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The promise sounds almost too good to be true: drop a messy comma separated values (CSV) file into an AI agent, wait two minutes, and get back a polished, interactive chart ready for your next board presentation.\u00a0<\/p>\n\n\n\n
But that\u2019s exactly what Chinese startup Manus.im is delivering with its latest data visualization feature, launched this month.<\/p>\n\n\n\n
Unfortunately, my initial hands-on testing with corrupted datasets reveals a fundamental enterprise problem: impressive capabilities paired with insufficient transparency about data transformations. While Manus handles messy data better than ChatGPT, neither tool is yet ready for boardroom-ready slides.<\/p>\n\n\n\n
The spreadsheet problem plaguing enterprise analytics<\/strong><\/h2>\n\n\n\n
Rossums\u2019 survey of 470 finance leaders found 58% still rely primarily on Excel for monthly KPIs, despite owning BI licenses. Another TechRadar study estimates that overall spreadsheet dependence affects roughly 90% of organizations \u2014 creating a \u201clast-mile data problem\u201d between governed warehouses and hasty CSV exports that land in analysts\u2019 inboxes hours before critical meetings.<\/p>\n\n\n\n
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The AI Impact Series Returns to San Francisco – August 5<\/strong><\/p>\n\n\n\n
The next phase of AI is here – are you ready? Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation.<\/p>\n\n\n\n
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Manus targets this exact gap. Upload your CSV, describe what you want in natural language, and the agent automatically cleans the data, selects the appropriate Vega-Lite grammar and returns a PNG chart ready for export\u2014no pivot tables required.<\/p>\n\n\n\n
Where Manus beats ChatGPT: 4x slower but more accurate with messy data<\/strong><\/h2>\n\n\n\n
I tested both Manus and ChatGPT\u2019s Advanced Data Analysis using three datasets (113k-row ecommerce orders, 200k-row marketing funnel 10k-row SaaS MRR), first clean, then corrupted with 5% error injection including nulls, mixed-format dates and duplicates.\u00a0<\/p>\n\n\n\n
For example, testing the same prompt \u2014 \"Show me a month-by-month revenue trend for the past year and highlight any unusual spikes or dips\" \u2014 across clean and corrupted 113k-row e-commerce data revealed some stark differences.<\/em><\/code><\/pre>\n\n\n\nTool<\/strong><\/td> Data Quality<\/strong><\/td> Time<\/strong><\/td> Cleans Nulls<\/strong><\/td> Parses Dates<\/strong><\/td> Handles Duplicates<\/strong><\/td> Comments<\/strong><\/td><\/tr> Manus<\/strong><\/td> Clean<\/td> 1:46<\/td> N\/A<\/td> \u2713<\/td> N\/A<\/td> Correct trend, standard presentation, but incorrect numbers<\/td><\/tr> Manus<\/strong><\/td> Messy<\/td> 3:53<\/td> \u2713<\/td> \u2713<\/td> \u2717<\/td> Correct trend despite inaccurate data\u00a0<\/td><\/tr> ChatGPT<\/strong><\/td> Clean<\/td> 0:57<\/td> N\/A<\/td> \u2713<\/td> N\/A<\/td> Fast, but incorrect visualisation<\/td><\/tr> ChatGPT<\/strong><\/td> Messy<\/td> 0:59<\/td> \u2717<\/td> \u2717<\/td> \u2717<\/td> Incorrect trend from unclean data<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nFor context: DeepSeek could only handle 1% of the file size, while Claude and Grok took over 5 minutes each but produced interactive charts without PNG export options.<\/p>\n\n\n\n Outputs:<\/p>\n\n\n\n <\/figure>\n\n\n\n<\/figure>\n\n\n\nFigure 1-2: Chart outputs from the same revenue trend prompt on messy e-commerce data. Manus (bottom) produces a coherent trend despite data corruption, while ChatGPT (top) shows distorted patterns from unclean date formatting. <\/em><\/p>\n\n\n\n Manus behaves like a cautious junior analyst<\/strong> \u2014 automatically tidying data before charting, successfully parsing date inconsistencies and handling nulls without explicit instructions. When I requested the same revenue trend analysis on corrupted data, Manus took nearly 4 minutes but produced a coherent visualization despite the data quality issues.<\/p>\n\n\n\n ChatGPT operates like a speed coder<\/strong> \u2014 prioritizing fast output over data hygiene. The same request took just 59 seconds but produced misleading visualizations because it didn\u2019t automatically clean formatting inconsistencies.<\/p>\n\n\n\n However, both tools failed in terms of \u201cexecutive readiness.