Here are some common problems we see in CSV data uploads, which will create a distorted dataset and inaccurate results when you try to analyze it. Please spend time to make sure your data is clean before sending us your CSV file. We promise you, it is worth it! You know the old data adage... garbage in, garbage out :)  We don't want you to be in that situation! 


Spell check your file thoroughly. For example, 'capacity building' and 'capacoty building' would be imported as two separate values. 

For example, UNdp, UNDP, and the United Nations Development Programme 


Check for capitalization differences. For example, 'Capacity building', 'capacity building', 'Capacity Building', 'CAPACITY BUILDING'. If these values were added in a CSV, they would each be imported as values, so you need to double check your data.

Grantee Names and Acronyms

Check for common errors with grantee names being spelled differently, or for the inconsistent use of acronyms, or different languages being used for the same grantee. 

For example, UNdp, UNDP, and the United Nations Development Programme.

All of these will be entered as unique grantee names giving you the wrong frequencies when you go to analyze your data. 

Country, State, and Region Names

Check for spelling, acronyms, and state/region codes using ISO standards.

For example: The U.S., USA, United States, and United States of America will all be imported differently.

If you want to map a region in a country or a state, make sure to use the appropriate region/state code (i.e., using the ISO standard here) for our system to be able to read the data and create the appropriate state/country maps for visualization.

Text and Numerical Data

Check to make sure there is no text in numerical data columns and vice versa. If so, delete it.

Check that financial data are consistent in terms of decimal points and there is a column for currency.

Check the spacing between numbers as well: for example, it should be '26,429' not 26, 429 

Text and numerical data might be in the same column. This won't be imported, and will lead to errors, so we recommend you separate them. 


Use international standard currency codes (i.e., CAD, USD, GBP, instead of Canadian, US $, or British Pound). 

Decimal Points

For numbers with three decimal points (12.346 or 1234.677), ensure that decimal points (periods) are used consistently (not commas or semicolons as with European numerical notation). We recommend that  you double check this across your data file.


Format dates this way: YYYY-MM-DD (i.e. 2020/12/20)

When your data is clean, please contact our team who will upload it for you. 

Missing Data

Make sure you do not have much missing data. If there is, please take the time with your team to complete the dataset. This will save you having to go back into ImpactMapper, to edit and update each missing variable one by one later. If you do have missing data, leave the cell blank.

Check out an overview of all the steps in cleaning your grants, reports, survey data here in our full collection. 

How do I import my data into ImpactMapper?

How can I clean my data?

How do I create and manage Custom Fields in my Grants and Grantee data?

If you have any further questions about cleaning, preparing, and uploading your data to ImpactMapper, check out the rest of our articles here, or get in touch via the chat icon below. 

We will also host webinars sharing best practices for keeping your data clean and up to date. Keep checking this page for the latest, and subscribe to our newsletter!

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