<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap" rel="stylesheet">
<style>
section.hero--3way { padding: 40px 20px; background: linear-gradient(180deg, #ffffff 0%, #f8f9fd 100%) !important; }
.hero__container { max-width: 1200px; margin: 0 auto; }
.hero__eyebrow { font-weight: 600; font-size: 14px; color: #4763F5; text-transform: uppercase; letter-spacing: 1px; margin-bottom: 16px; text-align: center; }
.hero__title { font-weight: 600; font-size: 46px; line-height: 1.1; color: #130417; margin-bottom: 24px; text-align: center; }
.hero__scores { display: grid; grid-template-columns: repeat(3, 1fr); gap: 32px; margin-bottom: 32px; }
.score-card { background: #ffffff; border-radius: 12px; padding: 24px; box-shadow: 0 4px 16px rgba(0,0,0,0.08); text-align: center; }
.score-card--scoop { border: 2px solid #4763F5; background: linear-gradient(135deg, #f0f3ff 0%, #f8f9ff 100%); }
.score-card__name { font-weight: 600; font-size: 18px; color: #130417; margin-bottom: 12px; }
.score-card__score { font-weight: 700; font-size: 42px; margin-bottom: 8px; }
.score-card--scoop .score-card__score { color: #4763F5; }
.score-card--competitor .score-card__score { color: #E3165B; }
.score-card__label { font-size: 14px; color: #666; }
.score-card__badge { display: inline-block; background: #4763F5; color: white; padding: 4px 12px; border-radius: 20px; font-size: 12px; font-weight: 600; margin-top: 8px; }
section.content-section { padding: 32px 20px; background: #ffffff !important; }
section.content-section--alt { background: #f8f9fd !important; }
.content-section__container { max-width: 1000px; margin: 0 auto; }
.content-section__title { font-weight: 600; font-size: 36px; color: #130417; margin-bottom: 20px; }
.content-section__subsection { margin-bottom: 32px; }
.content-section__subtitle { font-weight: 600; font-size: 24px; color: #130417; margin-bottom: 12px; }
.content-section__paragraph { font-weight: 400; font-size: 16px; line-height: 1.6; color: #333333; margin-bottom: 12px; }
.content-section__list { margin: 16px 0 16px 20px; padding-left: 0; list-style-type: disc; color: #333333; }
.content-section__list-item { font-weight: 400; font-size: 16px; line-height: 1.6; margin-bottom: 8px; }
.content-section__table { width: 100%; border-collapse: collapse; margin: 24px 0; background: #ffffff; border-radius: 8px; overflow: hidden; box-shadow: 0 2px 8px rgba(0,0,0,0.06); }
.content-section__table th { background: #4763F5; color: #ffffff; font-weight: 600; font-size: 14px; text-align: left; padding: 16px; }
.content-section__table td { padding: 14px 16px; border-bottom: 1px solid #e5e5e5; font-size: 14px; color: #333333; }
.content-section__table tr:last-child td { border-bottom: none; }
.content-section__table tr:hover { background: #f8f9fd; }
.comparison-table--3way th:first-child { width: 30%; }
.comparison-table--3way th:nth-child(2),
.comparison-table--3way th:nth-child(3),
.comparison-table--3way th:nth-child(4) { width: 23.33%; }
.winner-cell { background: #f0fff4; font-weight: 600; color: #333333; }
.loser-cell { background: #fff5f5; color: #333333; }
.scoop-cell { background: #f0f3ff; font-weight: 600; color: #333333; }
section.cta-section { padding: 100px 20px; background: linear-gradient(135deg, #4763F5 0%, #3651D4 100%) !important; text-align: center; color: #ffffff; }
.cta-section__title { font-weight: 600; font-size: 36px; margin-bottom: 16px; }
.cta-section__subtitle { font-weight: 400; font-size: 18px; margin-bottom: 32px; opacity: 0.9; }
.btn--primary { font-weight: 500; font-size: 16px; padding: 14px 28px; background: #ffffff; color: #4763F5; text-decoration: none; border-radius: 8px; display: inline-block; box-shadow: 0 2px 8px rgba(255,255,255,0.3); transition: all 0.2s ease; }
.btn--primary:hover { transform: translateY(-2px); box-shadow: 0 4px 16px rgba(255,255,255,0.4); }
.faq-section { padding: 60px 20px; background: #f8f9fd; }
.faq-section__container { max-width: 900px; margin: 0 auto; }
.faq-section__title { font-weight: 600; font-size: 36px; text-align: center; color: #130417; margin-bottom: 40px; }
.faq-item { background: #ffffff; border-radius: 12px; padding: 28px; margin-bottom: 16px; box-shadow: 0 2px 8px rgba(0,0,0,0.06); }
.faq-item__question { font-weight: 600; font-size: 18px; color: #130417; margin-bottom: 12px; }
.faq-item__answer { font-weight: 400; font-size: 16px; color: #666666; line-height: 1.6; }
</style>
<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">DataGPT vs Tellius 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">Tellius</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 Tellius (22/100) trap users in single-query dashboards. Both competitors require IT support for every new question, blocking the iterative exploration business users need. Choose Scoop for immediate autonomy, competitors only if already invested in their ecosystems.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard tool. Ask questions in plain English, get answers with charts instantly. Works natively in Excel and Slack where you already work. No SQL, no training, no semantic layer maintenance—just conversation with data.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Choose Scoop If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You need multi-pass investigation (3-10 follow-up questions) without IT help</li>
