Forget learning from your organization’s gigabytes; trends in data analysis and machine learning are combining the power of everyone’s data to better understand the nuances of human behavior. The unique set of challenges faced by today’s data analysts when evaluating terabytes and even petabytes of data have led to significant advances in both hardware and software.

Ten years ago, gaining access to the critical computing power required to query massive amounts of data in reasonable timeframes was such an obstacle that organizations could label analytics as a “luxury for the wealthy”, but services like Google’s BigQuery and products like Apache’s Hadoop have leveled the playing field in recent years. Corporations, schools, governments and nonprofits can now overlay their data with thousands of publicly available datasets and turn exploration technologies loose on that data to better understand their own customers, students, constituents, and donors.

This recode.com article was published describing how Crisis Text Line used machine learning to understand how the use of the word “ibuprofen” was 16 times more likely to correlate with the need for emergency assistance than “suicide”. That same article detailed how machine learning is helping conversational artificial intelligence products better engage with users. As these trends continue, the applications for nonprofits will be tremendous. Imagine turning an artificially intelligent robot loose on your donors to engage with them by email or SMS thanking them for their recent contribution or asking them questions about changes in their metadata.

An excellent example of big data and machine learning working together to solve modern data analysis challenges is the recent release of data visualization powered by machine learning in Google Sheets. The gif below shows how users of Google Docs can set machine learning loose on data of any size in Google Sheets to quickly and accurately create visualizations.

"Explore in Sheets, powered by machine learning, helps teams gain insights from data, instantly. Simply ask questions—in words, not formulas—to quickly analyze your data. For example, you can ask "what is the distribution of products sold?" or "what are average sales on Sundays?" and Explore will help you find the answers."

But what do you do if your organization isn’t prepared for the era of big data and machine learning? What if your organization doesn’t have a data scientist or a team of analysts? If your organization finds itself in the grips of a data-driven world without a high-power data team, DMI can help your organization develop strategies for adopting new fundraising technologies and increasing operational efficiencies.