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<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">Sisense vs Zenlytic vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">Sisense</div>
<div class="score-card__score">28/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--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 Sisense (28/100) and Zenlytic (42/100) trap users in dashboard paradigms. Both competitors require IT intervention for new questions, creating the same bottlenecks they claim to solve. 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 follow-up questions) without IT help</li>
<li class="content-section__list-item">Business users work primarily in Excel and need analytics there</li>
<li class="content-section__list-item">You want to eliminate consultant dependencies and training costs permanently</li>
<li class="content-section__list-item">Teams need answers in hours, not weeks of dashboard development</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Sisense If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already heavily invested in the Sisense ecosystem and can't migrate</li>
<li class="content-section__list-item">Your use cases are purely static 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 SQL-based semantic layer control despite the maintenance burden</li>
<li class="content-section__list-item">Your team prefers building dashboards over getting immediate answers</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The BUA scores reveal the truth: Scoop's 82/100 represents genuine business empowerment, while Sisense's 28/100 and Zenlytic's 42/100 confirm continued IT dependency . Business users become truly autonomous, asking and answering their own questions immediately.</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">28/100</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Investigation Depth</td><td class="">2/8</td><td class="">3/8</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="">Query Flexibility</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 versus near-zero for Sisense and Zenlytic, which require IT-maintained semantic layers for every query. Business users can investigate problems independently with Scoop through natural conversation, while competitors trap users in pre-built dashboards requiring IT support for new questions.</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Workflow Integration</td><td class="">0/8</td><td class="">1/8</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="">Sharing & Collaboration</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 bringing analytics directly into Slack and Teams, while Sisense and Zenlytic score 0/20, requiring users to visit separate portals. Scoop eliminates context switching, preserves conversation history, and enables instant sharing without exports or screenshots.</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">2/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Business Terminology Mapping</td><td class="">0/8</td><td class="">2/8</td><td class="">5/8</td>
</tr>
<tr>
<td class="">Error Recovery & Guidance</td><td class="">0/8</td><td class="">1/8</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 Understanding versus 0/20 for both Sisense and Zenlytic. Scoop handles natural business language without requiring semantic layer setup, while Sisense and Zenlytic force users to learn technical field names and pre-defined metrics. Business users can ask Scoop questions conversationally and get immediate answers.</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Chart Selection</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Context Integration</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Export Quality</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</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 Sisense and Zenlytic. Scoop's AI automatically selects chart types, formats visualizations, and embeds explanatory context. Sisense and Zenlytic require manual chart selection, formatting, and export cycles. Business users get boardroom-ready outputs instantly with Scoop.</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>Sisense</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Database Connection</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Multi-Source Joining</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Real-Time Data Access</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Schema Flexibility</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Governance</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 by connecting directly to raw databases without preparation. Sisense and Zenlytic both score 0/20, requiring extensive data modeling, ETL pipelines, and semantic layers before any analysis. Scoop eliminates the entire data preparation layer that consumes 60% of traditional BI project time.</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 becomes stark. Traditional BI shows you what happened through dashboards. Investigation platforms help you understand why. This capability separates single-query tools from true analytical partners. Most platforms require you to know what questions to ask. Investigation means the platform helps you discover questions you didn't know existed. It's the difference between a speedometer showing you're slowing down and a diagnostic system explaining why.</p>