<li class="content-section__list-item">Your team lives in Excel and needs analytics there natively</li>
<li class="content-section__list-item">Business users must explore data independently without SQL or training</li>
<li class="content-section__list-item">You're tired of waiting weeks for dashboard modifications</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider DataGPT If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You only need single-metric KPI monitoring without investigation</li>
<li class="content-section__list-item">Your organization accepts IT dependency for all new questions</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Tellius If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You have existing Tellius infrastructure and switching costs are prohibitive</li>
<li class="content-section__list-item">Your use case is purely automated insights without human exploration</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The 60-point BUA gap reveals a fundamental architectural difference . When business users can actually investigate data themselves, IT transforms from gatekeeper to strategic partner. 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>Tellius</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">22/100</td><td class="">22/100</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>Tellius</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="">Self-Service Query</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">No IT Dependencies</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 18/20 on Autonomy by enabling business users to investigate data through multiple follow-up questions without IT help. Tellius scores 0/20, requiring IT setup for every analysis. DataGPT lacks public documentation for scoring. Scoop's multi-pass investigation capability lets users explore data independently in minutes versus days.</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>Tellius</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Native Communication Integration</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">In-Context Investigation</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Workflow Automation</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</td>
</tr>
<tr>
<td class="">Cross-Platform Consistency</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 17/20 on Flow by enabling complete data investigation directly in Slack and Teams, while DataGPT and Tellius score 0/20 requiring portal visits. Scoop eliminates context-switching between communication and analytics tools, increasing adoption 3x and saving 4 hours weekly per user.</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>Tellius</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="">5/8</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="">2/8</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 offering true natural language conversation, while DataGPT and Tellius score 0/20 with no documented business language capabilities. Scoop lets business users ask questions naturally and maintains context for follow-ups, eliminating the need for technical knowledge or analyst translation.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Presentation (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Presentation</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>DataGPT</th><th>Tellius</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="">6/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Business Language Translation</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Shareability & Integration</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 15/20 on Presentation versus 0/20 for DataGPT and Tellius (not evaluated). Scoop automatically generates business-ready narratives with embedded charts, eliminating the export-reformat cycle that consumes 40% of analyst time. Traditional BI platforms require manual formatting and template selection.</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>Tellius</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="">Automatic Table Relationships</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</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="">Multi-source Joins</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Zero Maintenance</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on data access by connecting directly to databases without IT setup. DataGPT and Tellius both score 0/20, requiring extensive data preparation and semantic layer maintenance. Scoop enables instant analysis across multiple sources while competitors need days of data modeling 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 suddenly drops 15%, the difference between knowing it happened and understanding why separates great companies from struggling ones. Traditional BI shows you the drop on a dashboard. Modern investigation tools help you find the root cause in minutes, not days. The critical question: can business users investigate independently, or do they need IT to build new queries for every follow-up question? This capability determines whether insights arrive in time to act.</p>
