The internet, data science, and digital technologies have both dark and bright sides. The internet was originally created to be a data commons where digital material could be accessed freely, shared, and integrated, but today it can be a magnet for scammers, fraudsters, fake news, and trolls. It is also bursting with high-quality, reliable data that can be used to create innovative products and services, make more sustainable decisions, and address shared problems (see the sidebar “Open Data Use Cases”).

Finance professionals should be aware of the side of the digital world that is based on data sharing, open-source cooperation, and collaborative possibilities, particularly to manage risk.

According to the Open Knowledge Foundation, “Open data is data that can be freely used, re-used and redistributed by anyone — subject only, at most, to the requirement to attribute and share alike.” And open data is intended to be interoperable, facilitating collaboration and cooperation (see the sidebar “Open Data Sources”).

Open data is here to use

Open, interoperable data, which means it can be exchanged and made use of, is not a utopian vision; open datasets are there waiting to be used. Even the most tech-hesitant will have already used some open, sharable, interoperable data, although in many cases they probably paid someone to provide them with freely available data. I have seen far too many expensive consultancy reports that contain straight lifts from freely available government or World Bank datasets — properly referenced, of course.

Open data forms the basis of many apps, websites, and information systems. These systems combine different open datasets to transform online experiences. Take, for example, the widely used global platform Tripadvisor. It seamlessly combines reviews and images provided by millions of consumers and marketing material from individual businesses, with open-source data such as location data from tourism boards, satellite images, mapping data, street view data, location services, and weather forecasts.

The property sector is another example of the innovative use of open data. In the analogue days, all the data you had when buying or leasing a property was an advert in a newspaper and two typewritten pages handed out when you viewed the property or physically went into a real estate agent’s office. If it was a really expensive property, you may have been sent a colour brochure by postal mail. Now, everyone has instant access to online video tours, photo galleries, property price datasets, local crime stats, average income data, details of local connecting roads and public transport, school performance data, and even drone or satellite footage of the property and its environs. You can even find old images of the property relating to the previous sale and do your own comparison of changes since then. And the sophistication and richness of data on these websites just keeps growing.

Using open-source data in your own systems

Imagine if you could emulate the changes observed in the property sector in your financial information systems, based on integrating open-source data. Think about what you could do with your asset register. No longer would it be a couple of columns in an Excel spreadsheet. You could geotag it and place it on a GIS (geographical information system), link it with data on climate risk exposure, risks of sea level rises, crime statistics, satellite images, potential sources of pollution, resource availability, access to clean water, demographic information to help with workforce planning, transportation accessibility data, drone footage, or videos to manage and monitor repairs and maintenance.

This new, enhanced asset register that draws on interoperable open data could greatly enhance strategic decisions on assets and their protection, maintenance, sale, and relocation, as well as help inform rental decisions or renegotiate insurance premiums.

Risk registers

Perhaps the biggest benefit from the integration of open-source data could be to your risk registers.

Often, risks come from changes outside your business, evidence of which was previously considered too expensive to gather and difficult to integrate into financial or business systems. Open-source data that provides reliable, contemporary data on the state of systems that are the source of these risks could make managing these risks a lot easier.

2022 survey of more than 3,000 UK adults, including more than 1,000 senior business decision-makers, suggests the preparedness of business to deal with the challenges of the sustainability agenda is low despite known existential threats to their business models.

Open datasets provide evidence that would simply be far too expensive for an individual business to obtain but have been collected, collated, and curated by organisations responsible for managing these risks. These organisations include planning regulators, universities, governments, producer organisations, international agencies, police, health boards, and so on. To fulfil their responsibilities, these organisations share data that can be used by others to manage risks.

It would not make sense for every business to invest in its own global climate change forecasting model, despite the exposure of all businesses to climate risks such as those leading to business relocation or supply chain disruptions from projected sea level rises, flooding, or wildfires.

Fortunately, there are many applications or datasets that provide easily usable data on the probability of damage for virtually all locations across the globe for different scenarios. When combined with data on the location of a business’s premises, its logistics network, and the location of its suppliers, it is possible to begin to identify how predicable threats could seriously impact the business. That means the business can respond proactively and doesn’t have to wait for a catastrophe to occur and the expense to be incurred to fix it.

