More than Mere Numbers
From Malte Spitz’s musings that cell phone records could have prevented the fall of the Berlin Wall to the White House’s $200 million commitment to “greatly improve the tools and techniques needed to access, organize, and glean discoveries from huge volumes of digital data,” big data is everywhere, and the philanthropic sector is no exception.
Grantmaking increasingly requires deftly balancing processes and outcomes, transparency and privacy, capacity building and program investment—all elements of decision-making that must incorporate the promised panacea: data.
More efficient and effective grantmaking will likely be the result of an increased understanding and use of data. But data analysis requires more than mere numbers. It requires the consideration of context and the incorporation of intuition.
With Big Data in particular, however, it is tempting to forget that anything exists beyond the purview of quantitative analysis—let alone anything that has not already been quantified or anything that should not be quantified.
Big Data is Watching
That subtly unsettling feeling of being watched is inextricably associated with big data. There is a personal tipping point for each of us. Suddenly we realize that the Crest ad parading across our computer screen isn’t designed to catch just anyone’s eye. It’s designed to catch our eyes—and conveniently remind us that we might be out of toothpaste, all based on our past toothpaste purchases.
As Charles Duhigg reported in The New York Times, Target Corp. so effectively used data analysis to predict behavior and to understand purchasing habits that the corporation detected, from a few shopping trips, that a particular customer was pregnant. They began sending pregnancy-related coupons to the customer’s home address; unfortunately, Target knew about the pregnancy before the pregnant high school teen told her father, who wondered at the content of the daily mail.
While Duhigg himself is empowered by his own mini-data analysis and pursuit to understand his afternoon cookie craving (not to mention the loss of 21 pounds), the fact that a corporation is privy to our intimate secrets is surprisingly easy to forget. But always alarming when remembered.
Using an analytic framework to pinpoint the behavioral cue behind a cookie craving seems harmless enough. Revealing a teen pregnancy to an unsuspecting parent seems at once voyeuristic and a violation—and much more than just a well-targeted ad campaign.
The hope that anonymity could maintain the privacy of an individual without sacrificing the useful side of data collection just doesn’t seem to be a viable option anymore. big data makes it increasingly difficult to maintain the separation between identifiable and unidentifiable information. Everything can be traced back to the originator. Quentin Hardy notes in his New York Times blog that our data isn’t just under attack by faceless corporations.
Our lives are so profoundly interconnected by and through data already that whatever we choose to share online or reveal through our purchases will likely include information about others, especially those we care about the most.
The Power of Prediction
With or without our names, big data is still extremely accurate and not just about predicting pregnancies of Target shoppers. Big data is an essential tool for understanding the dangers of prescription drugs or tracking the spread of diseases by aggregating personal experiences. The very analysis that will let me know if I’m going to catch the flu next week or will like the new Judd Apatow movie can, however, become stifling—particularly if we divorce data from our own intuition and from the possibility of the unpredictable.
As Joseph Jerome of the Future of Privacy Forum notes, “[d]ata mining allows entities to infer new facts about a person based upon diverse data sets, threatening individuals with discriminatory profiling and a general loss of control over their everyday lives.”
We aren’t just a constellation of data points; we are human and can (and do) change our behavior. As grantmakers, that possibility of change is exactly what we’re striving to inspire. But how does this reconcile with what the data predict?
Finding (Another) Third Way
The interconnection that big data promises is very appealing to grantmaking and seems integral to understanding large-scale social change. We want to analyze our contributions to our grantee organizations as well as build the knowledge of our field, connecting beyond our niche and across our funding areas. Big data feels creepy and unsettling in the hands of corporations desperate to influence our brand loyalty, but surely the nonprofit sector can be trusted to steward data responsibly, right?
Big data expert Alistair Croll proposes that “[t]he only way to deal with [big data] properly is to somehow link what the data is with how it can be used.” While a thoughtful and viable system of connecting knowledge and purpose hasn’t yet been devised, Croll’s cry to recognize the importance of implementation is ultimately a cry to remember that we are in charge. Big data is not driving the analysis. We have a responsibility to remember context and think critically about consequences.
Jacob Harold, President and CEO of Guidestar, captures the role of grantmakers in this debate. » In a post entitled “Philanthropy and Emotion in the Age of Big Data” on the Greater Good Arabella Advisors’ blog, Harold outlines two basic principles:
First, information is meant to inform, not decide. If we ever let data offer only one explanation, or one interpretation, we will become robotic in our practice. Data are meant to complement intuition and stories, not to replace them.
Second, embrace many sources of data. The nonprofit sector is simply too complex for any single measure of performance. We need a variety of measures—from randomized controlled trials to beneficiary reviews to financial analysis—to tell the full story of social change.
Big data is a powerful tool for grantmakers, and one that must be used responsibly and mindfully, and which is always open to interpretation. Big data can help us in our decision-making, but cannot replace our professional experience, ideas, critical thinking, or understanding of social change. n