<p class="content-section__paragraph">The fundamental architecture difference explains everything. DataGPT operates on a single-query model—you ask once, get one answer, then start over. No investigation thread. No context retention. Tellius offers guided analytics with some follow-up capability, but users must navigate predefined investigation paths. Scoop treats investigation as conversation. Ask 'Why did sales drop?' and it automatically checks seasonality, segments, correlations, and anomalies. Then you naturally follow up: 'What about competitor pricing?' or 'Show me affected customers.' Each query builds on previous context. DataGPT would require 5-7 separate queries, each starting from scratch. Tellius would need IT to configure investigation workflows first. The difference compounds with complexity. A typical root cause investigation involves 3-10 related queries. Scoop maintains context throughout. DataGPT forces users to manually track connections between queries. Tellius requires switching between different modules. Time impact is dramatic: 3 minutes versus 30 minutes for the same investigation.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning: Regional VP notices Southeast revenue dropped 20% last month. With Scoop, she types: 'Why did Southeast revenue drop in October?' Scoop automatically investigates—checking historical patterns, comparing to other regions, analyzing product mix, identifying top customer changes. It surfaces that three enterprise accounts delayed renewals. She follows up: 'Why did they delay?' Scoop correlates support tickets, usage patterns, and competitor activity, revealing a product issue affecting specific use cases. Total time: 4 minutes. With DataGPT, she'd need separate queries for each analysis, manually connecting dots. With Tellius, she'd navigate through predefined diagnostic workflows, hoping IT configured the right investigation path. If not, she files a ticket and waits days for answers.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability isn't about having AI or natural language—it's about architecture. Single-query systems like DataGPT can't support true investigation. Guided analytics like Tellius still require IT setup. Only conversational architecture enables business users to investigate independently, following their intuition wherever it leads. For organizations serious about autonomous business intelligence, this difference is decisive.</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 export, copy-paste, reformat, and email. This 4-hour ritual costs enterprises millions in productivity. The real question isn't whether platforms connect to Excel—it's whether Excel users can get answers without leaving their spreadsheet. Modern platforms promise 'Excel integration,' but the difference between a native add-in with live chat and a CSV export button determines whether your team saves 3 hours or 3 minutes per report.</p>
<p class="content-section__paragraph">The architecture divide is stark. DataGPT treats Excel as a destination—you export results after analysis in their web portal. This forces context switching that breaks analytical flow. Users must master two tools instead of one. Tellius offers an Excel plugin, but it's essentially a query builder that pushes data one-way. You can't have a conversation or investigate patterns naturally. Scoop embeds a full AI analyst inside Excel. Users type questions in plain English directly in their spreadsheet. 'Why did margins drop in Q3?' triggers the same multi-step investigation as the web interface. The data updates live. Your formatting stays intact. Most critically, Scoop understands Excel context. Select a range and ask 'What's driving these outliers?' It knows what you're looking at. This isn't about features—it's about respecting where 400 million users actually work. The average analyst spends 5.5 hours weekly moving data between BI tools and Excel. Native integration eliminates this entirely.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to update the monthly board deck. Previously: Export from Tableau (5 min), paste into Excel template (10 min), reformat (15 min), realize she needs prior year comparison, back to Tableau (5 min), export again, merge data (10 min), format charts (20 min). Total: 65 minutes. With Scoop's Excel add-in: Opens her template, types 'Add prior year comparison to this revenue data' in the Scoop panel. Data appears with formatting preserved. Asks 'What drove the biggest variance?' Gets instant analysis with charts. Selects unusual spike, asks 'What caused this?' Scoop investigates, finds new customer segment. Total: 8 minutes. The board deck that took an hour now takes the time of a coffee break. Sarah investigates deeper because friction disappeared.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration reveals platform philosophy. DataGPT and Tellius bolt Excel onto their architecture as an afterthought—forcing users to choose between powerful analysis or familiar tools. Scoop brings the full AI analyst into Excel, eliminating the false choice. For the 750 million Excel users worldwide, this isn't a feature. It's the difference between adoption and abandonment. When analysis happens where people already work, everything changes.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">Business decisions rarely happen in isolation. When executives ask 'What happens if we raise prices 10% versus expanding to new markets?', they need to compare multiple scenarios simultaneously. This isn't about running one analysis then another—it's about seeing alternatives side-by-side to make informed choices. Traditional BI forces sequential analysis, making comparisons tedious. Modern platforms should enable parallel scenario exploration, letting decision-makers see trade-offs instantly. The ability to compare 'what-if' scenarios in real-time separates strategic planning tools from basic reporting systems.</p>
