. Scoop automatically pursues investigations like a human analyst would, testing hypotheses and uncovering root causes in minutes instead of hours.</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 read. This workflow reveals a fundamental truth: Excel remains the lingua franca of business analysis. The real question isn't whether platforms support Excel—it's how many clicks, exports, and manual steps stand between your Excel users and live data. Modern platforms should make Excel a first-class citizen, not an afterthought. Let's examine how each platform bridges this critical gap.</p>
<p class="content-section__paragraph">The Excel integration landscape splits into two camps: platforms that treat Excel as an export destination versus those that embrace it as a primary interface. Snowflake Cortex follows the traditional path—users must leave Excel, write SQL queries in Snowflake's interface, then export results back. Each question requires this round trip. Zenlytic improves slightly with API access, but users still work outside Excel, using Zenlytic's interface to build queries before pulling results. Scoop flips this model entirely. Its Excel add-in brings the full chat interface directly into spreadsheets. Users never leave Excel. They type questions in plain English, get live data with charts, and continue their analysis seamlessly. This architectural difference compounds. A financial analyst building a monthly report might ask 15-20 questions. With Snowflake, that's 15-20 context switches. With Scoop, it's zero. The time savings are measurable: 3 minutes per query with exports versus 30 seconds within Excel. For a 20-query analysis, that's an hour saved.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to build the monthly revenue report every first Monday. With Snowflake Cortex, she opens Snowsight, writes SQL to pull last month's revenue, exports to CSV, imports to Excel, then repeats for each data cut—by region, by product, by segment. Total time: 45 minutes. With Zenlytic, she uses their interface to build saved queries, then pulls each into Excel via their API connection. Faster, but still requires switching between tools. Total time: 25 minutes. With Scoop's Excel add-in, Sarah opens her template and types directly in Excel: 'Show me last month's revenue by region.' The data appears instantly. She continues: 'Compare to same month last year' and 'What drove the biggest change?' Each answer appears in seconds, right in her spreadsheet. Total time: 8 minutes. The report that consumed her entire morning now takes less time than a coffee break.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration reveals each platform's philosophy about business users. Snowflake and Zenlytic treat Excel as a destination—somewhere to put data after the 'real' analysis happens in their tools. Scoop treats Excel as home base, bringing AI-powered analysis to where business users already work. For organizations where Excel drives decision-making, this difference transforms productivity from hours to minutes.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">When your CFO asks 'What happens to our margins if we raise prices 5% but lose 10% of customers?', you need more than historical data—you need scenario modeling. Side-by-side scenario analysis lets teams compare multiple what-if situations simultaneously, testing assumptions before making critical decisions. This capability separates true analytical platforms from simple reporting tools. While dashboards show what happened, scenario analysis reveals what could happen under different conditions. Let's examine how each platform handles this strategic planning essential.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. Snowflake Cortex treats each scenario as a separate SQL query, requiring technical users to write CASE statements and JOIN logic for comparisons. You're essentially building a data model for each what-if question. Zenlytic offers basic scenario templates but limits you to predefined variables and three concurrent scenarios. Users report spending 2-3 hours setting up comparison frameworks. Scoop's conversational approach changes the game entirely. Type 'Compare our current pricing with a 5% increase scenario and a volume discount scenario, showing impact on revenue and margin.' Scoop builds all three models simultaneously, displaying them side-by-side with synchronized axes. When the CFO asks 'What if we make it 7% instead?', you simply type the adjustment. No query rewriting, no template editing. The platform maintains context across all scenarios, automatically recalculating dependencies. This isn't just faster—it enables exploration during live meetings. Teams report testing 10-15 scenarios in the time traditional tools handle 2-3.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A pricing committee meets to evaluate a new strategy. The VP Sales suggests raising enterprise prices 8% but worries about churn. With Scoop, the analyst types: 'Show me three scenarios: current pricing, 8% increase with 5% churn, and 5% increase with 2% churn. Include revenue, margin, and customer count impacts.' Within seconds, three columns appear showing quarterly projections. The CFO asks: 'What if churn hits 10%?' The analyst types the adjustment, and charts update instantly. They test seven different combinations in 15 minutes, documenting assumptions automatically. With Snowflake Cortex, this requires writing seven separate SQL queries with complex CASE logic. Zenlytic would need multiple dashboard configurations. The Scoop team reaches consensus with data, not opinions.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the gulf between chat interfaces and true conversational AI. While Snowflake Cortex requires SQL expertise for each scenario and Zenlytic constrains you to templates, Scoop enables real-time strategic planning through natural conversation. Teams test 5x more scenarios in 80% less time, turning what-if analysis from a technical exercise into a business conversation.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Machine Learning & Pattern Discovery</h3>