Difficult-to-manage risks

Similarly, open data can be used to model for very-difficult-to-manage risks, such as modern slavery or human rights abuse in the supply chain. Slavery, forced labour, and child labour are known to be far more widespread than many business leaders would like to admit, particularly in international supply chains.

No one connected to a business would be happy knowing they have been complicit with exploiting child cobalt miners in Congo or trafficked fishing workers in Thailand. And ignoring these risks to maintain a kind of culpable deniability is problematic in a world of ever-expanding and easily accessible knowledge.

Reasonably reliable data produced by nongovernmental organisations and international agencies exists on the location of forced labour practices by industry and location, which can be used to map out a business’s risks by product, sector, or supplier locations. By mapping out possible intersections between information on raw materials, components, and suppliers, and these sectoral or national datasets, a business could begin to predict the possibility of connections with labour practices that could destroy its reputational capital.

This enhanced knowledge allows a business to focus any investigation or enquiry into the high-risk parts of its supply chain.

Socially conscious investors no longer need to rely on borrowers to produce reports on what happened with the money. Investors could ask borrowers to upload videos on their GIS of what they are doing and monitor index changes of multiple deprivation and health statistics, or evidence from local environmental regulators or community groups — even in remote areas such as the Niger Delta.

Managing other sustainability-related risks like biodiversity could also be enhanced by using open data. You can search for and monitor the performance of all delicate ecosystems connected to your business, or even integrate data on the quality of these ecosystems on your website or as part of your sustainability reporting system. The integration of independent, open-sourced, third-party data can greatly enhance the credibility and legitimacy of your reporting.

Tips to use open-source data to manage risk

Finance professionals are natural collaborators who work better collectively. Harnessing the wisdom of the crowd has been and remains a winning strategy, and digital technology has turbocharged this potential.

Here are five tips to incorporate open-source, nonfinancial data into financial management decisions:

• Integrating open data on possible risks could give you a competitive edge in insurance, procurement, and other contract negotiations. When you identify data gaps in your information systems, search for open datasets rather than carry on with no evidence whatsoever.
• Explore the benefits of combining business information systems with open-source mapping data and software. This can provide new enhanced data visualisations and improve the interpretability of complex datasets.
• Open datasets related to environmental risks often include detailed, reliable forecasts and scenarios that can be readily integrated into strategic planning and budgeting.
• Take advantage of online training material and invest in developing the finance team’s capacity to integrate open-source data.
• Remember to use only reliable datasets from trusted organisations in order not to breach data privacy or security laws.

Integrating open data offers considerable cost reductions, better risk management, and value-creating potential. A few days of training, often provided free by open-data proponents, open up a universe of data and possibilities. And many new graduates already possess data analysis capacity. The data-sharing economy is alive and thriving in the World Wide Web — and waiting to be used.

The added value from using open data comes from better, quicker decision-making, new products and services, and improved risk management, regulatory compliance, and accountability.

Open data can also facilitate innovative joint ventures. UK national mapping agency Ordnance Survey (OS) collaborated with MapServe, a London-based open-source platform for publishing spatial data and interactive mapping applications to the web, to produce a tool to help developers, town planners, architects, and surveyors with planning application processes. This collaboration allows developers to add their project to OS maps to demonstrate how their proposal protects the natural environment and provides people with access to greenspaces and transport links, education, and healthcare.

OS data also played a critical part in the UK’s COVID-19 vaccination roll-out. The integration of road network dataset OS MasterMap Highways Network with data collected from in-vehicle GPS allowed the UK’s National Health Service to minimise the travel time of 50 million adults to 2,000 proposed vaccination sites.

There is a whole ecosystem of open data standards, protocols, and organisations designed to help share open data, if you know where to look for it.

Open data collections and foundations:

• The ODI.
• Open Knowledge Foundation.

International agencies: Social and economic data:

• World Bank Open Data.
• UNICEF Data.
• OECD Open Government Data.
• Data Europa EU.

National government sources:

• Statistics Netherlands.

Geospatial data management and analysis:

• Open Data Cube.

Ian Thomson, ACMA, CGMA, is professor of accounting and sustainability and director of the Lloyds Banking Group Centre for Responsible Business at the University of Birmingham in the UK. To comment on this article or to suggest an idea for another article, contact Oliver Rowe at [email protected].


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