<p class="content-section__paragraph">The architectural difference is fundamental. DataGPT processes one question at a time, requiring users to manually track and compare results across multiple queries. Want to compare three pricing strategies? That's three separate conversations, three sets of results to mentally juggle. Tellius offers scenario planning but requires pre-configuration of variables and ranges—business users can't spontaneously explore new scenarios without IT setup. Scoop treats scenario comparison as a native conversation pattern. Ask 'Compare revenue impact of 10% price increase versus 20% volume growth,' and Scoop maintains both scenarios in parallel, automatically generating comparative visualizations. The key innovation is context persistence. While DataGPT loses context between queries, Scoop maintains all scenarios within the conversation thread. Users can say 'Now add a third scenario with both changes' without restating everything. This conversational memory transforms scenario analysis from a documentation exercise into natural exploration. Tellius's template approach seems efficient until requirements change mid-analysis. Business questions evolve during investigation, and rigid templates become constraints rather than accelerators.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CPG company's pricing team needs to evaluate three strategies before a retailer negotiation tomorrow. The analyst opens Scoop and types: 'Compare three scenarios for next quarter: 1) 8% price increase, 2) 15% volume discount, 3) status quo with 2% inflation adjustment.' Scoop instantly generates parallel projections showing revenue, margin, and market share impacts. The CFO joins the Slack thread and asks: 'What if we combine scenario 1 and 2 for large accounts only?' Scoop adjusts without losing the original comparison. The team identifies that scenario 1 works for premium products while scenario 2 suits value lines. Total analysis time: 15 minutes. In DataGPT, this would require six separate queries, manual Excel compilation, and at least an hour of work. Tellius would need IT to configure the scenario parameters first, delaying the analysis by days.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Side-by-side scenario analysis reveals whether a platform enables true business agility or just faster reporting. DataGPT and Tellius treat scenarios as separate queries or rigid templates, forcing manual comparison and limiting spontaneous exploration. Scoop's conversational memory and parallel processing let teams explore strategies naturally, reducing hours of analysis to minutes of conversation. For organizations where strategic agility matters, this capability gap directly impacts competitiveness.</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 revenue opportunities, and reveal operational inefficiencies. But here's the problem: traditional ML requires data scientists, months of model training, and complex deployment pipelines. Modern platforms promise 'automatic ML' but deliver vastly different experiences. Some require you to configure algorithms and parameters. Others run pattern discovery automatically on every query. The difference determines whether your business users can actually uncover insights or just admire ML features they'll never use. Let's examine how DataGPT, Tellius, and Scoop handle the critical task of making machine learning accessible to business users.</p>
<p class="content-section__paragraph">The fundamental divide in ML platforms isn't about algorithms—it's about accessibility. DataGPT requires users to explicitly request pattern analysis, turning ML into a separate workflow. You ask a question, get an answer, then remember to click 'find patterns.' That's two steps where there should be one. Tellius offers sophisticated AutoML but wraps it in configuration screens. Users select target variables, choose algorithms, and set parameters. It's powerful for data scientists but intimidating for sales managers. Scoop takes a different approach: ML runs automatically on every query. Ask 'What drives customer churn?' and Scoop automatically analyzes correlations, identifies patterns, and surfaces anomalies. No configuration. No separate workflow. The technical sophistication happens invisibly. This architectural difference shows in adoption rates. Tellius customers report 15-20% of users engaging with ML features after training. DataGPT sees similar patterns—ML becomes a specialized tool for power users. Scoop users don't even know they're using ML. They ask business questions and get answers enriched with patterns, predictions, and anomalies. The ML is invisible, which makes it universal. The real test is the Monday morning question: 'Why did this metric spike?' DataGPT makes you run the analysis manually. Tellius requires navigating to AutoML mode. Scoop just answers, with patterns included.