<p class="content-section__paragraph">Sisense operates on a dashboard paradigm where each question requires a new widget. Users must manually construct investigation paths through multiple dashboards. Their Pulse Alerts can notify about anomalies but can't explain them. Zenlytic bridges the gap with conversational follow-ups, allowing users to ask 'why' and 'what else' naturally. Their correlation engine suggests related metrics but requires users to validate connections manually. Scoop treats investigation as a multi-step conversation. Ask about dropping revenue and it automatically checks seasonality, segments, product mix, and customer behavior. Each answer builds on the previous, creating a investigation thread. The platform remembers context across questions, eliminating the need to restate parameters. Where Sisense users click through 5-6 dashboards to investigate an issue, Scoop users have one conversation. This architectural difference means business users can investigate independently without pre-built dashboards or IT support.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A VP of Sales sees conversion rates dropped 20% last month. With Sisense, she opens the conversion dashboard, notices the drop, then manually checks regional dashboards, product dashboards, and rep performance dashboards. Each requires different filters and parameters. After 45 minutes and help from IT to build a custom query, she discovers it's isolated to enterprise accounts in the Northeast. With Zenlytic, she asks about the conversion drop and follows up with 'break this down by segment.' The platform shows enterprise is the issue. She asks 'why enterprise?' but needs to manually investigate further. With Scoop, she types 'Why did conversion rates drop last month?' Scoop automatically segments by region, product, and customer type, identifies enterprise Northeast as the problem, discovers these accounts had longer sales cycles after a competitor's price cut, and suggests focusing on value proposition. Total time: 3 minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability determines whether business users can answer 'why' questions independently. Sisense's dashboard-centric approach requires extensive manual analysis and pre-built views. Zenlytic enables conversational exploration but stops short of automatic root cause analysis. Scoop's multi-pass architecture means business users can investigate complex issues in minutes, not hours, without any technical knowledge.</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 costs enterprises millions in duplicate work and stale data. The real question isn't whether platforms support Excel—it's whether business users can seamlessly work between their spreadsheets and analytics without IT intervention. Modern platforms approach this challenge through fundamentally different architectures: native add-ins that bring analytics into Excel, versus export-import cycles that fragment the workflow.</p>
<p class="content-section__paragraph">Sisense takes the traditional approach with an Excel add-in that requires installation, IT approval, and training on proprietary formulas. Users must learn Sisense-specific functions to pull data, and any formula changes require understanding the semantic layer. The add-in provides live connections but forces users to work within Sisense's data model constraints. Zenlytic offers basic export functionality without any Excel integration, treating spreadsheets as a data destination rather than a workflow component. Users export static CSV files and lose all connection to the source data. Scoop flips the paradigm entirely. Instead of forcing Excel to understand analytics platforms, Scoop understands what Excel users need. Ask 'What were the top 10 products by revenue last quarter?' and get a perfectly formatted table ready to paste. Need a different view? Just ask again in plain English. No add-ins to install, no formulas to learn, no semantic layer to navigate. The AI handles the complexity while users focus on their analysis. This architectural difference shows in productivity metrics: Sisense users average 15 minutes from question to Excel-ready data, Zenlytic users spend 20 minutes on export and formatting, while Scoop users get Excel-ready results in under 2 minutes.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to create the monthly board report combining data from multiple sources. With Sisense, she opens Excel, loads the add-in, authenticates, navigates to the right data model, writes SISENSE.GET formulas for each metric, waits for refresh, then formats the results. If she needs a different breakdown, she must rewrite formulas and refresh again. Total time: 45 minutes. With Zenlytic, she logs into the web interface, creates separate queries for each metric, exports to CSV, imports to Excel, manually formats everything, and loses any ability to refresh the data. Total time: 35 minutes. With Scoop, she types 'Monthly revenue by product line with YoY comparison' and gets a formatted table in 30 seconds. She asks follow-up questions like 'Break down the enterprise segment by region' and pastes the new view. The entire report takes 8 minutes, and she can update it next month by simply asking the same questions with new dates.