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See DataLumio in Action

One Workspace to Clean, Analyze, Visualize, and Understand Your Data

Data analysis can mean working through spreadsheets, research papers, interview transcripts, survey responses, customer feedback, reports, and other files. DataLumio brings those workflows together in one place.

Upload your data, clean it when needed, choose the right analysis, explore the results, create visualizations, and turn the work into structured outputs.

No formulas to build. No coding required.

Start Analyzing Your DataOur Features
CSVExcelPDFDOCTSVGoogle Drive & Google Sheets
DataLumio product demo preview

What Can You Do With DataLumio?

DataLumio is built around the different jobs that usually happen during data analysis.

Chat with PDFs and ask questions about long documents

Analyze qualitative data such as interviews, transcripts, and open-ended responses

Analyze quantitative data such as surveys, spreadsheets, and research datasets

Clean messy datasets before using them for analysis

Build data visualization dashboards from structured datasets

Connect supported files from Google Drive and Google Sheets

Generate structured reports from analysis results

Bring your data in→Work with it→Understand the results→Use the output

Explore the Main DataLumio Features

Six workflows that cover the everyday work of analyzing documents, datasets, and research material.

1PDF Analysis

PDF Analysis

Ask Questions About Long Documents Without Searching Every Page

Research papers, reports, policy documents, financial documents, and other PDFs can contain hundreds of pages.

Instead of repeatedly searching through a document, upload it to DataLumio and work with the PDF directly.

The PDF workspace lets you:

  • Ask questions about the document
  • Get answers based on the uploaded content
  • View the PDF alongside the conversation
  • Examine selected areas of a document
  • Work with charts, tables, and visual sections
  • Keep conversations saved with the file

This is particularly useful when you need to understand a long document before using its information in research, reporting, or analysis.

Explore PDF Analysis
annual_report_2025.pdf148 pages
What were the main cost drivers this year?
Three areas account for most of the increase: logistics, cloud infrastructure, and contractor spend.
Pages 42, 57, 91
Show me the table on page 57.
2Qualitative Data Analysis

Qualitative Data Analysis

Turn Interviews, Transcripts, and Open-Ended Responses Into a Structured First Pass

Qualitative research produces information that cannot simply be reduced to columns and numbers.

Interview transcripts, open-ended survey responses, research notes, and other text-based documents may contain important themes scattered across many pages.

DataLumio can help organize that material by identifying:

ThemesSupporting quotesSentimentRecurring patternsTheme occurrencesDifferences between documentsStructured findings

You can upload supported PDF and DOC files, select the qualitative workflow, and work toward a structured analysis. The result gives you a starting point for closer review and interpretation.

Explore Qualitative Data Analysis
12 interview transcripts5 themes
Onboarding41
Response time32
Documentation24
Pricing clarity15
“The first week felt overwhelming — I wasn't sure who to ask.” — P04
3Quantitative Data Analysis

Quantitative Data Analysis

Analyze Surveys, Spreadsheets, and Structured Research Data

When your data is numerical or structured, you need a different kind of analysis. DataLumio supports CSV, XLSX, and XLS files for quantitative analysis.

Depending on the dataset, you can explore:

Descriptive statisticsFrequency tablesStatistical testsCorrelationRegressionANOVAChi-squareOutlier detectionChartsGroup comparisonsPlain-language explanations

You can also start with an optional research question to give the analysis a clearer direction. DataLumio is designed to give you a useful first pass without requiring SPSS syntax, Python, R, or complex formulas. Your research design and statistical judgement still determine whether a particular method is appropriate.

Explore Quantitative Data Analysis
survey_responses.csv1,284 rows
7.4Mean score
±1.3Std. dev
0.62Correlation
ANOVA: group differences are statistically significant (p < 0.05). Review against your study design before reporting.
4Data Cleaning

Data Cleaning

Get Messy Spreadsheets Ready for Analysis

Analysis is only as useful as the data you start with. A spreadsheet may contain duplicate records, blank rows, empty columns, missing values, or other problems that are easy to overlook.

DataLumio's data cleaning workflow supports CSV, XLSX, and XLS files and helps identify common data-quality issues before you move into analysis or visualization.

