See onoho in Action
Instantly identify discrepancies between two datasets and get actionable suggestions.
With onoho you can easily replace your old data matching tool and makes your financial data reconciliation in seconds, not days.
Upload two files & get reconciliation in seconds.
Matches and differences are highlighted line by line.
Key, sum, and fuzzy match tips help resolve discrepancies.
Runs in your browser with no install, and your data stays secure.
When records share an ID or name but the numbers don’t align, onoho flags it immediately. You’ll instantly see if, say, the same invoice ID has different amounts in each dataset, so nothing slips through unnoticed. No more poring over spreadsheets cell by cell to catch these inconsistencies – onoho brings them front and center, so you can fix issues with confidence.
onoho automatically detects when multiple entries in one dataset sum up to a single entry in the other. For example, if five payments in Sheet A equal one invoice total in Sheet B, you’ll know. This saves you from manually adding up numbers to find missing totals. It uncovers split transactions or partial payments that would otherwise be hard to catch, ensuring your records are fully reonohod.
Typos, abbreviations, or name variations won’t derail your reconciliation. onoho uses smart fuzzy matching to spot nearly identical keys that should be matches – e.g., “ACME Inc” vs “ACME Incorporated” with the same amount. It flags these near-duplicates and suggests treating them as a match. The benefit? You maintain data integrity by identifying entries that are the same despite minor differences, so you can correct them and trust your data.
onoho is used to compare two data sets (such as spreadsheets or CSV files) and identify any differences between them. It highlights which rows match perfectly and which don’t, making it easy for finance or BI teams to verify that records from different sources line up accurately.
onoho uses three strategies to help resolve mismatches. First, it flags exact key matches where the IDs are the same but values differ (so you know which numbers to correct). Second, it looks for sum matches – cases where multiple entries on one side add up to an entry on the other side – and points those out. Third, it applies fuzzy matching to find entries that have slightly different keys (typos or name variations) but likely refer to the same thing. Based on these, onoho offers suggestions or pairings so you can quickly reonoho the two sets.
No. onohoruns entirely in your web browser as a lightweight web app. There’s nothing to install, and you don’t need any programming or SQL knowledge. Just go to the onoho site, upload your two data files, and it will do the comparison and analysis for you automatically.
Yes. We take data security seriously. When you upload files to onoho, your data is processed securely (over an encrypted connection). We do not store your datasets permanently – the files are only used for the reconciliation session. In fact, all the processing can happen in-memory on the server (or even in your browser) and the data is discarded once the comparison is done. Your confidential financial data stays safe, and you control when to upload or remove it.
Currently, you can upload data in common formats like CSV or Excel (.xlsx) files. onoho will parse those and perform the reconciliation. Many users export reports from databases or BI tools into CSV/Excel and then use onoho. (Support for direct integrations with databases or Google Sheets is on our roadmap.) For now, as long as you can get the data into a spreadsheet format, onoho can reonoho it.
onoho is designed for anyone who works with two sets of data that should match. Typically, data analysts, accountants, financial controllers, auditors, and business intelligence (BI) professionals find it most useful. If you’ve ever had to manually verify that two spreadsheets or system outputs agree with each other, onoho can save you a ton of time. It’s built to handle use cases from reconciling financial records (like bank vs. ledger transactions) to verifying data migrations or any scenario where you need to double-check data consistency.
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