Data for Good Is Good for Us All
Philanthropy is more than just a way of giving back, but do we test and validate our assumptions of the good such donations do? As champions of data-driven analysis, we should be practicing what we preach.
From a business perspective, philanthropy is more than just a way of "giving back."
It's more than just a tax write-off, too. Charitable donations are a highly visible form of marketing, a way for organizations to tout their altruism, social responsibility, and -- at the risk of sounding cynical -- taste. For too many organizations, however, this is a missed opportunity .
At the last Pacific Northwest BI and Analytics Summit, Jill Dyché, vice president of best practices with SAS Institute, explored this missed opportunity in a presentation called "Data for Good." Due to altruism, practicality, and sheer self-interest, some organizations are using their data -- along with their data science expertise -- to aid social causes, Dyché told attendees. She argued that these organizations are making an effort to manage their philanthropic efforts like they manage their business operations.
"It's not enough to say, 'We're giving money for clean water in sub-Saharan Africa.' It needs to be, 'We're funding this [clean-water] effort and here's how many villages, how many people, and how many children now have access to clean water. Here's how we've improved the quality of life for [these people],'" Dyché told Upside in a follow-up interview.
The Intersection of Charity and Self-Interest
Giving money to organizations that are best able to quantify the social, environmental, and humanitarian impact of their work is the responsible thing to do. It's the very stuff of good corporate social responsibility. However, using data for good is also a matter of self-interest: promoting success in charitable giving can drive measurable shifts in goodwill and positive brand association.
Data for good can likewise be leveraged as part of a corporate marketing strategy -- e.g., smart organizations give money to smart charities that do smart, impactful things with it. Finally, the bottom-line impact of data for good can be measured in new or increased business, reduced tax liabilities, and other externalities.
"If you can quantify it and say 'we added four clients because we gave to this charity last year and we also radically reduced our tax burden,' that's a win-win," Dyché explained. "The biggest barrier to really seeing the benefits of this stuff is that organizations just don't know what they don't know about their own missions and the potential [of using data for good]."
Styles of Giving Back
Dyché described five different types of data-for-good practitioner. The first consists of organizations that explicitly operate as nonprofits or nongovernmental organizations. She called this type -- of which Habitat for Humanity is a prominent example -- "mission-driven" data for good. Obviously, comparatively few organizations slot into this category.
The second type is "corporate citizens," which describes the overwhelming majority of organizations. In this system, charitable giving typically falls under the auspices of corporate social responsibility, e.g., as a means of "giving back" in some way. Not surprisingly, "corporate citizens" tend to have a vague sense of what this actually entails. In addition to altruism, they're motivated by a desire to create goodwill and to enhance their brands.
The third type is "profit-for-good," companies which want to monetize their goodwill. This includes low-profit, limited liability companies (L3C); in some states, organizations such as small organic family farms and food co-ops take advantage of this relatively new structure to receive grants without being fully nonprofit.
Fourth are the data-for-good "innovators" -- organizations that explicitly use data, analytics, and data science to look for insights that have the potential to revolutionize the impact of charitable giving -- or the efficacy of charitable activities -- in different segments.
Rounding things out is "government," which -- surprise! -- Dyché said has been an engine for innovation in using data for good. "Government institutions have leapfrogged private institutions in their ability to leverage data for good because they have [access to] so much data in the first place," she told attendees. "A lot of people think that open data and data.gov are mostly federal [efforts], but it's really trickling down to the state and local levels."
Again, most for-profit companies could be considered "corporate citizens."
If they're going to be good corporate citizens, however, they're going to have to demand the same rigor, transparency, and accountability from the charitable organizations that receive their philanthropic largesse as they do from their internal accounting and finance departments. As Dyché and other experts argue, there's considerable room for improvement.
Data for Good Is Good for Us All
Corporate philanthropy should not be a dumb or reflexive thing -- automatically cutting a check to "The Human Fund" not only isn't good corporate social behavior but does nothing to help corporate branding and marketing efforts.
Many organizations understand this -- including companies (such as SAS) that are especially well positioned to make good use of data, analytics, and insights. Other prominent analytics vendors, such as Teradata, promote their own (data-driven) data-for-good efforts, too. The nonprofit sector is also collectively trying to move toward better measures, to make it easier for prospective donors to understand actual impact.
We tend to assume that philanthropy is good for corporate givers, good for the direct beneficiaries of corporate giving, and good for society -- or, more ambitiously, humanity -- as a whole. An effort such as data for good asks us to have the confidence to test and validate these claims. As champions of data-driven analysis, we should be practicing what we preach.