Metaverse, Data-as-a-Service, Alternative Data, Data Monetization, Data Operations, Data Quality

This week, we begin with an article showing the vast adoption of Metaverse in Southeast Asia and the need by the authorities to solve present gaps in the safety of its residents’ personal data. Next, is a story about the emergence of Data-as-a-Service (DaaS) leading to improvements in credit decisions. Then, we have a piece focusing on the potential of Alternative Data studies to explain market anomalies better than the fundamental analysis or traditional finance theories. Following that, we have an essay describing the initiation of organizations realizing data as an asset & how they are monetizing their data. Next, is an analysis of the thriving data operations in every business today & some crucial ways to achieve effective data ops. Lastly, we have a survey revealing that data quality concerns are widespread and have a negative impact on organization outcomes.

The Metaverse And The Importance Of Personal Data Protection In Southeast Asia

Like many countries across the globe, Southeast Asian countries have been embracing the idea of creating alternate realities in the ‘metaverse’. The concept, envisaged as a seamless network of artificial virtual worlds, had been the stuff of science-fiction but has been gaining mainstream popularity since Mark Zuckerberg announced in October 2021 his decision to rebrand Facebook to Meta and bring the concept to reality. Knowledge isn’t just power, it’s also opportunity. 

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Data-as-a-Service, A New Frontier For Credit Decisioning

It is not a secret that many financial institutions struggle to transition to more advanced credit models. Technologically, many institutions have outdated system capabilities, with limited access to data sources, paired with outdated analytical engines. This has resulted in poorer credit decisioning, as the strong reliance on traditional data, limited access to alternative data, inflexible models, and subjective assessment by fallible managers and underwriters, have impeded growth, even before regulatory reviews come into the picture.

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Understanding Market Anomalies Through Alternative Data

Thomas Kuhn once wrote that scientific revolutions happen when enough anomalies appear. They are unsolvable and do not fit into current theoretical and empirical frameworks. Enough of these anomalies can cause a paradigm shift, necessitating a re-imagining of scientific theory. “Traditional” finance theory partly rests upon the Efficient Market Hypothesis (EMH), i.e., markets and prices reflect all publicly available information at all times. There are some other assumptions, such as the idea that all investors are rational actors, but we will be focusing primarily on EMH.

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Three Paths To External Data Monetization

A sea change is occurring on the data monetization front, as companies begin realizing data is as an asset that can generate profits rather than just a liability that brings risk. Data clearly has value from an internal data science perspective. But other opportunities for data monetization exist outside your company that you should be aware of. “The way that we’re looking at data now is shifting,” says Traci Gusher, the principal of E&Y’s data and analytics business in the Americas. “For a long time, data was viewed as, for lack of a better term, a burden. 

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3 Must-Haves For Effective Data Operations

Data can be a company’s most valued asset — it can even be more valuable than the company itself. But if the data is inaccurate or constantly delayed because of delivery problems, a business cannot properly utilize it to make well-informed decisions. Having a solid understanding of a company’s data assets isn’t easy. Environments are changing and becoming increasingly complex. Tracking the origin of a dataset, analyzing its dependencies and keeping documentation up to date are all resource-intensive responsibilities.

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Data Quality Study Reveals Business Impacts Of Bad Data

If your data warehouse is starting to look like Miss Havisham’s decaying mansion, you may have a data quality problem. A new survey of 500 data professionals from open source data quality tool Great Expectations revealed that 77% have data quality issues, and 91% report they are impacting their company’s performance. Only 11% did not report having problems related to data quality. “Poor data quality and pipeline debt create organizational friction between stakeholders, with consequences like degraded confidence,” said Abe Gong, CEO and co-founder of Superconductive, the company that makes Great Expectations. 

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