Our blog

**You love your donors and you want them to love you. ** You craft amazing campaigns. Powerful messages, moving stories, carefully constructed asks. You believe in your mission and you are very good at connecting with others who do, too. But you arent getting the donations that you need, and you keep losing donors. Its like your donations are a leaky bucket. You work harder and harder, create more campaigns, reach out to more people, but you cant keep the donors youve got or raise as much as you need to.

Cleaning, wrangling, munging, preparation, validation, ETL, transformation, ELT these are all different names for making data more useful. Whether youre doing one-off analysis, scheduled reports analytics, or integrating two different data systems or applications, when you work with data, data cleaning is an important part of almost all data processes. Data cleaning is taking data as-is and changing the content, structure or format to be more useful. Removing blank rows and deleting invalid addresses are examples of data cleaning.

Over the past several months, Inciter has been pleased to work on a project with a summer camp network. The goal of the network is to enhance accessibility and inclusion for campers and staff with disabilities by providing capital improvements, professional development, staff training, research, and evaluation to both participating camps and the field at-large. In many ways the project is similar to the other program evaluations that we work on with organizations, foundations, and agencies - large and small.

Whether you are getting feedback from program participants, members, donors or other stakeholders, an online survey can be the easiest and most efficient way to do it. Of course, online surveys are not a one size fits all solution for collecting data to answer your burning questions, but they can be a cost effective method to gather certain data for your organization. No matter your field or audience, you have to follow some best practices if you want to make use of your data.

Nonprofits are in survival mode. You work every weekend, you give everything to your clients and leave nothing for yourself, you can’t give anymore and yet it’s not enough. Systems don’t work, the team is unhappy, and the community isn’t sure we’ll be there when they need us. There’s never enough time or money to fix it, so we just hold on until tomorrow.

You want data at your fingertips, when you need it, in the right format. Who doesnt? But often when you go to get reports, the data is siloed and you cant bring it together, or some of its missing, or its not accurate, or you dont know what to make of it. Maybe you think technology is the answer. Its understandable. Machine learning. Artificial intelligence. Big data. There is all this amazing technology out there that can make cleaning, analyzing and reporting on data so much easier.

Tired and unfocused during long meetings? Im going to help you with that. Last week, I had a seven hour Zoom conference call, with about 20 people in it. (I know. I know.) It was necessary, and all parties were actively engaged, and needed to be. This was an authors retreat for a large research project, where Inciter is serving as the data visualization team. It was important that I stay engaged, not space out, and listen to the findings, the nuances, the decisions people were making.

This is the eighth and final post in our Data Cleaning in Excel blog post series. As the series comes to a close, wed like to take an opportunity to organize and recap material weve covered. Well review some of the useful Excel functions weve used, and provide some guidance for incorporating the techniques into your data cleaning routine. Every dataset has its own issues. Our posts in this series have been written to address ad-hoc common problems.

In this part of our data cleaning series, well be focusing on managing blank cells. There isnt a singular approach to handling blank cells in your dataset. Because there are numerous reasons why a cell might be blank, context is key when determining how to fill them. Sometimes, youll need to fill every blank cell in your data with the same constant. Other times, youll pick up clues as to what should be there from the surrounding data.