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: While Sisense offers traditional Excel integration through add-ins and Zenlytic provides basic exports, Scoop eliminates the integration problem entirely. Business users get Excel-ready data through natural conversation, no technical setup required. The difference isn't just speed—it's that finance teams can actually create reports independently without IT support or formula training.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">When executives need to compare multiple business scenarios—like 'What if we raise prices 10% versus cutting costs 15%?'—they're testing strategic decisions worth millions. Traditional BI forces this analysis into Excel exports and manual calculations. Modern platforms should enable instant scenario comparison without leaving the analysis environment. The difference between platforms isn't whether they can show scenarios, but how many steps business users need to create and compare them. Let's examine how Sisense, Zenlytic, and Scoop handle this critical strategic capability.</p>
<p class="content-section__paragraph">The architectural divide shows clearly in scenario analysis. Sisense requires building ElastiCube models with predetermined scenarios—business users wait for IT to configure each comparison view. Their Fusion Analytics Engine handles calculations, but someone must code the formulas first. Zenlytic's SQL-first approach means scenarios need database expertise. Users write queries like 'SELECT revenue * 1.1 AS scenario_1' for each variation. No business user does this independently. Scoop treats scenarios as conversation. Ask 'Compare revenue if we increase prices 10% versus expanding to two new markets.' The AI builds all calculations, adjusts for dependencies, and presents side-by-side results. When executives want to adjust—'What if we only raise prices 5%?'—it's another sentence, not another IT ticket. The investigation continues naturally. Traditional platforms fragment scenario analysis across tools. Sisense users export to Excel for complex comparisons. Zenlytic requires multiple query windows. This context-switching breaks analytical flow. Scoop keeps everything in one conversation thread. The scenario history becomes organizational memory—why did we choose Option A over Option B? The conversation shows the full analysis path.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CFO preparing for board meeting needs to compare three growth strategies: raising prices 8%, cutting operational costs 12%, or entering two new markets. With Sisense, she requests IT to build a comparison dashboard. IT creates ElastiCube calculations for each scenario—two days later, she gets static views. Adjusting assumptions means another IT request. With Zenlytic, the data team writes SQL queries for each scenario, exports results to Excel, and manually creates comparison charts. The CFO can't test variations herself. With Scoop, she types: 'Compare our profit margin under three scenarios: price increase 8%, cost reduction 12%, or entering Texas and Florida markets.' Scoop instantly generates side-by-side projections with confidence intervals. She asks: 'What if we only cut costs 8% but also raise prices 3%?' New comparison appears in seconds. The entire analysis, including seven scenario variations tested during the conversation, takes 15 minutes. She shares the Scoop thread directly with the board—complete analysis history preserved.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the investigation gap between platforms. Sisense and Zenlytic treat scenarios as predetermined dashboard configurations requiring technical setup. Scoop enables fluid scenario exploration through conversation—business users test unlimited variations independently. The difference: strategic decisions in minutes versus days of IT dependency. When million-dollar choices need quick scenario testing, conversational analysis beats dashboard configuration every time.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Machine Learning & Pattern Discovery</h3>
<p class="content-section__paragraph">Your sales data contains hidden patterns that predict next quarter's revenue, but finding them shouldn't require a data science degree. Modern platforms promise automatic pattern discovery and predictive insights, yet most still require manual model configuration, Python scripting, or expensive add-ons. The real question isn't whether a platform 'has ML'—it's whether business users can actually discover meaningful patterns without IT intervention. Let's examine how each platform delivers on this promise, from automatic anomaly detection to predictive forecasting.</p>
<p class="content-section__paragraph">The architectural divide in ML capabilities mirrors the broader platform philosophies. Sisense treats ML as an advanced feature requiring Sisense Fusion configuration and often custom scripting. Their documentation explicitly states 'data scientists can extend analytics with R and Python notebooks,' revealing the expected user profile. Zenlytic embeds basic ML into their semantic layer but limits it to pre-defined metrics and time-series forecasting. Users can't ask for custom pattern analysis without SQL knowledge. Scoop's conversational AI automatically applies relevant ML techniques based on the question context. Ask 'What factors correlate with customer churn?' and Scoop runs correlation analysis, identifies patterns, and explains findings in plain English. No configuration, no code, no data science degree. The difference shows in deployment stats: Sisense ML features see 15% adoption after 6 months versus 73% for Scoop's automatic insights. This isn't about having more algorithms—it's about making pattern discovery accessible to everyone who needs answers.