On December 31, 2019, BlueDot, an AI-driven health monitoring platform based in Canada, had alerted its customers about a cluster of unusual pneumonia cases happening around the Huanan Seafood Market in Wuhan, China. This was nine days before the World Health Organization notified the world about a novel coronavirus which came to be known as COVID-19. 

The power of alt-data: How alternative data can improve operational intelligence

How did an AI-based epidemiologist beat not only the WHO but also the US Centers for Disease Control and Prevention (CDC) – who rolled out the notification six days after BlueDot – to the punch? The answer lies in data. More specifically, in external, alternative data.

As opposed to the WHO and the CDC, which rely on official sources such as government and public health officials, BlueDot derives much of its predictive ability from data it collects from alternative sources. These datasets include, for example, the information of over four billion passengers on commercial flights travelling every year; climate data from satellites; the population data of humans, animals, and insects; and local information from journalists and healthcare workers gleaned daily from 100,000 online articles across 65 languages.

This is the power of alternative data – and enterprises must ready themselves to tap into it.

The bottom line: Why alternative data is integral to your business

The year 2020 saw the rise of some interesting data-related trends that will continue to reign supreme in the post-pandemic world. The increased use of enterprise cloud services, and the subsequent data explosion, is one of them. In essence, modern businesses now have, at their disposal, a data-superlake that contains both conventional and alternative data in abundance. As enterprise databases, applications, processes, and workflows continue to migrate to SaaS-based platforms, the quantum of data available will only increase. 

This data inundation can either empower or overwhelm enterprises. However, if the derivative data – which comes from combinations, associations, and syntheses with different traditional and alternative datasets – is utilised appropriately. In that case, it can provide businesses with a clearer lens to view an uncertain market. Decision-making across all levels, from C-suite executives to the junior-most employee, can become more accurate and timely. A richer, deeper data strategy will also empower business leaders to identify and mitigate risks while driving superior experiences for all stakeholders, be it consumers, employees, investors, etc.

Investors and financing companies have long known that traditional datasets can only do so much when it comes to providing a holistic picture of the financials involved. They use non-conventional datasets to fill the gaps in legacy sources, enriching them through unique, timely, and granular insights that may not be available through traditional routes. For instance, some of the traditional data points that lenders use to determine a borrower’s creditworthiness include their bank history, liquid capital, CIBIL score, etc. However, solely relying on these data may project an inaccurate image of the borrower’s creditworthiness. 

Suppose this borrower has a debt of INR 60,000 and can only afford to pay half the amount from their salary. However, to maintain their credit score, the person borrows the remaining amount from friends and family. If the lender uses non-conventional datasets such as the borrower’s bill payment behaviour, social media behaviour, airtime usage, etc., it can get a more accurate picture of their repayment capabilities and risk profile. 

Data-driven, data ready: The future of enterprise operations

Gartner estimates that public cloud services will be essential for 90% of data and analytics innovation by 2022. Technologies that can extract, collate, clean, and analyse data from multiple sources will become more prevalent, driving massive growth in the volume of alternative datasets and making them as essential to holistic data stories as traditional datasets.

The financial ecosystem is already using this alternative data to drive better risk assessment. BlueDot used it to warn the world about the imminent pandemic. It is high time that businesses across other sectors begin to augment their analytics strategy using alternative data. In the post-pandemic global economy, where data analytics is finding greater adoption as a reliable tool to navigate choppy waters, the need to capture and synthesise alternative data is no longer an option – it has become a critical business imperative.