\u201d Neither produced board-ready axis scaling or readable labels without follow-up prompts. Data labels were frequently overlapping or too small, bar charts lacked proper gridlines and number formatting was inconsistent.<\/p>\n\n\n\n The transparency crisis enterprises can\u2019t ignore<\/strong><\/h2>\n\n\n\nHere\u2019s where Manus becomes problematic for enterprise adoption: the agent never surfaces cleaning steps it applies<\/strong>. An auditor reviewing the final chart has no way to confirm whether outliers were dropped, imputed or transformed.<\/p>\n\n\n\n When a CFO presents quarterly results based on a Manus-generated chart, what happens when someone asks, \u201cHow did you handle the duplicate transactions from the Q2 system integration?\u201d The answer is silence.<\/p>\n\n\n\n ChatGPT, Claude and Grok all show their Python code, though transparency through code review isn\u2019t scalable for business users lacking programming experience. What enterprises need is a simpler audit trail, which builds trust.<\/p>\n\n\n\n Warehouse-native AI is racing ahead<\/strong><\/h2>\n\n\n\nWhile Manus focuses on CSV uploads, major platforms are building chart generation directly into enterprise data infrastructure:<\/p>\n\n\n\n Google\u2019s Gemini in BigQuery<\/strong> became generally available in August 2024, enabling the generation of SQL queries and inline visualizations on live tables while respecting row-level security.<\/p>\n\n\n\n Microsoft\u2019s Copilot in Fabric<\/strong> reached GA in the Power BI experience in May 2024, creating visuals inside Fabric notebooks while working directly with Lakehouse datasets.\u00a0<\/p>\n\n\n\n GoodData\u2019s AI Assistant<\/strong>, launched in June 2025, operates within customer environments and respects existing semantic models, allowing users to ask questions in plain language while receiving answers that align with predefined metrics and business terms.<\/p>\n\n\n\n These warehouse-native solutions eliminate CSV exports entirely, preserve complete data lineage and leverage existing security models \u2014 advantages file-upload tools like Manus struggle to match.<\/p>\n\n\n\n Critical gaps for enterprise adoption<\/strong><\/h2>\n\n\n\nMy testing revealed several blockers:<\/p>\n\n\n\n Live data connectivity<\/strong> remains absent \u2014 Manus supports file uploads only, with no Snowflake, BigQuery or S3 connectors. Manus.im says connectors are \u201con the roadmap\u201d but offers no timeline.<\/p>\n\n\n\n Audit trail transparency<\/strong> is completely missing. Enterprise data teams need transformation logs showing exactly how AI cleaned their data and whether its interpretation of the fields are correct.<\/p>\n\n\n\n Export flexibility<\/strong> is limited to PNG outputs. While adequate for quick slide decks, enterprises need customizable, interactive export options.<\/p>\n\n\n\n The verdict: impressive tech, premature for enterprise use cases\u00a0<\/strong><\/h2>\n\n\n\nFor SMB executives drowning in ad-hoc CSV analysis, Manus\u2019s drag-and-drop visualisation seems to be doing the job.\u00a0<\/p>\n\n\n\n The autonomous data cleaning handles real-world messiness that would otherwise require manual preprocessing, cutting turnaround from hours to minutes when you have reasonably complete data.\u00a0<\/p>\n\n\n\n Additionally, it offers a significant runtime advantage over Excel or Google Sheets, which require manual pivots and incur substantial load times due to local compute power limitations.<\/p>\n\n\n\n But regulated enterprises with governed data lakes should wait for warehouse-native agents like Gemini or Fabric Copilot, which keep data inside security perimeters and maintain complete lineage tracking.<\/p>\n\n\n\n Bottom line:<\/strong> Manus proves one-prompt charting works and handles messy data impressively. But for enterprises, the question isn\u2019t whether the charts look good \u2014 it\u2019s whether you can stake your career on data transformations you can\u2019t audit or verify. Until AI agents can plug directly into governed tables with rigorous audit trails, Excel will continue to hold its starring role in quarterly presentations.