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns and needs to understand the root cause. With Scoop, she types: 'Analyze inventory anomalies for the past quarter and identify patterns.' Scoop automatically detects outliers in 47 locations, correlates them with local events, weather patterns, and promotional calendars, then presents findings: '73% of anomalies correlate with unplanned competitor promotions, concentrated in urban locations.' Total time: 2 minutes. With Sisense, she would need IT to configure anomaly detection rules in Fusion, build correlation widgets, and manually cross-reference external factors—a 2-week project requiring data team support. Zenlytic would require writing SQL queries to identify outliers, with no automatic correlation capability. The business impact? Scoop users identify and respond to patterns in minutes. Traditional BI users often never discover them at all.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI platforms splits into two camps: those requiring configuration and code (Sisense, Zenlytic) versus conversational discovery (Scoop). While Sisense offers powerful ML through Fusion and notebooks, it's built for data scientists, not business users. Zenlytic provides basic forecasting but lacks true pattern discovery. Scoop makes ML invisible—users just ask questions and get intelligent answers with automatic pattern detection, correlation analysis, and predictive insights.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Your best insights mean nothing if they're trapped in a BI portal. Modern business happens in Excel, Slack, email—wherever teams actually work. The real test isn't whether a platform has mobile apps or APIs. It's whether a sales rep can pull customer insights during a client call, or if your team can investigate a revenue drop without leaving Slack. This comparison examines how each platform fits into real workflows versus forcing users into yet another portal.</p>
<p class="content-section__paragraph">The workflow integration divide reflects fundamental architecture choices. Sisense follows the traditional BI pattern: build in our portal, export elsewhere. Their mobile app displays pre-built dashboards but doesn't allow new questions. Excel users get static exports, breaking the investigation flow. Zenlytic takes a developer-first approach with API access but lacks native integrations business users need. No Excel add-in means analysts must constantly switch contexts. Scoop's chat-first architecture translates naturally across channels. The same conversation interface works in Excel, Slack, mobile, or email. Users ask questions wherever they are, using the same natural language. This isn't about having more integrations—it's about maintaining investigation continuity. When a sales manager spots an anomaly in Excel, they can investigate right there. When the CEO asks a question in Slack, the team explores together without switching tools. The mobile experience exemplifies this difference: Sisense and Zenlytic mobile apps are dashboard viewers, while Scoop maintains full investigation capability.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning, 9 AM. The VP of Sales notices unusual churn numbers in her Excel forecast model. With Sisense, she must: export the data, log into the portal, build a new dashboard to investigate causes, then screenshot results back to Excel. Total time: 45 minutes across 4 applications. With Zenlytic, she's stuck—no Excel integration means manual data transfer and no way to investigate without SQL knowledge. With Scoop's Excel add-in, she types 'Why did enterprise churn spike last month?' directly in Excel. Scoop investigates: identifies 3 major accounts lost to a competitor, shows contract timing patterns, and reveals price sensitivity increased after the competitor's new launch. She shares findings to Slack where her team continues investigating. Total time: 5 minutes, zero context switches.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about feature checkboxes—it's about investigation continuity. While Sisense offers basic exports and Zenlytic provides APIs for developers, only Scoop maintains full analytical capability across all channels. Business users can start an investigation in Excel, continue it in Slack, and check results on mobile without losing context or capability.</p>
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<h2 class="content-section__title">Frequently Asked Questions</h2>
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<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 Sisense and Zenlytic 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 Sisense?</h3>
<p class="content-section__paragraph">Sisense requires building dashboards first, then drilling down through pre-configured paths. You can't ask follow-up questions dynamically. Scoop automatically investigates anomalies through 3-10 chained queries, testing hypotheses like a real analyst would. Zenlytic offers some automated insights but limited follow-up capability.</p>
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<h3 class="content-section__subtitle">Can Zenlytic do root cause analysis automatically?</h3>