The workflow is straightforward:

  • Upload — start with the spreadsheet you already have
  • Check — DataLumio examines the dataset for common issues
  • Clean — the workflow helps address supported data-quality problems
  • Download — use the cleaner version for analysis, dashboards, or reporting

Your original file remains the source file, so important datasets can still be reviewed before the cleaned version is used.

Explore Data Cleaning
customer_export.xlsx4 checks
Duplicate rows18 found
Empty rows7 found
Blank columns2 found
Missing values34 cells
Cleaned copy ready to downloadDone
5Data Visualization Dashboard

Data Visualization Dashboard

Turn Structured Data Into Visuals You Can Actually Read

Rows of numbers can make patterns difficult to see.

DataLumio's visualization dashboard turns supported datasets into visual summaries that make comparisons, trends, and key figures easier to inspect.

Depending on your dataset, you can create visualizations such as:

Bar chartsLine chartsPie chartsScatter plotsHeatmapsKPI viewsTablesOther supported visual summaries

Upload CSV, XLSX, XLS, or TSV data and create an interactive dashboard without building a visualization pipeline from scratch. This can be useful for research results, customer data, sales information, operational reports, survey results, and other structured datasets.

Explore Data Visualization
Q3 dashboard6 charts
12.4kResponses
68%Satisfied
4.2Avg rating
6Data Integration

Data Integration

Work With Files Where They Already Live

Not every dataset needs to be downloaded and uploaded again.

DataLumio currently supports connections with Google Drive and Google Sheets, allowing you to access supported files through connected sources.

You can:

  • Connect your Google Drive
  • Connect supported Google Sheets
  • Browse available files
  • Open supported documents and spreadsheets
  • Work with connected files
  • Keep file-specific conversations saved

This is useful when your research files, reports, spreadsheets, or business documents are already organized in Google Workspace.

Explore Data Integration
Connected sourcesGoogle Drive
SHEETq3_survey_resultsConnected
XLSXcustomer_accounts.xlsxConnected
PDFmarket_review_2025.pdfConnected
SHEETinterview_scheduleConnected

See How the Features Work Together

Data analysis is more than one step.

Most projects need more than one feature

A researcher may need to clean a spreadsheet, analyze survey responses, review research PDFs, and prepare charts. A business team may need to connect a spreadsheet, analyze customer data, create a dashboard, and prepare a report.

One project, several kinds of data

An academic project may involve interviews as well as numerical survey data. DataLumio connects these stages into a broader workflow instead of leaving each one in a separate tool.

A Typical Workflow

01

Bring in your data

Upload supported files or connect supported sources.

02

Prepare the data

Clean structured datasets when necessary.

03

Choose your analysis

Use quantitative analysis for structured data or qualitative analysis for text-based research material.

04

Explore the results

Review statistics, themes, quotes, patterns, comparisons, or document findings.

05

Visualize

Turn structured data into charts and dashboard views where useful.

06

Report

Create structured outputs that can be reviewed and used in your wider workflow.

The exact path depends on the type of project and data you are working with.

See DataLumio With Different Types of Data

Start from the material you already have and follow the workflow that matches it.

Survey Data

Upload survey results and explore descriptive statistics, frequencies, relationships, comparisons, and visual summaries. For open-ended responses, qualitative analysis can help identify themes and recurring ideas.

Explore Survey Data Analysis

Thesis & Dissertation Data

Work with the different files that often make up academic research, and use the appropriate DataLumio workflow for each part of the project.

  • Survey datasets
  • Excel files
  • Interview transcripts
  • Research PDFs
  • Statistical data
  • Open-ended responses
Explore Thesis & Dissertation Data Analysis

Excel Data

Upload supported XLS or XLSX files to clean, analyze, and visualize structured data without building the entire workflow manually.

Explore Excel Data Analysis

Research Data

Research projects often involve more than one data format. Use quantitative analysis for structured datasets, qualitative analysis for text, PDF analysis for research documents, and dashboards for visual exploration.

Explore Research Data Analysis

Customer Feedback

Analyze supported customer comments and feedback to identify recurring themes, sentiment, concerns, and patterns that may otherwise take hours to organize manually.

Explore Customer Feedback Analysis

Interview Transcripts

Work with supported transcript documents to identify themes, supporting quotations, sentiment, and recurring patterns across qualitative research material.

