Data Will Create "More and More Value" But Risks Damaging Trust, Says World Bank Report

The World Bank’s World Development Report is among the most authoritative sources on the development of low- and middle-income countries, and this year’s edition is focused on the impact of data. It finds that big data has the potential to benefit humanity and reduce global inequality, but if it is not used ethically it could severely undermine trust. The “Data for Better Lives” report also makes recommendations for how companies can use alternative data to benefit both themselves and society but notes that data introduces risk management  concerns for firms.

Data’s ‘untapped’ potential for global development

This report looks at global development, inequality and poverty so it might seem surprising that its focus is on data. The report’s analysis shows that data-driven services have grown exponentially since 1990, now constitutes half of global trade in services. Data as a service trade now exceeds transport services and travel services combined.

The report’s key message is that “the value of data for development is untapped” and it can be used to “promote economic and social development.”David R. Malpass, President of the World Bank Group, launched the report at a virtual event, saying that data offers "a resource that can be used and reused repeatedly to create more and more value”.

Alternative data ‘transforming’ companies

The report says that companies around the world have already felt transformative effects of big data. It cites a study of 179 large firms in the US which indicated that those who use data to make decisions increased productivity by 5-6% above what would otherwise be expected.

It also found that alternative data is giving financial services companies a competitive edge. Data on media and social media, mobile phone data and company data help banks to “assess creditworthiness’’ and it provides “detailed information on individuals, businesses, economic outcomes, and phenomena,”according to the report.

But for this data to be most useful, it should be presented in a format that it can be used by big data technologies such as Artificial Intelligence. “By consuming more data types and extracting relevant information from seemingly unrelated patterns, machine learning could generate credit scores for more individuals with greater precision,” the authors wrote.

The risks of unethical use of data

The report also issued a stark warning: If companies use data to develop products that damage society, hold personal data in an unethical manner or fail to  ensure data transparency and confidentiality, they could suffer regulatory, reputational, financial and strategic risks. In addition to potentially losing the trust of the consumers, investors and prospective employees, companies could also find themselves the target of a regulatory investigation.

What should companies do to mitigate these risks? Here are three important steps:

1. Ensure you use data in a trusted way. The report recommends that “trust in data transactions can be supported through a robust legal and regulatory framework”. This applies not just to laws but to companies’ internal policies and procedures, which should follow best practices around data governance. Companies should also ensure they acquire data from reputable sources.
2. Use data to vet prospective clients and suppliers. Due diligence processes should use comprehensive news, company and legal data to ensure that the company does not do business with unethical third parties. Technology can help this process by enabling intelligent automation for high-volume third-party screening against a wide range of sources, allowing risk management professionals to focus on analyzing findings, assessing risk and conducting enhanced due diligence when further checks are necessary.
3. Test ESG claims with data. Investors are increasingly looking to move their holdings to companies with a strong Environmental, Social and Governance (ESG) record. Many companies claim to have a strong ESG policy, but this can only be determined by looking at a range of data sources including adverse media feeds.

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