Here's how you can enhance your problem solving skills as a non-profit manager using data and analytics.

Imo this is approach is too simplistic, especially when it comes to the manager role. First of all you should understand what problem you are trying to solve and how this problem applies to your processes across the organization (5 why method could be very handy at this point). After understanding the problem we are trying to solve, it's beneficial and essential for a manager to understand the process they're actually managing and also the entire value chain. At the end of the day the processes should all provide some kind of value to the customer so understanding their mutual relationship is very important. Then yes we can start having look at the data after understanding the process because what if the data needed is missing?

To address challenges effectively, it's vital to distinguish between different data types. Quantitative data, like fundraising amounts, offers measurable insights, while qualitative data, such as donor feedback, provides context and depth. Both are essential for comprehensive analysis. Start by understanding your organization's data collection methods. Ensure they're robust enough to capture the necessary information for informed decisions. This balance between numerical and descriptive data will strengthen your problem-solving capabilities and strategic planning.

For this section to be helpful it needs significant expansion. You need to have a data collection/ data retention tool to put all the data into. You need to work with your people to understand what data they already create is, and what they think could be helpful, and the data that actually isn't currently. You DO need to educate yourself and your staff on any privacy/regulatory concerns. You need to have someone in your organization that can technically facilitate this. Of course this is really just a start.

Make sure that your data is being correctly collected in order to avoid missing data. Use droplists or similar tools wherever possible and make mandatory fields where needed, especially for datatypes you want to analyze later on.

Root cause analysis (RCA) and similar initiatives are also beneficial if there are resources. It's important to understand the variables that are compromising the business's goals.

View each problem as an opportunity for learning and growth. After implementing a solution, conduct a post-implementation review to analyze outcomes and assess the accuracy of your data-driven predictions. This review helps identify what worked and what needs improvement. Maintain a log of problem-solving activities and outcomes for retrospective analysis and training. Share these findings in team meetings to foster a culture of transparency and collective learning. Continuously learning from each experience sharpens your problem-solving skills and enhances future decision-making.

The technical systems that hold your data, how they are accessed and secured need discussion in detail. If these are not integrated into the discussion you will end up with large number of unconnected data sets with little cross reference nor standardization which will limit their later usefullness in analysis