The Big Data revolution is upon us. Everywhere you look or listen there are people ruminating about how the mass generation and collection of consumer information in unimaginable quantities has heralded a new dawn in marketing, sales and organizational structure. Data scientists, organizational behaviorists and sociologists can't seem to stop writing about how this glut of information will soon be utilized to build corporate and municipal infrastructure.
The data universe is expanding exponentially. By 2016 annual global IP traffic is expected to surpass a zettabyte (that's 1,000,000,000,000,000,000,000 bytes). Even if only a fraction of that is applicable to any particular organization or enterprise, that still leaves a massive amount of data that needs to be collected, organized and more importantly, analyzed. In a recent New York Times article, experts estimated 50 to 80 percent of data scientists' time is spent organizing and prepping data before analysis can even begin.
Tech companies continue to work furiously to create data-crunching software to meet the demand. But in the end interpreting information and applying it to, say, sales figures or population trends will always require trained human brains, and the men and women who possess them come at a premium. According to Glassdoor.com, the median salary for a data scientist in the United States is $115,000. And, as a recent Harvard Business Review blog post discussed, in the for-profit world that number is supplemented by nonmonetary benefits:
"…a community of data scientists to work with and bounce ideas off; an adequate computing environment; ready access to data (without red tape); and access to the details of how the data was collected."
Unfortunately, many nonprofits are not in a position to pay competitive salaries to data scientists, let alone foster the kind of full-service, cooperative internal environment they crave. That’s a particular shame because for nonprofits, Big Data opens doors to prospect research (donor identification, profiling and segmenting); more efficient fundraising tracking, and more actionable insights into donor-organization connections. And that means not investing in data comes at a particularly steep cost: missed prospects, underutilized relationships and a fundraising strategy built on cold calls instead of warm introductions.
But all is not lost for nonprofits. There are a number of viable options before the last resort of taking up a collection for an in-house data scientist. For NPOs looking for data and the personnel to analyze it, initiatives like DataKind and the Data Science for Social Good summer fellowship at University of Chicago place established and burgeoning analysts with nonprofit groups on a per-project basis. And for nonprofits with tech-savvy staffers, software platforms that collect and organize data may be sufficient. Some companies that traffic in that space even offer their services at reduced rates for NPOs.
One thing is for certain—nonprofits can't let the data revolution pass them by. When personnel and tech costs are prohibitive, organizations must mine the field for low- or no-cost programs that can help them generate and utilize information about prospective donors. Social impact is now, for better or worse, tied to data, and those with access to it hold an advantage.
The preceding is a guest post by Josh Mait, Chief Marketing Officer at Relationship Science LLC (RelSci). He is responsible for guiding the overall marketing strategy and its application across all communication channels for the 2013 launch of the ‘ultimate business development tool. He lives with his wife Kira and their two children in Brooklyn.