The Role of Data Aggregation in the Geospatial Risk Data Revolution
Data aggregation is a process in which disparate datasets are compiled together and assembled into a format that is easy to manage and understand. In the past, ag banks and financial institutions have been limited by having separate data silos for different parts of their operations, such as loan, collateral, and appraisal data.
As it pertains to agricultural water risk, data management and analysis are many times done in spreadsheets derived from outdated water information systems, such as onion paper.
By un-siloing and aggregating this data, financial institutions can put this information into context and represent it in a way that meets the needs of an informed decision-making process.
The International Institute for Sustainable Development (IISD) explains how data aggregation can help address some of the most pressing issues of the day:
“By integrating sources of diverse geospatial and statistical natures, innovative data partnership approaches could effectively help in the understanding of the territorial behavior of … poverty or climate change, [as well as] the coronavirus pandemic.”
This post will explore the ways the role that data aggregation can play in the geospatial risk data revolution, especially in the context of agricultural finance.
How Data Aggregation Unlocks Datasets
According to A World That Counts, a recent report by the U.N. Data Revolution Group, “New technologies are leading to an exponential increase in the volume and types of data available.” At the same time, many valuable datasets “remain unused because they are released too late or not at all, not well documented and harmonized, or not available at the level of detail needed for decision-making.”
They describe the data revolution as a dramatic increase in the amount of data being produced, how quickly data can be produced and disseminated, and the sheer number of individuals and institutions that are producing this data – on everything from mobile phones to IoT (Internet of Things) devices.
When it comes to agricultural finance, these data sets can come in many forms, from drought and flood layers, to water rights, to soil data, to companies’ ESG data, and more. The context gained when combining risk data to proprietary bank data helps to identify risks in a bank’s loan or investment portfolio.
The Benefits of Geospatial Data
If these data sets exist in isolation, then banks and other financial institutions simply can’t unlock all of the information within them, nor can they truly unlock their own data.
The Data Revolution Group points out that much of the world’s data is “often restricted behind technical and/or legal barriers,” “buried in pdf documents,” or otherwise difficult to work with.
By aggregating data into a central, standardized database, it becomes easier for users to work with this data and identify links between disparate datasets. This is one of the first steps in implementing GIS tools and is the foundation for portfolio-specific GIS platforms that can help professionals make better risk decisions.
With the right platform, finance professionals can view data in an intuitive map-based format, adding and removing layers to explore unseen connections. The Brookings Institute shows just how unexpected some of these connections can be:
“Researchers have found that high-resolution, spatially tuned satellite imagery can provide important insight into human economic activity…. Social scientists have started to use nighttime light measures, or luminosity, as proxies for economic activity and population distribution.”
Geospatial data is especially important in agriculture, an industry in which drought risk, groundwater levels, water and soil quality, and regulatory requirements all dictate how resilient an operation is in the face of water stress and climate change. Ag lenders and investors can use parcel-specific GIS data to assess and mitigate portfolio risk.
Data Aggregation Allows Datasets to Come Together
In a report called Making Climate Finance Work in Agriculture, the World Bank explains how imperative it is for the ag finance sector to adapt to a changing climate:
“To realize the opportunities agriculture has to offer, the sector needs to be transformed so it can deliver more sustainable agriculture and smallholder farmers can … become successful and profitable while also delivering food and nutrition security.”
This will require ag finance institutions to expand their approach to loan risk, taking into account not just a borrower’s financial risk, but other physical and material risks.
How Data Aggregation Improves the Decision-Making Process
By incorporating multiple datasets into their decision-making process, ag lenders and investors can improve their ability to identify and mitigate risk. They can pair their own existing financial datasets with environmental and social risk datasets to put each of these layers into context and make better decisions about overall risk.
This aggregated data – displayed in an easy-to-read geospatial format – can help to illuminate stranded assets, transitions risks, and other long-term risks due to drought, increased regulations, and charging market pressures. This can help lenders reduce risks related to existing borrower relationships and close less risky loans.
As the World Bank notes, a lack of technical ability to “build climate-smart agriculture portfolios” has led investors to direct climate-related funding into other industries. By using data aggregation and geospatial tools to assess their portfolios, ag banks and lenders can get ahead of the curve and work with other stakeholders to de-risk their businesses and build resilience.
The Bottom Line
Banks and other ag finance institutions have long used data to inform their loan and investment decisions and identify the financial risk of borrowers. But financial data is only part of the picture, and today’s agricultural economy calls for more robust data collection across a wide range of factors, including social and environmental risk.
Data aggregation is the process by which ag banks and lenders can un-silo datasets and represent them in a dynamic, geospatial format. Not only can GIS platforms save banks time and money, but they can also improve operational efficiency and strategic decision-making, bringing ag finance into the 21st century.
GIS Connect is designed specifically for agricultural finance, allowing users to aggregate data, securely apply context to their own proprietary data, and represent it in a map-based format.
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