Was sind die effektivsten Datenbereinigungstechniken für die Bewertung der Auswirkungen von Fördermitteln?

In my experience, data quality standards need to be set with utmost care. Make them too strict and you will find no data passing through to generate insights. Make them too weak and your insights may come out flawed. And to set these standards, one must rely on business understanding and intuition more than statistics. While you need to freeze statistical metrics, but do so basis what type of problem you are solving and what is the requirement.

Effective data cleaning techniques are crucial for ensuring the accuracy and reliability of grant impact evaluation. The kind of data cleaning techniques you should use will depend on the nature of your data, your research questions, and the goals of your grant impact evaluation. Thorough and systematic data cleaning is essential for producing reliable and meaningful results.

One thing i have found helpful while doing EDA is to again have a direction of what you want to achieve out of this. It’s a vast area with sky being the limit and endless directions to proceed in, so be a bit targeted like know i want to predict likelihood to response, then think what can help predict that. Now go do some EDA, you have a winning stratgey in place.

When analyzing your EDA, look first at the outliers. Those data points that deviate outside the patterns you see. Ask yourself what occurred with that data point for it to be outside of the normal curve. If it was an age outside of your normal range, a random unrepeated event, or possibly an input error, take it out of your subset. Be aware that COVID related data is skewing results when you are trying to look for usage patterns over the past five years and account for that deviation in your report.

While these softwares are helpful, dont blundly trust what they do, as no one size fits all here. It depends on your type of data and your end goal as to which software or technique will be most appropriate.

At smaller organizations it is common for someone with little to no training in data management and cleaning to be tasked with creating reports and providing data for grants. If you are this person and feel under-trained for the task (which is why you're reading this article), ask for help or training. If you are the Development Director or Executive Director, carve out the funds and time to invest in clean data and at least one staff person to be well trained in data management with your CRM or other database.

Depending on the constituents you serve, your data collection will differ. Healthcare agencies mine data from their patients electronic records and appointments. Education nonprofits gather it from the clients application. Look at your overall process of how you are serving people and add in procedures to collect the information that will help you serve them better. You need information such as demographic, income, cultural considerations, and regional challenges to show how you are impacting your community. This will make you more competitive for grant awards.