Chinese startup Manus challenges ChatGPT in data visualization: which should enterprises use?

The promise sounds nearly too good to be true: drop a messy comma separated values (CSV) file into an AI agent, wait two minutes, and get again a refined, interactive chart prepared in your subsequent board presentation. 

However that’s precisely what Chinese language startup Manus.im is delivering with its newest knowledge visualization function, launched this month.

Sadly, my preliminary hands-on testing with corrupted datasets reveals a elementary enterprise drawback: spectacular capabilities paired with inadequate transparency about knowledge transformations. Whereas Manus handles messy knowledge higher than ChatGPT, neither instrument is but prepared for boardroom-ready slides.

The spreadsheet drawback plaguing enterprise analytics

Rossums’ survey of 470 finance leaders discovered 58% nonetheless rely totally on Excel for month-to-month KPIs, regardless of proudly owning BI licenses. One other TechRadar examine estimates that total spreadsheet dependence impacts roughly 90% of organizations — making a “last-mile knowledge drawback” between ruled warehouses and hasty CSV exports that land in analysts’ inboxes hours earlier than important conferences.

Manus targets this actual hole. Add your CSV, describe what you need in pure language, and the agent robotically cleans the information, selects the suitable Vega-Lite grammar and returns a PNG chart prepared for export—no pivot tables required.

The place Manus beats ChatGPT: 4x slower however extra correct with messy knowledge

I examined each Manus and ChatGPT’s Superior Information Evaluation utilizing three datasets (113k-row ecommerce orders, 200k-row marketing funnel 10k-row SaaS MRR), first clear, then corrupted with 5% error injection together with nulls, mixed-format dates and duplicates. 

For instance, testing the identical immediate — "Present me a month-by-month income development for the previous 12 months and spotlight any uncommon spikes or dips" — throughout clear and corrupted 113k-row e-commerce knowledge revealed some stark variations.

Device	Information High quality	Time	Cleans Nulls	Parses Dates	Handles Duplicates	Feedback
Manus	Clear	1:46	N/A	✓	N/A	Appropriate development, commonplace presentation, however incorrect numbers
Manus	Messy	3:53	✓	✓	✗	Appropriate development regardless of inaccurate knowledge
ChatGPT	Clear	0:57	N/A	✓	N/A	Quick, however incorrect visualisation
ChatGPT	Messy	0:59	✗	✗	✗	Incorrect development from unclean knowledge

For context: DeepSeek may solely deal with 1% of the file measurement, whereas Claude and Grok took over 5 minutes every however produced interactive charts with out PNG export choices.

Outputs:

Determine 1-2: Chart outputs from the identical income development immediate on messy e-commerce knowledge. Manus (backside) produces a coherent development regardless of knowledge corruption, whereas ChatGPT (high) reveals distorted patterns from unclean date formatting.

Manus behaves like a cautious junior analyst — robotically tidying knowledge earlier than charting, efficiently parsing date inconsistencies and dealing with nulls with out express directions. After I requested the identical income development evaluation on corrupted knowledge, Manus took practically 4 minutes however produced a coherent visualization regardless of the information high quality points.

ChatGPT operates like a velocity coder — prioritizing quick output over knowledge hygiene. The identical request took simply 59 seconds however produced deceptive visualizations as a result of it didn’t robotically clear formatting inconsistencies.

Nonetheless, each instruments failed when it comes to “govt readiness.” Neither produced board-ready axis scaling or readable labels with out follow-up prompts. Information labels had been ceaselessly overlapping or too small, bar charts lacked correct gridlines and quantity formatting was inconsistent.

The transparency disaster enterprises can’t ignore

Right here’s the place Manus turns into problematic for enterprise adoption: the agent by no means surfaces cleansing steps it applies. An auditor reviewing the ultimate chart has no solution to verify whether or not outliers had been dropped, imputed or reworked.

When a CFO presents quarterly outcomes based mostly on a Manus-generated chart, what occurs when somebody asks, “How did you deal with the duplicate transactions from the Q2 system integration?” The reply is silence.

ChatGPT, Claude and Grok all present their Python code, although transparency by code evaluate isn’t scalable for enterprise customers missing programming expertise. What enterprises want is a less complicated audit path, which builds belief.

Warehouse-native AI is racing forward

Whereas Manus focuses on CSV uploads, main platforms are constructing chart era straight into enterprise knowledge infrastructure:

Google’s Gemini in BigQuery grew to become usually obtainable in August 2024, enabling the era of SQL queries and inline visualizations on dwell tables whereas respecting row-level safety.

Microsoft’s Copilot in Material reached GA within the Energy BI expertise in Could 2024, creating visuals inside Material notebooks whereas working straight with Lakehouse datasets. 

GoodData’s AI Assistant, launched in June 2025, operates inside buyer environments and respects present semantic fashions, permitting customers to ask questions in plain language whereas receiving solutions that align with predefined metrics and enterprise phrases.

These warehouse-native options remove CSV exports solely, protect full knowledge lineage and leverage present safety fashions — benefits file-upload instruments like Manus battle to match.

Vital gaps for enterprise adoption

My testing revealed a number of blockers:

Reside knowledge connectivity stays absent — Manus helps file uploads solely, with no Snowflake, BigQuery or S3 connectors. Manus.im says connectors are “on the roadmap” however affords no timeline.

Audit path transparency is totally lacking. Enterprise knowledge groups want transformation logs displaying precisely how AI cleaned their knowledge and whether or not its interpretation of the fields are appropriate.

Export flexibility is restricted to PNG outputs. Whereas satisfactory for fast slide decks, enterprises want customizable, interactive export choices.

The decision: spectacular tech, untimely for enterprise use instances 

For SMB executives drowning in ad-hoc CSV evaluation, Manus’s drag-and-drop visualisation appears to be doing the job. 

The autonomous knowledge cleansing handles real-world messiness that might in any other case require guide preprocessing, chopping turnaround from hours to minutes when you’ve got fairly full knowledge. 

Moreover, it affords a big runtime benefit over Excel or Google Sheets, which require guide pivots and incur substantial load occasions as a consequence of native compute energy limitations.

However regulated enterprises with ruled knowledge lakes ought to await warehouse-native brokers like Gemini or Material Copilot, which maintain knowledge inside safety perimeters and keep full lineage monitoring.

Backside line: Manus proves one-prompt charting works and handles messy knowledge impressively. However for enterprises, the query isn’t whether or not the charts look good — it’s whether or not you may stake your profession on knowledge transformations you may’t audit or confirm. Till AI brokers can plug straight into ruled tables with rigorous audit trails, Excel will proceed to carry its starring function in quarterly shows.