House View by the Chief Investment Office

Why impact investing needs Silicon Valley

9 February 2017 | New York

Reading time: 6 minutes

By Andrew Lee, Head of Impact Investing and Private Markets at UBS Wealth Management

Last week I was in Silicon Valley at a private gathering of members of UBS's Industry Leader Network - executives and entrepreneurs at privately held companies worldwide. Our discussions focused on artificial intelligence and machine learning and their potential implications for business operations and investment. One key takeaway is the ever-increasing volumes of data needed as essential inputs for these iterative, algorithmic approaches to data analysis. Indeed, the amount of digital data is expected to reach 44 zettabytes by 2020, according to IDC and IMC. That is 50 times the size of the data universe in 2010, equivalent to 318 iPhones for every household. In many industries, the focus now is less on data availability, but more on data consistency and how best to action artificial intelligence-driven insights.

"When it comes to impact investing, good data remains less available"

When it comes to impact investing, however, good data remains less available, and what we do have can be uneven in quality and comparability. Yet in my view, more quality data to support rigorous impact measurement and management is exactly what is needed to mobilize private impact investment at scale to address the massive social and environmental challenges that we face and achieve the UN-supported Sustainable Development Goals (SDGs) by 2030. Last year, the United Nations Association - UK produced a report entitled "Sustainable Development Goals 2016: The people’s agenda" addressing the SDGs. In it, contributors Mahmoud Mohieldin and Claire Melamed observe that the quality of SDG-related data varies considerably between countries, constraining our ability to identify and address the most pressing global gaps. For instance, they found no available data on measurable poverty trends in 29 developing countries between 2002 and 2011, and only a single data point for an additional 28 emerging countries.

Perhaps more importantly, there is still not critical mass of quality data in many focus areas to properly assess which interventions and strategies actually work. There are multiple initiatives underway to address these shortcomings and continue strengthening the evidence base, but success will require collaboration and support from public and private players alike. According to UBS' white paper for the World Economic Forum Annual Meeting 2017, sustainability-related data systems and infrastructure receive minimal funding – only 0.24% of all aid in 2013. Increased investment and effective leveraging of technology are necessary to improve collection, measurement and quality of SDG-related data. Although recommending specific initiatives is beyond the scope of this blog, in recent years data-related efforts have included the Global Partnership for Sustainable Development Data, the Millennium Institute’s iSDG Integrated Simulation Tool, or OpenForis’ Collect Earth, which helps identify degraded land in Africa that requires restoration.

"Sustainability-related data systems and infrastructure receive minimal funding"

More also needs to be done to analyze these data and insights and translate them into increased private investment. With respect to restoration and conservation alone, the World Resources Institute estimates that the private sector accounts for only about 20% of the USD 50 billion spent on this area annually. Private investors are likely to commit more capital to SDG-related opportunities that generate compelling financial returns as well as measurable impact, so better data and analytics supporting both of these objectives will be key to activating these investors.

"More needs to be done to analyze these data and insights and translate them into increased private investment"

We can take a cue from artificial intelligence's dependence on data to learn and evolve. Increasing the availability and quality of impact focused data should support better decision-making and iterative improvements in impact measurement and management. Also important will be continued efforts to strengthen the links between funding sources, data and analytics, leveraging shared networks and platforms. If we are to meet the UN SDGs by 2030, these connections and data will be crucial to attract for-profit investment at scale from industry leaders, private wealth and non-public sector players.

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