<p class="content-section__paragraph">Your sales data contains hidden patterns that predict customer churn, identify anomalies, and forecast trends—but only if you can access them. Traditional BI requires data scientists to build models, deploy them, and maintain them over time. Modern platforms promise to democratize ML, but the reality varies wildly. Some require SQL expertise to invoke functions. Others automatically surface insights without any configuration. The difference determines whether your business users discover critical patterns themselves or wait weeks for IT support.</p>
<p class="content-section__paragraph">Snowflake Cortex provides powerful ML functions—if you know SQL. Their anomaly detection requires writing queries like 'SELECT anomaly_detection()' with proper parameters. Business users can't access these capabilities directly. They need data engineers to build and maintain models. Zenlytic automatically runs anomaly detection on tracked KPIs. But it's limited to pre-configured metrics in dashboards. Users can't ask 'What's unusual about California sales this month?' and get an answer. Scoop treats ML as conversation. Ask 'What patterns exist in customer churn?' and it automatically runs clustering, correlation analysis, and predictive modeling. No configuration. No SQL. The platform learns from every query, improving pattern recognition over time. This architectural difference is fundamental. Cortex and Zenlytic bolt ML onto existing paradigms—SQL queries and dashboards respectively. Scoop embeds ML into natural conversation. A marketing manager can discover that customers who view pricing pages three times are 73% likely to churn—without knowing what logistic regression means.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory inconsistencies. With Scoop, she asks: 'What patterns exist in our stock-outs?' Scoop automatically analyzes seasonality, correlates with promotions, identifies supplier delays, and discovers that Tuesday deliveries have 3x higher stock-out rates. It then predicts next week's risk items. Total time: 2 minutes, zero technical knowledge required. With Snowflake Cortex, she would need IT to write SQL queries for time-series analysis, correlation matrices, and predictive models—a two-week project. Zenlytic could show anomalies if IT had pre-built the right dashboard, but couldn't investigate the pattern or predict future issues. The business impact: Scoop prevents $50K in lost sales next week, while competitors are still building the analysis.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in Snowflake Cortex requires SQL expertise, making it inaccessible to 95% of business users. Zenlytic offers basic anomaly detection but only on pre-configured metrics. Scoop democratizes ML completely—any employee can discover patterns, predict outcomes, and identify anomalies using plain English. This isn't about having ML features; it's about making them usable by the people who need them most.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Your best insights are worthless if they're trapped in a browser tab. Modern data work happens where teams collaborate—in Slack threads during incidents, Excel sheets during planning, mobile phones during customer visits. The real test isn't whether a platform has an API, but whether business users can actually access insights without leaving their workflow. Let's examine how each platform handles the reality of distributed work, where 73% of data questions arise outside traditional BI portals and teams need answers in seconds, not after logging into another dashboard.</p>
<p class="content-section__paragraph">The architectural divide is stark. Snowflake Cortex treats workflow integration as an afterthought—you get SQL APIs that require developers to build custom integrations. Business users can't just start chatting with data from Slack. Zenlytic offers basic webhook notifications but no true bi-directional communication. You'll know a metric changed but can't investigate why without switching contexts. Scoop's chat-first architecture changes everything. The same natural language interface works everywhere—Excel, Slack, mobile, API calls. A sales manager can start investigating win rates in Excel, continue the analysis in a Slack thread with the team, and finish on mobile at the airport. No context switching. No relearning interfaces. The investigation state travels with you. This isn't about having more integrations—it's about maintaining the investigation flow. When a CEO asks 'What happened with that deal?' in Slack, Scoop users answer in the same thread with live data. Cortex users paste screenshots from Snowsight. That five-second difference compounds across hundreds of daily questions.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning sales review. The VP notices unusual churn in the Enterprise segment while reviewing her Excel forecast model. With Scoop's Excel add-in, she types 'Which Enterprise customers churned last month and why?' directly in a sidebar. Scoop analyzes usage patterns, support tickets, and contract data, revealing that three customers cited missing API features. She copies this thread link to Slack, where the product team continues the investigation, asking 'What specific API features did they need?' The entire investigation—from Excel discovery to Slack collaboration to mobile review during her commute—maintains full context. With Snowflake Cortex, she would export data from Snowsight, import to Excel, write SQL for the churn analysis, screenshot results for Slack, and lose all investigation context when the product team has follow-up questions. The Scoop workflow took 5 minutes. The Cortex workflow takes 45 minutes and requires SQL knowledge.