Explore Interview Transcript Analysis

Statistical Analysis

For suitable structured datasets, explore descriptive statistics, correlation, regression, ANOVA, chi-square, outliers, charts, and other supported statistical workflows.

Explore Statistical Analysis

What Makes the DataLumio Workflow Different?

You do not have to start with a technical setup.

A lot of data analysis software assumes you already know which formulas to write, which code to run, which statistical package to open, or which visualization pipeline to build.

DataLumio takes a different approach. You start with the material you already have.

A spreadsheetA survey exportAn interview transcriptA research paperA customer feedback fileA business report

Then you choose the workflow that matches what you need to understand. That makes DataLumio useful for people who work with data but do not want every project to become a technical exercise.

Your data stays at the center of the workflow

DataLumio is not about forcing every project into the same analysis. The file determines the workflow.

Structured numerical data?

Use quantitative analysis.

Messy spreadsheet?

Start with data cleaning.

Interview transcript?

Use qualitative analysis.

Long research PDF?

Open it in PDF Analysis.

Need to explain the numbers visually?

Build a dashboard.

Files already in Google Drive?

Connect the supported source.

Need a structured starting point for reporting?

Generate a report from the analysis.

Built for Different Kinds of Data Work

Researchers

Analyze research datasets, interviews, transcripts, survey responses, and research documents from a single workspace.

Students

Work on assignments, research projects, theses, dissertations, and survey analysis without needing to build a complicated technical workflow.

Analysts

Clean datasets, explore relationships, create visualizations, and prepare structured outputs.

Business Teams

Analyze customer information, surveys, sales data, reports, and operational datasets.

Consultants

Move from client files to organized findings and presentation-ready outputs more efficiently.

UX and Research Teams

Review interviews, open-ended responses, customer feedback, and other qualitative research material.

See What DataLumio Can Do With Your Own Data

The best demo is your own dataset.

A product demonstration can show you the workflow. Your own data tells you whether that workflow fits your project.

Start with a file you already need to understand.

Upload a spreadsheetTry a survey datasetOpen an interview transcriptAsk questions about a research PDFConnect your Google DriveBuild a dashboard

Then review the results yourself.

Start Analyzing Your DataView Pricing

No complicated setup. Start with the data you already have.

Frequently Asked Questions

The DataLumio demo shows the platform's main workflows, including PDF analysis, qualitative data analysis, quantitative data analysis, data cleaning, visualization dashboards, data integration, and report generation.

Depending on the workflow, DataLumio supports research documents, interview transcripts, survey responses, CSV datasets, Excel spreadsheets, PDFs, customer feedback, business data, and connected files from supported integrations.

Yes. DataLumio supports XLS and XLSX files for supported cleaning, quantitative analysis, and visualization workflows.

Yes. CSV files are supported for data cleaning, quantitative analysis, and dashboard workflows where applicable.

Yes. You can upload supported PDFs and ask questions about their contents through the PDF analysis workflow. You can also analyze selected visual areas of supported PDFs.

Yes. Supported transcript documents can be analyzed for themes, supporting quotes, sentiment, patterns, and other qualitative findings.

Yes. Structured survey data can be analyzed quantitatively, while open-ended responses can be examined through qualitative analysis where appropriate.

Yes. DataLumio can create interactive visualization dashboards from supported structured datasets, including charts, KPI views, comparisons, and other visual summaries.

Yes. Google Drive and Google Sheets are currently supported integrations. You can access supported connected files without repeatedly downloading and uploading them.

Yes. DataLumio can generate structured reports from supported qualitative and quantitative analysis workflows.

No. DataLumio is designed around no-code workflows for its supported analysis tasks.

Not for every project. Specialist software may still be appropriate for advanced statistical procedures, highly customized models, programming-heavy workflows, or specialized research requirements.

Yes. Important results should always be reviewed against the original data and the context in which the data was collected.

For academic research, statistical analysis should follow the study methodology and institutional requirements.

For business decisions, important figures and findings should be checked before they are used.

Ready to See Your Data Differently?

You do not need to build a complicated analysis workflow before you can start understanding your data. Bring in the files you already have.

Clean them when neededAnalyze the numbersExplore the textAsk questions about documentsBuild visualizationsConnect your existing filesCreate structured reports
Start Analyzing Your DataView Pricing

Clean. Analyze. Visualize. Understand. Your data is already there — now make it easier to work with.