Governments around the world have been guided by data science modelling during the pandemic. Used to justify everything from the tiered systems to the new roadmap out of the pandemic, analytical calculations have affected our lives more clearly over the past year than any time previously. And it’s not just governments that needed to get the calculations right: shops have been managing demand, and banks figuring out how to approve loans even while their computer models face such unprecedented economic parameters. Analytics have certainly been pushed to the limit during COVID-19.
As we emerge into a more data-conscious future, analytical skills will remain in high demand. Yet, algorithmic decisions have caused controversy and criticism and there are sobering lessons to be learned from mistakes made during the pandemic. Moving forward, our societies will benefit not only from more algorithmic decisions but better-quality ones which are based upon sound ethical principles. Guaranteeing the fairness and success of tomorrow’s model-based decision-making begins with educating those building, monitoring and deciphering models today.
The best analytics education starts with insight into what came before
Simply creating more data scientists to meet demand won’t necessarily improve the quality of model-based decision-making. Universities and employers alike need to rethink which methods are most effective for teaching students the skills they need to survive in the ‘real world’, where data is not always – or even mostly – clean and complete.
“Simply creating more data scientists to meet demand won’t necessarily improve the quality of model-based decision-making”
It’s generally agreed by higher education institutions that case studies are one of the most effective tools for teaching analytics. These are analytical challenges based on real-world data. Case studies have long been used in business schools for more general learning and have a very good reputation. Case study ‘clearing houses’ have been set up to assure the quality of these teaching resources, and there is considerable competition to get cases accepted for publication. The best cases are studied around the world by tens of thousands of business students every year.
Businesses see case studies as ways to enhance their reputation. They often commission case studies about their organisations, using them to highlight successes or to raise brand awareness with an influential demographic, particularly future employees.
However, until recently, the number of quantitative case studies developed for academic use has been limited by data privacy and data protection issues. It’s generally hard to get hold of real-world data that’s both sufficiently useful for students and which doesn’t disclose confidential or business-sensitive information. These issues can’t be minimised or diluted, but it needs to be recognised that data science students must have case studies if they are to learn the skills they need.
A national analytical library could open up data science best practice to all
To address the shortfall in quantitative case studies, we need to actively seek out new analytical cases with accompanying data. Compiling these together can create a new academic case library for use by higher education institutions across the nation.
The SAS Case Library provides a model for how this national analytical library could work. Experts continuously commission and curate new cases for the library. They can therefore ensure the library reflects a range of common analytical techniques applied across different industries. The cases are also tagged to show their suitability for different levels of academic attainment. Where new content is developed for existing cases, it’s only added to the library after careful review.
The SAS analytical case studies are written by academics to ensure they are valuable for an academic audience. They follow a globally recognised format for academic case studies, and are grouped by industry and analytical topic, making it easier to navigate. All the studies are data-rich and based on real-world problems. Step-by-step demos, analytical games, and ideas for datathons and hackathons are available to encourage students. Access to free software and training materials is also provided.
Decisions that affect real lives must be based on lived experience
Existing case studies should be the bedrock of teaching data science and algorithm creation. Only from seeing the interaction of data-driven decisions with business goals, customer expectations and citizen experiences can data scientists understand how their decisions impact real lives. As organisations and their employees become increasingly data-literate, case studies pitched to a variety of levels will become key to democratising data and ensuring that those working alongside algorithms are ready to make the most positive impact possible.
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