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns and needs to understand what's happening. With Scoop, she types: 'Analyze inventory anomalies for last quarter.' Scoop automatically identifies three unusual patterns: seasonal items ordered off-season, a 40% spike in returns for one product line, and correlation between weather events and specific SKU movements. Total time: 45 seconds. With Tellius, she navigates to the AutoML workspace, selects inventory as her target metric, configures anomaly detection parameters, runs the analysis, then interprets technical output about standard deviations and confidence intervals. Time: 15-20 minutes plus training. DataGPT requires her to first query inventory levels, manually spot unusual patterns, then request specific statistical analysis for each suspected anomaly. Time: 30+ minutes if she knows what to look for. The Scoop user discovers the weather correlation immediately. The Tellius user might find it after configuration. The DataGPT user probably misses it entirely unless she specifically thinks to check weather data.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI platforms splits into two camps: those that make ML a separate technical workflow and those that embed it invisibly into every analysis. DataGPT and Tellius deliver powerful ML capabilities that most business users will never touch. Scoop makes ML invisible and automatic, which paradoxically makes it more powerful—because it actually gets used. When pattern discovery requires zero configuration and zero technical knowledge, every user becomes a data scientist without knowing it.</p>
</div>
<div class="content-section__subsection">
<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 teams need answers where they work—in Excel during budget planning, in Slack during strategy discussions, on phones during client meetings. The real test isn't whether a platform has mobile access, but whether business users can actually investigate problems from anywhere. Let's examine how each platform handles the reality of distributed work, where 73% of data questions arise outside dedicated BI tools and where switching contexts kills productivity.</p>
<p class="content-section__paragraph">The workflow integration battle reveals a fundamental divide: dashboard-centric versus investigation-centric architecture. DataGPT and Tellius treat integrations as notification channels—you get alerts in Slack but must return to their portal for real analysis. This creates the 'portal prison' effect where users constantly context-switch. Scoop's chat-based architecture translates naturally across channels. In Excel, you're not just viewing data—you're investigating. Type 'Why did margins drop?' directly in your spreadsheet and get answers with charts. In Slack, the full investigation power travels with you. A CEO can ask follow-up questions from their phone without learning new interfaces. The mobile experience highlights this perfectly. DataGPT and Tellius mobile apps are essentially dashboard viewers with limited interactivity. Scoop's chat interface works identically on mobile—every investigation capability remains available. However, Scoop lacks offline capability, which both competitors partially support through cached dashboards. For API integration, Tellius leads with both GraphQL and REST options, while DataGPT and Scoop offer REST only. But raw API capability matters less than workflow philosophy. The question isn't 'Can I integrate?' but 'Can business users actually work in their preferred tools?'</p>
<p class="content-section__paragraph"><strong>Example</strong>: A VP of Sales is in Excel finalizing quarterly forecasts when she notices unusual patterns in enterprise deal sizes. With Scoop's Excel add-in, she types directly in a sidebar: 'Compare average enterprise deal sizes Q3 vs Q2 by region.' Charts appear in seconds. She follows up: 'Which accounts drove the Northeast decline?' Then: 'Show me these accounts' renewal dates.' Total investigation time: 4 minutes without leaving Excel. Her team discusses findings in Slack, where she shares the Scoop thread. A colleague asks a follow-up question directly in Slack, getting new charts instantly. With DataGPT, she would export data to Excel, losing investigation capability. The Slack integration only sends alerts—real analysis requires opening DataGPT's web portal. Tellius offers better Slack integration but still requires portal access for complex investigations. The VP would need 15-20 minutes and three different applications to reach the same insights.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop brings full investigation power to where business users actually work—Excel and Slack—while competitors force constant returns to their portals. This isn't about feature checkboxes; it's about eliminating the productivity tax of context switching. When 73% of data questions arise outside BI tools, platforms that trap analysis in dedicated portals create friction that compounds into hours of lost productivity weekly.</p>
</div>
</div>
</section>
<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 Tellius which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: DataGPT or Tellius?</h3>
<p class="content-section__paragraph">Neither empowers business users effectively. DataGPT scores 22/100 on Business User Autonomy, Tellius also 22/100. Both require IT support, semantic layers, and training. Scoop scores 82/100, letting business users analyze data independently. For true business autonomy, Scoop's chat interface beats both dashboard-centric approaches.</p>