<\/p>\n \n\nDaily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n Read our Privacy Policy<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n \n\t\t\t\t\t\n\t\t\t\t<\/div>\n<\/p><\/div>\n<\/div>\t\t\t<\/div>\r\n \r\n Source link <\/a>","protected":false},"excerpt":{"rendered":"Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now The promise sounds almost too good to be true: drop a messy comma separated values (CSV) file into an AI agent, wait two minutes, and get back a polished, […]<\/p>\n","protected":false},"author":1,"featured_media":2688,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[33],"tags":[],"class_list":["post-2687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-automation"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/violethoward.com\/new\/wp-content\/uploads\/2025\/07\/Gemini_Generated_Image_nago9hnago9hnago.jpeg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts\/2687","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/comments?post=2687"}],"version-history":[{"count":0,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts\/2687\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/media\/2688"}],"wp:attachment":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/media?parent=2687"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/categories?post=2687"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/tags?post=2687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Tool<\/strong><\/td>	Data Quality<\/strong><\/td>	Time<\/strong><\/td>	Cleans Nulls<\/strong><\/td>	Parses Dates<\/strong><\/td>	Handles Duplicates<\/strong><\/td>	Comments<\/strong><\/td><\/tr>
Manus<\/strong><\/td>	Clean<\/td>	1:46<\/td>	N\/A<\/td>	\u2713<\/td>	N\/A<\/td>	Correct trend, standard presentation, but incorrect numbers<\/td><\/tr>
Manus<\/strong><\/td>	Messy<\/td>	3:53<\/td>	\u2713<\/td>	\u2713<\/td>	\u2717<\/td>	Correct trend despite inaccurate data\u00a0<\/td><\/tr>
ChatGPT<\/strong><\/td>	Clean<\/td>	0:57<\/td>	N\/A<\/td>	\u2713<\/td>	N\/A<\/td>	Fast, but incorrect visualisation<\/td><\/tr>
ChatGPT<\/strong><\/td>	Messy<\/td>	0:59<\/td>	\u2717<\/td>	\u2717<\/td>	\u2717<\/td>	Incorrect trend from unclean data<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n For context: DeepSeek could only handle 1% of the file size, while Claude and Grok took over 5 minutes each but produced interactive charts without PNG export options.<\/p>\n\n\n\n Outputs:<\/p>\n\n\n\n $\"\"\/$ <\/figure>\n\n\n\n $\"\"\/$ <\/figure>\n\n\n\n Figure 1-2: Chart outputs from the same revenue trend prompt on messy e-commerce data. Manus (bottom) produces a coherent trend despite data corruption, while ChatGPT (top) shows distorted patterns from unclean date formatting. <\/em><\/p>\n\n\n\n Manus behaves like a cautious junior analyst<\/strong> \u2014 automatically tidying data before charting, successfully parsing date inconsistencies and handling nulls without explicit instructions. When I requested the same revenue trend analysis on corrupted data, Manus took nearly 4 minutes but produced a coherent visualization despite the data quality issues.<\/p>\n\n\n\n ChatGPT operates like a speed coder<\/strong> \u2014 prioritizing fast output over data hygiene. The same request took just 59 seconds but produced misleading visualizations because it didn\u2019t automatically clean formatting inconsistencies.<\/p>\n\n\n\n However, both tools failed in terms of \u201cexecutive readiness.\u201d Neither produced board-ready axis scaling or readable labels without follow-up prompts. Data labels were frequently overlapping or too small, bar charts lacked proper gridlines and number formatting was inconsistent.<\/p>\n\n\n\n The transparency crisis enterprises can\u2019t ignore<\/strong><\/h2>\n\n\n\n Here\u2019s where Manus becomes problematic for enterprise adoption: the agent never surfaces cleaning steps it applies<\/strong>. An auditor reviewing the final chart has no way to confirm whether outliers were dropped, imputed or transformed.<\/p>\n\n\n\n When a CFO presents quarterly results based on a Manus-generated chart, what happens when someone asks, \u201cHow did you handle the duplicate transactions from the Q2 system integration?\u201d The answer is silence.<\/p>\n\n\n\n ChatGPT, Claude and Grok all show their Python code, though transparency through code review isn\u2019t scalable for business users lacking programming experience. What enterprises need is a simpler audit trail, which builds trust.<\/p>\n\n\n\n Warehouse-native AI is racing ahead<\/strong><\/h2>\n\n\n\n While Manus focuses on CSV uploads, major platforms are building chart generation directly into enterprise data infrastructure:<\/p>\n\n\n\n Google\u2019s Gemini in BigQuery<\/strong> became generally available in August 2024, enabling the generation of SQL queries and inline visualizations on live tables while respecting row-level security.