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop brings data investigation to where work actually happens, while Snowflake Cortex and Zenlytic force users to come to them. The difference isn't just convenience—it's the difference between a five-minute Slack conversation that solves a problem and a two-day email chain with screenshots and spreadsheet attachments. When 73% of data questions arise outside BI tools, platforms that trap insights in portals become expensive data graveyards.</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 Snowflake Cortex and Zenlytic which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop excels at multi-step investigation, automatically chaining 3-10 queries to find root causes. Snowflake Cortex handles single queries only, while Zenlytic offers basic drill-downs. Scoop investigates like a human analyst—testing hypotheses, exploring patterns, and uncovering insights dashboards miss entirely.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">How do I investigate anomalies in Snowflake Cortex?</h3>
<p class="content-section__paragraph">Snowflake Cortex can't investigate anomalies—it only answers single predefined questions. You'd need to manually write multiple SQL queries or build custom dashboards. Scoop automatically investigates anomalies through multi-pass analysis, testing hypotheses and finding root causes without any SQL knowledge required.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Zenlytic do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Zenlytic provides basic drill-downs but can't perform true root cause analysis. It requires manual exploration through pre-built dashboards. Scoop automatically conducts root cause analysis by chaining multiple queries, testing correlations, and identifying contributing factors—like having a data scientist investigate for you.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: Snowflake Cortex or Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic scores higher for business users (BUA 42/100) versus Snowflake Cortex (26/100), but both require significant IT support. Zenlytic offers better visualizations while Cortex has stronger SQL generation. Scoop (82/100) surpasses both by eliminating IT dependencies entirely—business users work completely autonomously.</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 in 30 seconds and start analyzing immediately—zero IT involvement. Snowflake Cortex requires data engineers for setup and maintenance. Zenlytic needs IT for semantic layer configuration. Scoop's 82/100 BUA score reflects true business autonomy versus 26/100 and 42/100 respectively.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What does Snowflake Cortex really cost including implementation?</h3>
<p class="content-section__paragraph">Snowflake Cortex true cost includes licenses, 3-6 month implementation, consultant fees, training programs, ongoing maintenance, and productivity loss during adoption. Total typically reaches 5-10x the license fee. Scoop eliminates implementation, training, maintenance, and consultant costs—reducing TCO by approximately 90 percent.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic claims no SQL required, but complex analysis needs technical knowledge of their semantic layer and metric definitions. Business users hit walls when questions exceed pre-built capabilities. Scoop requires zero technical knowledge—just type questions naturally. The AI handles all complexity behind the scenes.</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 requires building reports before asking questions. Scoop answers any question immediately through conversation. No semantic layers, no report builders, no SQL—just natural dialogue that delivers insights in seconds, not weeks.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Snowflake Cortex work with Excel?</h3>
<p class="content-section__paragraph">No, Snowflake Cortex requires using their web interface or SQL worksheets—no native Excel integration. Users must export data manually for Excel analysis. Scoop works directly inside Excel through an add-in, letting users analyze data where they're already working without switching tools or copying data.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can I use Zenlytic directly in Slack?</h3>
<p class="content-section__paragraph">Zenlytic offers limited Slack notifications but no direct analysis capabilities. Users receive alerts then must switch to Zenlytic's web interface. Scoop provides full analysis directly in Slack—ask questions, get charts, share insights without leaving your conversation. True workflow integration, not just notifications.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What training does Snowflake Cortex require?</h3>
<p class="content-section__paragraph">Snowflake Cortex requires understanding SQL concepts, data warehouse architecture, and Snowflake-specific functions. Typical training takes 2-4 weeks for basic proficiency. Advanced features need months of practice. Scoop requires zero training—if you can type a question, you're already an expert user.</p>
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<div class="content-section__subsection">
<h3 class="content-section__subtitle">What's the typical implementation time for Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic implementation typically takes 2-3 months including semantic layer setup, metric definitions, dashboard creation, and user training. Requires dedicated IT resources throughout. Scoop connects in 30 seconds with no implementation phase—business users start getting value immediately without any IT involvement or setup.</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. Snowflake Cortex requires SQL understanding, Zenlytic needs semantic layer knowledge. With Scoop, you just type questions as you'd ask a colleague. The AI handles all technical complexity, making everyone instantly productive.</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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