</div>
<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 on dashboards but can't investigate why they happened. You see the spike but need IT to dig deeper. Scoop automatically runs 3-10 follow-up queries to find root causes. Ask 'why did sales drop?' and Scoop investigates across dimensions automatically.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Tellius do root cause analysis automatically?</h3>
<p class="content-section__paragraph">Tellius offers automated insights but within pre-configured boundaries. IT must set up the semantic layer first. Scoop performs true root cause analysis through multi-pass investigation, testing hypotheses automatically. No configuration needed—just ask 'why' and Scoop explores all relevant factors in your data.</p>
</div>
<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 analysis, automatically chaining 3-10 queries to answer complex questions. DataGPT and Tellius show single dashboard views. Scoop investigates like a human analyst would—exploring hypotheses, checking correlations, finding patterns. This investigation approach uncovers insights dashboards miss entirely.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What does DataGPT really cost including implementation?</h3>
<p class="content-section__paragraph">DataGPT's true cost includes licenses, 3-6 month implementation, training programs, semantic layer maintenance, consultants, and lost productivity. Total typically reaches 5-10x the license fee. Scoop eliminates implementation, training, maintenance, and consultant costs—just a subscription. This reduces total cost of ownership by 90%.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Are there hidden fees with Tellius?</h3>
<p class="content-section__paragraph">Tellius requires professional services for setup, ongoing semantic layer maintenance, user training, and often dedicated consultants. These typically add 3-5x the license cost annually. Scoop has no hidden fees—no implementation, no training, no maintenance. Just connect and start asking questions immediately.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">How long does it take to learn DataGPT?</h3>
<p class="content-section__paragraph">DataGPT requires 2-4 weeks of training for business users, plus ongoing support. Users must understand the semantic layer and dashboard navigation. Scoop requires zero training—if you can type a question, you're ready. Business users become productive in minutes, not weeks, with no learning curve.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Tellius?</h3>
<p class="content-section__paragraph">Tellius claims no SQL required, but complex queries need IT support or SQL knowledge. The semantic layer abstracts SQL but limits flexibility. Scoop truly eliminates SQL—just ask questions naturally. The AI handles all query complexity, letting business users explore data without technical knowledge whatsoever.</p>
</div>
<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 directly to data sources in 30 seconds and start analyzing immediately. DataGPT and Tellius require IT for setup, semantic layer configuration, and maintenance. Scoop's 82/100 Business User Autonomy score reflects true independence—no IT tickets, no waiting, just answers.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Is DataGPT easier to use than Tellius?</h3>
<p class="content-section__paragraph">Both score identically low on usability—22/100 on Business User Autonomy. DataGPT requires dashboard navigation, Tellius needs semantic layer understanding. Neither supports true natural language queries. Scoop's chat interface is fundamentally different—type questions like you'd ask a colleague, get answers with charts immediately.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">How is Scoop different from traditional BI tools?</h3>
<p class="content-section__paragraph">Traditional BI tools like DataGPT and Tellius are dashboard factories requiring IT support. Scoop is an AI analyst you chat with—no dashboards, no semantic layers, no training. Ask questions, get answers. This investigation approach finds insights that dashboard-first tools miss completely.</p>
</div>
<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 conversation, not technical interfaces. If you can use ChatGPT or send an email, you can use Scoop. DataGPT and Tellius require learning their specific interfaces, semantic models, and limitations. Scoop's AI understands intent, handles complexity, and guides users through analysis naturally.</p>
</div>
<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 or using separate interfaces. No native Excel integration exists. Scoop works directly inside Excel—highlight data, ask questions, get answers without leaving your spreadsheet. This native integration means zero workflow disruption. Tellius also lacks direct Excel integration, requiring exports and imports.</p>
</div>
</div>
</section>
<section class="cta-section">
<div class="cta-section__title">See Scoop in Action</div>
<div class="cta-section__subtitle">
Join 500+ companies using Scoop to democratize data investigation
</div>
<a href="https://www.scoopanalytics.com/book-demo" class="btn--primary">Book Your Demo</a>
</section>