<p class="content-section__paragraph">Zenlytic provides some automated insights but can't chain multiple investigative queries. True root cause requires 3-10 follow-up questions. Scoop automatically performs multi-pass investigation, while Zenlytic stops after one insight. Sisense requires manual dashboard navigation. Only Scoop mimics how analysts actually investigate problems.</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 investigate problems. Ask 'why did sales drop?' and Scoop explores regions, products, and timeframes automatically. Sisense and Zenlytic require manual dashboard clicking. This investigation capability is what separates Scoop from traditional BI tools.</p>
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<h3 class="content-section__subtitle">Does Sisense work with Excel?</h3>
<p class="content-section__paragraph">Sisense offers limited Excel export functionality but doesn't work natively within Excel. Users must switch between applications constantly. Scoop runs directly inside Excel as an add-in. Zenlytic also requires separate login. With Scoop, analyze data without leaving your spreadsheet—where business users actually work.</p>
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<h3 class="content-section__subtitle">Can I use Zenlytic directly in Slack?</h3>
<p class="content-section__paragraph">Zenlytic doesn't offer native Slack integration—you must use their separate web interface. Scoop works directly in Slack channels where teams collaborate. Sisense also lacks native Slack support. Ask Scoop questions in Slack, get charts instantly. No context switching, no separate logins, just answers where you work.</p>
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<h3 class="content-section__subtitle">What does Sisense really cost including implementation?</h3>
<p class="content-section__paragraph">Sisense true cost includes licenses, 3-6 month implementation, training, consultants, maintenance, and lost productivity. Total typically reaches 5-10x the license fee. Zenlytic has similar hidden costs. Scoop eliminates implementation, training, and consultant fees—just subscription pricing. This reduces total cost of ownership by 90%.</p>
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<h3 class="content-section__subtitle">Do I need consultants to use Sisense?</h3>
<p class="content-section__paragraph">Yes, most Sisense deployments require consultants for setup, semantic layer design, and dashboard creation. Costs range from $50k-500k annually. Zenlytic also needs technical expertise. Scoop requires zero consultants—business users connect and start asking questions immediately. No semantic layer, no data modeling, no consultant dependency.</p>
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<h3 class="content-section__subtitle">How long does it take to learn Sisense?</h3>
<p class="content-section__paragraph">Sisense requires 2-4 weeks of formal training plus months to master. Users must learn data modeling, dashboard design, and Sisense-specific formulas. Zenlytic needs 1-2 weeks. Scoop requires zero training—if you can type a question, you're ready. Business users become productive in minutes, not weeks.</p>
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<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 queries still need technical knowledge. Their natural language has limitations requiring SQL fallback. Sisense also requires technical skills for advanced analysis. Scoop handles complex multi-table joins automatically through AI. Business users get enterprise-grade analysis without any SQL knowledge.</p>
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<h3 class="content-section__subtitle">Can business users use Scoop without IT help?</h3>
<p class="content-section__paragraph">Yes, business users connect Scoop to data and start analyzing in 30 seconds—completely autonomous. Sisense scores 28/100 on business user autonomy, requiring heavy IT support. Zenlytic scores 42/100, still IT-dependent. Scoop's 82/100 score means true self-service: no tickets, no waiting, just answers.</p>
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<h3 class="content-section__subtitle">Which is better for business users: Sisense or Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic edges out Sisense for business users with 42/100 versus 28/100 BUA score. However, both still require significant IT support. Scoop at 82/100 delivers true business autonomy. Neither Sisense nor Zenlytic supports multi-pass investigation. For genuine self-service analytics, Scoop surpasses both traditional platforms.</p>
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<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 like Sisense and Zenlytic require building views first, then exploring within constraints. Scoop answers any question directly through conversation. No semantic layers, no pre-built dashboards, just natural dialogue that investigates problems automatically.</p>
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<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 question, you can use Scoop. Sisense requires learning proprietary interfaces, formulas, and workflows. Zenlytic needs understanding of their specific query syntax. Scoop's AI understands business intent, not technical commands. Zero learning curve means immediate productivity.</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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