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# Intrepid Insight Posts

Surprisingly, data cleaning is quite time intensive and often not something that can be automated. In my experience, the skill set that makes someone good or bad at data cleaning is not exactly the same as the skill set that makes someone a good researcher or a good analyst. There is overlap of course – creativity is helpful in overcoming data cleaning challenges and in developing analytical models. But being a good analyst often requires one to be alert to the big picture, and to be able to make simplifying assumptions when necessary, whereas data cleaning is all about the odd cases – in fact I think I have spent the vast majority of my time dealing with the 0.1% of observations that do not quite look like the other 99.9%.

Whether a non-profit is a small organization working on a niche issue or a multinational giant working to end global poverty, I believe the big problem facing its administrators can be summed up this way: “How do we generate the most ________ given ______?” The first blank can be filled in with a metric of success for the organization. Things like “awareness,” “support,” “donations,” “participation,” etc.

The second blank can be filled in with whatever the relevant operating budget is for the program. Whether that number is $100 or$100 million is really irrelevant – the point is that non-profits have a finite amount of resources to accomplish a goal.

While there is a massive literature in economics, finance and management on how to maximize profit, there is not a ton of free resources on how to apply these intuitions to non-profits. The data and technical expertise required to perform these types of analyses also exceed the capacity of most small non-profits. As a result of these two factors, optimizing operation stratgies may seem like an impossible task for small organizations.