<\/p>\n\n\n\n Microsoft\u2019s Copilot in Fabric<\/strong> reached GA in the Power BI experience in May 2024, creating visuals inside Fabric notebooks while working directly with Lakehouse datasets.\u00a0<\/p>\n\n\n\n GoodData\u2019s AI Assistant<\/strong>, launched in June 2025, operates within customer environments and respects existing semantic models, allowing users to ask questions in plain language while receiving answers that align with predefined metrics and business terms.<\/p>\n\n\n\n These warehouse-native solutions eliminate CSV exports entirely, preserve complete data lineage and leverage existing security models \u2014 advantages file-upload tools like Manus struggle to match.<\/p>\n\n\n\n Critical gaps for enterprise adoption<\/strong><\/h2>\n\n\n\n My testing revealed several blockers:<\/p>\n\n\n\n Live data connectivity<\/strong> remains absent \u2014 Manus supports file uploads only, with no Snowflake, BigQuery or S3 connectors. Manus.im says connectors are \u201con the roadmap\u201d but offers no timeline.<\/p>\n\n\n\n Audit trail transparency<\/strong> is completely missing. Enterprise data teams need transformation logs showing exactly how AI cleaned their data and whether its interpretation of the fields are correct.<\/p>\n\n\n\n Export flexibility<\/strong> is limited to PNG outputs. While adequate for quick slide decks, enterprises need customizable, interactive export options.<\/p>\n\n\n\n The verdict: impressive tech, premature for enterprise use cases\u00a0<\/strong><\/h2>\n\n\n\n For SMB executives drowning in ad-hoc CSV analysis, Manus\u2019s drag-and-drop visualisation seems to be doing the job.\u00a0<\/p>\n\n\n\n The autonomous data cleaning handles real-world messiness that would otherwise require manual preprocessing, cutting turnaround from hours to minutes when you have reasonably complete data.\u00a0<\/p>\n\n\n\n Additionally, it offers a significant runtime advantage over Excel or Google Sheets, which require manual pivots and incur substantial load times due to local compute power limitations.<\/p>\n\n\n\n But regulated enterprises with governed data lakes should wait for warehouse-native agents like Gemini or Fabric Copilot, which keep data inside security perimeters and maintain complete lineage tracking.<\/p>\n\n\n\n Bottom line:<\/strong> Manus proves one-prompt charting works and handles messy data impressively. But for enterprises, the question isn\u2019t whether the charts look good \u2014 it\u2019s whether you can stake your career on data transformations you can\u2019t audit or verify. Until AI agents can plug directly into governed tables with rigorous audit trails, Excel will continue to hold its starring role in quarterly presentations.<\/p>\n \n \n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n Read our Privacy Policy<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n \n\t\t\t\t\t $\"\"\/$ \n\t\t\t\t<\/div>\n<\/p><\/div>\n<\/div>\t\t\t<\/div>\r\n \r\n Source link <\/a>","protected":false},"excerpt":{"rendered":" Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now The promise sounds almost too good to be true: drop a messy comma separated values (CSV) file into an AI agent, wait two minutes, and get back a polished, […]<\/p>\n","protected":false},"author":1,"featured_media":2688,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[33],"tags":[],"class_list":["post-2687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-automation"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/violethoward.com\/new\/wp-content\/uploads\/2025\/07\/Gemini_Generated_Image_nago9hnago9hnago.jpeg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts\/2687","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/comments?post=2687"}],"version-history":[{"count":0,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/posts\/2687\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/media\/2688"}],"wp:attachment":[{"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/media?parent=2687"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/categories?post=2687"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/violethoward.com\/new\/wp-json\/wp\/v2\/tags?post=2687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}