Pictures of MANUS CHATHPTS CHATGPT DATA VISUALIZATION: which should businesses use?

The promise is as good to be true: drop a messy comma separating values (CSV) an AI agent, wait two minutes, and reconcile a definite chart.

But that’s really starting China Manus.im is to deliver the most recent part of the data rush, launched this month.

Unfortunately, my first hands-to try damaged datasasts reveals a basic business problem: impressive business problems with insufficient transparency. While manus is in charge of messy data better than chatgpt, even the tool is still ready for boardroom slides.

Spreadsheet problem struck by enterprise analysis

Rosss’ The survey of 470 financial leaders found 58% is still primarily primarily in Excel for monthly KPIs, despite owning bi licenses. other Teknangan Estimates to study that total spreadsheet ride affects nearly 90% of organizations – the export of warbouses “Hasty-Mile exporting in CSV inboxes.

Target the manus this exact gap. Upload your CSV, describe what you want in the natural language, and the agent automatically cleanses data, selects a Vega-Lite chart that needs export-no export tables.

Where Chatgpts Attack: 4x slowly but more accurately with dangerous data

I tried anus and advanced analysis of chatgpt data using three datasets (113k-row Ecommerce orders200k row Marketing funnel 10k-row Saas MRR), first clean, then damaged with 5% injection of error including nulls, mixed dates and doubles.

For example, testing the same prompt — "Show me a month-by-month revenue trend for the past year and highlight any unusual spikes or dips" — across clean and corrupted 113k-row e-commerce data revealed some stark differences.

For context: Dereseeeek can only handle 1% of file size, while Claude and Grok took 5 minutes each but produced ex-export options.

Outputs:

Figure 1-2: Chart outputs from the same prompts to prompt data E-Commerce data. Manus (low) produces an aggregate data corruption, while the chatgpt (top) displays evaluates of date formatting patterns.

Manus acts like a careful junior analyst – Automatic data tement before charting, successful parsing date parsing date of ban and handling nulls with no obvious instruction. If I request the same analysis of a data, Manus takes approximately 4 minutes but makes a uniform quality issues.

Chatgpt works like a Clock Coder – Pronounce facilitating output to data hygiene. The same request is only taken in 59 seconds but makes mischievous views because it is not automatically cleaned in formatting formatting.

However, both tools fail in terms of “executive ready.” Neither make board-ready axis scaling or read labels without follow-up prompts. Data labels often overlap or fewer, bar charts with no correct gridlines and the formatting of the number is not equal.

Transparency Crisis Enterprises cannot escape

Here’s where the manus has a problem for adopting business: The agent does not face cleaning measures available to it. An auditor reviewing the final chart has no way to confirm when the outliers have dropped, printed or changed.

If a CFO provides quarterly results based on the chart made by a manus, what happens if someone asks, “How do you handle duplicate transactions from the Q2 System integration?” The answer is silence.

Chatgpt, Claude and Grek all showed their Python code, although the transparency by reviewing code is not scalable for program users lacking program experience. What business needs a simple audit path, building trust.

Warehouse-Native AI has a race ahead

While Manus focuses on CSV upload, large platforms build chart chart directly to business data infrastructure:

Google’s Gemini in BigQuery turned generally on August 2024, allowing the generation of SQL questions and inline views on living tables while respecting the array of row.

Microsoft’s Copilot in Fabric The GA has reached the power of BI experience on May 2024, making visuals within fabric notebooks while working directly with datasets in Lubohouse.

Gooddata has assistantlaunched in June 2025, moving within the customer’s environments and respects the semantic models, allowing users to ask answers with the terms with the terms with the specified standards and terms of business.

These native warehouse solutions eliminate CSV Exports fully, preserve complete data line and retract security models such as the battle of the benefits such as manous struggles to match.

Critical gaps for enterprise adoption

My test reveals many blockers:

Live data connection Remaining no – Manus supported file uploads, without snowflake, bigquery or S3 connectors. Manus.IM says the connectors “of the roadmap” but do not give a timeline.

Transparency transparency lost completely. Business data timers should log changes that show how AI has cleaned their data and if translating fields is correct.

Export to Export limited to PNG outputs. While adequate for easy slide decks, businesses should not be used, export options.

The judge: the impressive tech, no business use cases

For SMB executives drowned by analyzing ad-hoc CSV, drag-anus drag seems to have worked.

Autonomous data cleaning carries the real jewelry world that will need manual preprocessing, cutting turning from hours in a few minutes when there is reasonably complete data.

In addition, it offers an important advantage of runtime over excel or google sheets, which requires manual pivots and have many load limits.

But regulates businesses with doong data lakes should wait for agents native like Gemini or Telic Copilot, which keeps security data.

Lower line: Manus confirms a quick chart chart but for businesses, the question is not if charts appear good – if you can block your career with data changes you cannot audit or verify. Until AI agents can be plugged directly directly to the namanic tables with stiff audit lanes, Excel will continue to hold a star paper in the quarterly presentation paper.

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