We believe in the power of information. We also believe in markets and capitalism as a force for good. The two are inexorably linked, because markets don’t work well without open access to reliable data and information, and the insights and perspectives they drive. Within the impact investing world, this is doubly so because of the desire—the need—to generate both financial and social returns.

As long-time practitioners in the space, we know that the soft underbelly of the impact investing movement—which for the purposes of this article also includes mission-related, sustainable, socially responsible, and environmental social and governance (ESG) investing—is the measurement, modeling, and demonstration of actual social impact. The world of philanthropy has suffered from a similar shortcoming. Hundreds of billions of dollars flow every day into companies, projects, products, and investment vehicles dedicated to making the world a better place; yet it is still highly challenging to measure many of the social, environmental, and economic benefits these investments produce.

How should we optimize for both impact and financial return? Where can capital generate the greatest beneficial outcome? What actions can companies and investors take today to maximize the odds of successful impact outcomes tomorrow? The lack of reliable, meaningful, data-driven insights relating to performance is materially hampering progress, and making it difficult to build the models we need to refine cost-benefit analyses and inform decision-making about capital allocation. And by making it harder to account for impact success, it is also constraining the flow of additional resources into the sector.

Lest we get too despondent, we should remember that traditional financial accounting has had more than 500 years to evolve since Franciscan friar Luca Pacioli first invented double entry bookkeeping back in 15th-century Venice. And even now, financial performance measurement can still be as much art as it is science. Nonetheless, there is little doubt that measuring and recording impact and philanthropic outcomes with the same discipline we use to assess financial performance is a prerequisite to driving a more just form of capitalism at scale.

One of the most widely held views in the impact sector is that there is actually a surfeit of data relating to impact performance—that the real problem is too much data, and what the field really needs is universal standards and metrics to drive data convergence and enhance the value of available data. There is some truth to this, and organizations such as the Sustainable Accounting Standards Board (SASB) are leading the charge of standards for disclosure on material sustainable issues across industries. But in our experience, there are still two more fundamental challenges facing the impact (and philanthropic) space: actual access to data and knowledge of how to process it to produce the desired outcome.

The Challenge of Data

Currently, impact data ranges from anecdotal, unrepresentative stories from idiosyncratic experiences and situations, to mega-scale government databases focused on highly specific themes and impenetrable to most human beings. But even in areas where data is readily available and accessible, there are challenges.

Take environmental issues. Government agencies, corporations, ESG data vendors, nonprofits such as the Carbon Disclosure Project (CDP), and rating companies have amassed vast quantities of comparable, specific performance data on all sorts of issues ranging from greenhouse gas emissions to water consumption. Yet impact investors still find it difficult to pinpoint how to most efficiently allocate capital to produce both a cleaner, healthier environment and the desired financial outcome. It’s a similar situation in the realm of corporate governance, leadership, and ethics. Thanks to the US Securities and Exchange Commission disclosure requirements, and the work of organizations such as Institutional Shareholder Services, BoardEx, and others, we are replete with excellent data and analysis on a wide range of traditional corporate governance metrics, such as board share of ownership, percentage of independent directors, and board diversity. Even so, defining how these things combine to ensure that a company is well run, maintains a high standard of leadership integrity, and produces outstanding long-term results is not obvious.

When it comes to tracking social issues, the picture is less encouraging. Here, standards and metrics abound, yet reliable, consistent, meaningful performance data is scarce. And when it does exist, it is either incomplete, inconsistent, or difficult to access. How do you know, for example, whether a company really pays a fair wage? Or treats its employees and customers with respect? Or helps the communities where it operates become stronger so that working families can build a better future? The answer is, you don’t. Typically, whatever information is available relates either to company policies (such as promoting gender pay equity or supporting the health of workers), or specific and overt actions by individual (usually highly proactive) companies. Actual system-wide performance data is rare, and analysis and insights on outcomes is even rarer.

Technology and the demand for greater transparency are helping. The pool of customer sentiment and product quality data from social media, for example, is vast and growing in utility. Employee pay and opinion data provided by crowdsourced websites such as GlassDoor (a JUST Capital partner) is also increasing rapidly. Information on community health, county-level economic and income conditions, local environmental conditions and pollution vectors, job quality and labor conditions, and myriad other aspects of socio-economic conditions around the country is becoming more widely available. Many companies are taking the lead on making data available. All this is raw material for impact-oriented data science exploration.

Enter Data Science

Notwithstanding the difficulties of collecting relevant performance data, the real problem becomes taking existing raw data and converting it into interpretable and actionable information—that is, doing the hard work of data science, and extracting real meaning from the data.

This is no cookie-cutter task. Take the problem of low wages, for example. How can we most effectively raise pay above living wage levels to produce the greatest beneficial impact for those at the bottom of the wage pyramid? Data science for impact can’t simply collapse performance into a single reductive metric (such as the number of workers not paid a single universal wage threshold in relation to profitability), as this could miss important contributing factors (like geographic location or workers’ family situations) and lead to impractical and potentially ineffective solutions (such as raising pay of all workers by a fixed dollar amount). In truth, living wage levels are calculated at local levels, and have to take into account all sorts of things particular to the specific circumstances of individual workers if they are to carry real meaning. What data science can do is enhance predictive power by injecting the much-needed human dimension; for example, beyond simply raising wages, what specific combinations of actions can a company take, and in which communities, to generate the most enduring positive impact on the lives of their employees and their families. Now that’s a real data challenge!

In our work in this area, we seek to use data and data science to shine a light on how companies can best address the real priorities of the American people, including: investing in building healthier communities, optimizing both social outcomes and financial performance, alleviating the pressures on the working poor, addressing environmental stresses while generating jobs, and isolating which social impact metrics are most powerful in predicting future business performance. But this is just the beginning. The sector desperately needs both data and data science to make impact investing more outcome driven. By collecting and making disparate performance data more readily accessible, the industry can help provide the necessary raw ingredients. And by crowdsourcing the best data science talent, we can turn those raw ingredients into truly valuable analyses that hopefully bend the curve of capitalism in the right direction.

Tracker Pixel for Entry