data, coding

Data analytics throughout COVID-19

Bingqian Gao, Data Science Lead at TrueCue

data, codingThe impact of COVID-19 has seen governments and organisations turn to data for their decision-making like never before, reflecting not only the increased importance of data, but also the speed at which senior management have come to recognise the insights which can be gained from harnessing it.

In only seven months, data literacy, collection and analytics has seen significant advancements, leveraged through increased quantitative information in the mainstream media informing and directing the public. Data analytics has also progressed in business contexts, helping employers to gain greater insights into their organisational needs, enabling them to target areas of struggle amongst the workforce and plan as much as possible for what remains a clouded future.

Because of this, now is the perfect time for reflection to better understand the role data analytics has played during the crisis and how adopting a data-driven approach can lead us into the next phases of recovery.

  1. Context is key

In order to leverage the true potential of available data, organisations must first make time to understand the context of the data that has been collected, whether it is system collected, reported or survey based. Raw data forms the foundation of any subsequent analysis and the quality of the data anchors impact the trustworthiness of the conclusions drawn. To put it simply, data that is collected from an unreliable source will have a negative impact on the overall analysis.

A significant example of an unreliable data source is the recent paper retraction scandal with The Lancet, which claimed and then retracted that using Hydroxychloroquine on COVID patients increased heartbeat irregularities and death rates. This had a devastating impact on the research and treatment landscape for COVID-19 and forces us to consider how many flawed analyses are out there.

To avoid error, once data collection has been grasped – bearing in mind there will always be some level of ambiguity – we must consider how to clean and process the data before analysis, and whether this places any implication to the interpretation and conclusions drawn. Ultimately, data-driven actions should be based on analyses that are scientifically rigorous and robust.

  1. Collaboration and speed to insight

Collaboration and speed to insight are critical when preparing for the unknown. As many have noted, the pace of change in the current climate is relentless, with organisations being compelled to make data-driven decisions within hours or even minutes. To minimise the risk of missed opportunities, swift action must be taken before the value of data diminishes.

At present, economists are taking high frequency or real-time data such as job postings and weekly unemployment claims as guidance. This must continue going forward, with organisations, healthcare services and government bodies sharing suitable data sources and making informed decisions based on insight from data they have now.

  1. Machine learning or scenario modelling?

Throughout the past seven months, many have wanted to utilise the power of machine learning to elevate their organisation from basic level descriptive and diagnostic analytics, to complex predictive or even prescriptive analytics. This not only enables employers to take immediate action, but also helps them to ensure preparations can be made for the near and mid-term future.

However, unprecedented events like COVID-19 have meant many organisations have been forced to take a step back and re-evaluate their approach. The idea of machine learning is to let the machine learn from existing or historical data. The validity of the approach is challenged when there is not enough historical data or past patterns to learn from.

To overcome this issue going forward, there are two viable solutions. The first is to stick with machine learning and use other historical events as a proxy. The second is to consider alternative techniques such as scenario modelling, which is a process of examining and evaluating possible future outcomes. Either way, finding new ways to conquer data and model the unknown will be critical in the coming months. 

  1. Data visualisation does not equate to interpretation

Data analysis and visualisation skills have been largely democratised, with desktop software such as Tableau widely accessible. Yet, in spite of the explosive amount of quantitative information in the mainstream media, data literacy is not just about creating charts and graphs.

Analysing data – apart from cleaning, transformation and visualisation – means interpreting the results and using this to accurately inform operational decisions. The key takeaway here is that visualisation does not equate to interpretation and storytelling, which often requires industry knowledge.

  1. Data drives actions

And finally, analyses should always result in data-driven insights that inform business actions. After data is collected, cleaned, analysed and presented in an accessible manner, it is important that analytics teams are thinking about what comes next and how businesses should act on the insights and better prepare for the future.

The use of data during this time has been critical to our survival, helping organisations, healthcare services and government bodies to mitigate the challenges brought about by COVID-19. If this progression continues – with innovation, speed and collaboration playing a lead role – there is every chance we will continue on this road of recovery and survive or even thrive in the challenging times ahead.


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