I first became interested in the idea of applying data analytics to development work 14 years ago, when I was invited to interview for a job as director of prospect research at a university that had just completed a major fundraising campaign and was looking to do a lot of analysis of its campaign data to figure out the best path forward. I didn’t have a great amount of experience in the field at the time, and I hadn’t actually applied for that particular job, but the hiring manager had come across my resume and thought I might be worth interviewing for the opening. Even though I wasn’t hired for the position, it was a great learning experience for me in many ways, not the least of which is that it inspired a lot of curiosity in me for the whats, whys, and hows of data analysis.
It remained an interest of mine, as gradually more presentations began to appear on the APRA conference schedule about analytics. At the time, though, I was working at an institution with an old database that was not user-friendly and which I couldn’t query on my own. It was frustrating knowing that there was a world of possibilities which I felt incapable of tapping into.
Eventually I got a job at the University of Nevada, Reno, where I could query Raiser’s Edge, and also run my own reports and exports. Suddenly, the possibilities began to open up again, but I still didn’t have access to statistical analysis software yet, and even if I had, I wouldn’t have known right away where to begin.
As it turned out, though, I came to recognize that there was a good amount of basic data analysis I was able to do without it, just using exports, Excel and Access. If you haven’t undertaken much in the way of data analysis yet, I’d recommend becoming familiar with what can be done with those programs.
Most of the attention in discussions of analytics has to do with predictive models, and while they are definitely valuable, they aren’t the only game in town when it comes to data analysis. One way to think about it is to think about the difference between running and walking. One should learn to walk before learning to run, but even once one learns to run, one doesn’t need to run everywhere all the time. Walking is still a great way to get around most of the time.
Although there are many faster and more sophisticated statistical packages out there, if you don’t have access to them, or don’t want to take the time to learn them, many insights can be generated simply by learning to make use of the features provided by Excel or Access to get a picture of your data. What are some of the things you can do?
1). Filtering: One of the first tips for data analysis I learned about was to use the filters in Excel or Access as a way of querying and analyzing your data. You might start by exporting a very broad query or data set and then you can refine it to the parameters you want to examine using filters, or you can combine several different filters to see what patterns emerge about the records in that particular data set.
2). Tracking with additional columns: Once you have selected certain combinations of variables with filters, you can add another column and enter a tracking variable or a set of variables. (You can also code such variables automatically using formulas in Excel or in Access once you know what you’re looking for.)
3). Joins and queries in Access: You can use Access to clean and modify a data set, or to join several different tables into one.
4). Pivot Tables: Once you determine the variables you want to examine, you can use pivot tables to evaluate the relationships between them and to look for patterns in your data.
5). Data Analysis Module: In Excel, you can add-in the “Data Analysis ToolPak” and use that to conduct more sophisticated forms of analysis, and you can make more detailed charts and tables and to practice looking for relationships between variables.
Once you’ve mastered these basic five steps and are ready for more, fortunately there are a lot of great options, both through books and through numerous online resources. I am planning to write more future posts describing other simple and economical ways to get started.
What are some of the basic tools and techniques you have used to begin learning about data analysis? Where and how did you get started? Or, if you are just getting started, are there tools or techniques I have described above would you like to learn more about in future posts? Please comment below.