We, as a world, take pride in our data collection abilities and the evolving technologies at our disposal. However, when we look closely, we often realize that especially in our nonprofit sector, the data we have is flawed.
For far too long, we have ignored collecting critical data points, missed creating healthy dialogues around that data, and we have added our biases to all of it — all of it — to perform research operations and take crucial decisions from it. And, while we leveraged this insufficient data to build our research capabilities, a set of analytics-based terms entered our industry – machine learning, deep learning, and artificial intelligence.