28th January 2021, Posted in AI Business.
When news that an infectious disease caused by a newly discovered coronavirus first began to circulate, it was clear that data and analytic techniques from the world of epidemiology was going to be a vital tool in the fight against the pandemic.
However, I hadn’t anticipated the role the pandemic would play in elevating the data literacy of the population at large, further elevating “data science” into the public lexicon.
Understanding the pandemic
As the urgency of the COVID-19 crisis took hold, many of us tuned in to the UK Government’s daily briefings to understand the spread of the virus through data and statistics. Across the media and in the daily briefings, “descriptive analytic” approaches have been used to present points and trends, including daily infection rates, areas of high risk and factors accelerating infection.
As such, the public has been regularly exposed to the world of data visualization (charts showing COVID-19 numbers), summary statistics (such as an R number), predictions (forward-looking projections) and simulations (understanding the projected impact of approaches to “flatten the curve”).
We’ve also seen the need for accurate and timely data – the more we know, the better and faster both government and public can act to drive down infections and the overall risks. We’ve even seen open discussion around data issues, and how this has impacted our ability to see the true picture of the spread of the virus.
I feel this has raised the bar in terms of data literacy, or at least expectations around the richness of information that could be presented to underpin a topic – the public has viewed the pandemic through the lens of data and statistics, gaining familiarity with the use of common analytic “tools” (charts, statistics, predictions) to better understand what is a complex topic.
Indeed, anyone interested enough in key trends such as local and national infection rates would find it relatively straightforward to follow various data sources updated and analyzed on a daily basis. The daily data and analysis provided by the Office for National Statistics (ONS), for example, is shared, recycled and augmented by professional and amateur data scientists alike.
Perhaps more importantly, the public has been frequently exposed to the links between “data”, “analysis” and “action”.
This clear connection between “what the data says” and “what action we’ll take” may seem obvious to data professionals, but I can’t remember another example when “data-led decision making” was so openly discussed. In fact, this concept underpins every aspect of our pandemic response, driving informed decisioning around measures put in place – the phrase “we will follow the science” could just as easily be interpreted as “we will follow the data”.
Indeed, the vital role of data science and analytics was highlighted again most recently with the identification of a new, more infectious variant of COVID-19 in the south east of England, which prompted the government to announce a new national lockdown to try and combat the surge in cases.
As highlighted by the World Economic Forum (WEF) in July, the pandemic created an urgent need for rapid decision making, informed and supported by constantly changing data sets, backed by effective visualization. It points towards the importance of “agile data science methods that address the speed, urgency, and uncertainty of decision making” as one of the key learnings about the impact of COVID-19 on data science and other applicable disciplines such as artificial intelligence (AI). Around the world, this has been key to influencing behavior and helping to build and maintain public support for tough restrictive measures.
The role of data science
From providing the insight that has enabled governments and healthcare systems to act quickly, to keeping the public informed and improving the efficacy of protection measures, the profile and appreciation of the world of data and analytics has skyrocketed.
Over the last year, the role of data science (or at least the concept of data-led decision making) has shaped how we live and work, whilst also facilitating significant drug discoveries in the global pharmaceutical sector’s quest to develop a COVID-19 vaccine. This has highlighted the breadth of challenges to which a “data” mindset can be applied – helping us tackle challenges in society as well as being a vital business tool.
As we look ahead, I see an ongoing need for businesses to embed data science to become more intelligent, efficient and relevant in an increasingly-competitive global market. For many businesses, it feels like 2021 will be a decisive year in the quest to create digital, data-driven organizations.
Beyond that, I hope the increasing understanding in the public of the potential for data science can support initiatives to create a healthier, greener, kinder world.
Rich Pugh is Chief Data Scientist at Mango Solutions – an Ascent company