After more than a year since COVID-19 became a global pandemic, we now understand that part of the challenge in fighting it, and any infectious disease, is solving the underlying data problem.
Without reliable data, leaders can’t plan, epidemiologists can’t model, and citizens don’t feel confident following expert recommendations. Bad data has led to both poor behavioral and policy-oriented decisions, exacerbating COVID-19 and prolonging its consequences.
It’s not purely a data problem, but a data engineering problem. The critical data is already being created, it’s just a mess, lives all over the place, and needs to be combined, cleaned, and curated to be useful. Health data from different countries and organizations, for instance, vary in data entry and quality. This data is structured across a variety of disparate systems, often walled off, with no easy way to transfer data between systems. And this is not just a one-time event, but a highly dynamic system of new data and new sources that must be blended in.
We’ve seen how data has been curated and shared at unprecedented speed and scale in the past year and a half. Using data wrangling, researchers and health organizations worldwide were able to meet the data challenge so they could respond to COVID-19 outbreaks and develop better prevention strategies and advance scientific discoveries. The steps forward we took in the global fight against COVID-19 have pushed the world to understand the value of a shared collection of data.
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Source: Hitconsultant
